{"conference":"AI Engineer World's Fair 2026","dates":"June 29 - July 2, 2026","location":"San Francisco, CA","website":"https://ai.engineer/worldsfair","scheduleVersion":1088,"totalSpeakers":194,"speakers":[{"name":"Aditya Gautam","role":"Machine Learning Technical Lead","company":"Meta","sessions":[{"title":"Modality Misalignment and Originality Attribution in Short-Form Video: A Multi-Agent Approach at Platform Scale","day":"Day 2 — Session Day 1","time":"11:10am-11:30am","room":"Track 7","type":"session","track":"LLM Recsys","status":"tentative","speakers":["Aditya Gautam"]}]},{"name":"Ahmed Ahres","company":"Reactor.inc","sessions":[{"title":"The Next Medium: Why Real-Time Interactive Video Changes Everything for Developers","day":"Day 4 — Session Day 3","time":"2:50pm-3:10pm","room":"Track 1","type":"session","track":"Generative Media","status":"tentative","speakers":["Ahmed Ahres"]}]},{"name":"Ajay Prakash","company":"Linkedin","sessions":[{"title":"500 Skills, Zero Fine-Tuning: LinkedIn's Playbook for AI Agents That Actually Know Your Codebase","day":"Day 3 — Session Day 2","time":"2:25pm-2:45pm","room":"Track 8","type":"session","track":"Context Engineering","status":"tentative","speakers":["Ajay Prakash"]}]},{"name":"Akele Reed","company":"Sondermind","sessions":[{"title":"Evals Driven-Development: Engineering a Mental Health AI Coach Ethically & Safely","day":"Day 3 — Session Day 2","time":"11:40am-12:00pm","room":"Track 5","type":"sponsor","track":"Evals","status":"tentative","speakers":["Akele Reed"]}]},{"name":"Alex Hancock","role":"AI Agent","company":"Block","twitter":"https://x.com/alexjhancock","sessions":[{"title":"The Universal Remote Control for AI","day":"Day 3 — Session Day 2","time":"2:50pm-3:10pm","room":"Track 8","type":"session","track":"Context Engineering","status":"tentative","speakers":["Alex Hancock"]}]},{"name":"Alex Volkov","company":"Coreweave / WandB","sessions":[{"title":"The Z/L Continuum: Should AI Engineers Still Read Code?","day":"Day 3 — Session Day 2","time":"11:10am-11:30am","room":"Leadership 2","type":"session","track":"AI Architects: Tokenmaxxing","status":"tentative","speakers":["Alex Volkov"]}]},{"name":"Aliisa Rosenthal","role":"General Partner","company":"Acrew Capital","sessions":[{"title":"Reverse-Engineering the AI Buyer","day":"Day 4 — Session Day 3","time":"10:45am-11:05am","room":"Track 6","type":"session","track":"AI in GTM","status":"confirmed","speakers":["Aliisa Rosenthal"]}]},{"name":"Aman Raj","company":"Decawork","sessions":[{"title":"IT Admin for the AI Workforce: Why Your AI Agents Will Need Their Own IT Department","day":"Day 2 — Session Day 1","time":"1:55pm-2:15pm","room":"Leadership 2","type":"session","track":"AI Architects: Show my Workflow","status":"tentative","speakers":["Aman Raj"]}]},{"name":"Amazon AGI","sessions":[{"title":"Amazon AGI","day":"Day 3 — Session Day 2","time":"9:50am-10:10am","room":"Main Stage","type":"keynote","track":"Autoresearch","status":"hold","speakers":["Amazon AGI"]}]},{"name":"Amit Desai","role":"Director of Voice AI","company":"Roku","sessions":[{"title":"The Goldilocks problem: when your Robot asks too much — or acts too soon.","description":"Embodied agents are crossing from answering questions to taking physical actions — moving a box, turning a wheel — and people will command them by voice, because voice is the fastest, most natural interface we have. But voice is also the most error-prone, and when a misheard command drives a physical action, the failure isn't a wrong answer; it's human harm, damage, or an expensive, irreversible mistake. The field has never needed a serious way to handle voice-command errors, because informational agents made them cheap. Embodiment ends that. This talk replaces the usual hand-waving — \"don't ask too much, don't get it wrong too much\" — with a single number you can optimize. The core idea: both confirming and erring cost the user. A confirmation is friction — attention, time, a delayed action; a wrong action is a mistake cost, often higher given physical harm or expense. Put them on one ledger and you can measure a voice interface as average user cost per command, and make minimizing it the system's objective. From that falls a non-obvious rule — you confirm or not based on both cost and uncertainty: an expected value. I'll frame confirmation as just one option alongside acting, disambiguation (choices), and deferring; reason at the level of goals rather than low-level motion; walk the architecture (task hypotheses → user-cost model → confirmation policy); and show eval results from a simulated environment measuring regret against oracle behavior. I'll close with what worked applying this to voice in smart TVs, speakers, and navigation — and a challenge to bring this metric to robots, cars, and wearables before the errors do.","day":"Day 2 — Session Day 1","time":"3:45pm-4:05pm","room":"Track 6","type":"session","track":"Voice & Realtime AI","status":"tentative","speakers":["Amit Desai"]}]},{"name":"Amit Navindgi","company":"Zoox","sessions":[{"title":"From Self-Driving Monorepo to Self-Driving Cars","day":"Day 3 — Session Day 2","time":"3:20pm-3:40pm","room":"Track 2","type":"sponsor","track":"Robotics & World Models","status":"tentative","speakers":["Amit Navindgi"]}]},{"name":"Anant Arora","role":"Senior Director of Product Management and Technology - Digital","company":"Lowe's","sessions":[{"title":"Autonomous Finance in Retail","day":"Day 4 — Session Day 3","time":"2:50pm-3:10pm","room":"Track 3","type":"session","track":"AI in Finance","status":"tentative","speakers":["Anant Arora"]}]},{"name":"Andon Labs","sessions":[{"title":"Vending-Bench: Long-Horizon Agent Evals for a Simulated Vending Business","description":"Long-horizon agent evals via a simulated vending machine business, testing negotiation, pricing, and supplier management over 365 days.","day":"Day 3 — Session Day 2","time":"10:45am-11:05am","room":"Track 5","type":"sponsor","track":"Evals","status":"tentative","speakers":["Andon Labs"]}]},{"name":"Andrew Dai","company":"Elorian AI","sessions":[{"title":"The Best Models Still Reason Like Toddlers","day":"Day 2 — Session Day 1","time":"11:10am-11:30am","room":"Track 2","type":"sponsor","track":"Vision & OCR","status":"tentative","speakers":["Andrew Dai"]}]},{"name":"Andrew Orobator","company":"Reddit","sessions":[{"title":"Spin at the Gate Until Green: The Engineering Primitives Behind Self-Driving Codebases","day":"Day 2 — Session Day 1","time":"1:30pm-1:50pm","room":"Main Stage","type":"session","track":"Software Factories","status":"tentative","speakers":["Andrew Orobator"]}]},{"name":"Andrew Qu","company":"Vercel","sessions":[{"title":"We Solved Agent Building - The Evolution of Building A Successful Data Science Agent","day":"Day 4 — Session Day 3","time":"3:20pm-3:40pm","room":"Main Stage","type":"session","track":"Harness Engineering","status":"tentative","speakers":["Andrew Qu"]}]},{"name":"Ang Li","role":"CEO and Co-Founder","company":"Simular","twitter":"https://x.com/angli_ai","sessions":[{"title":"Reliable Computer Use Agents require coding","description":"Even the world's best computer-use agents cannot repeat their successes at the moment. Agents that write code — emitting structured selector-based actions instead of clicking pixels — break through that ceiling. We'll share two years of experience from Simular's production agent platform, the architectural decisions that mattered (refs over pixels, code as substrate, Simulang DSL), and a live demo: a 30-step unattended Windows workflow, side-by-side with a vision-only baseline. If you're shipping agents to real users, this is the playbook.","day":"Day 3 — Session Day 2","time":"3:45pm-4:05pm","room":"Track 7","type":"session","track":"Computer Use","status":"tentative","speakers":["Ang Li"]}]},{"name":"Angela Jiang","role":"Head of Product, Claude Platform","company":"Anthropic","twitter":"https://x.com/angjiang","sessions":[{"title":"Katelyn Lesse & Angela Jiang (Anthropic)","day":"Day 2 — Session Day 1","time":"9:30am-9:50am","room":"Main Stage","type":"keynote","track":"Software Factories","status":"confirmed","speakers":["Katelyn Lesse","Angela Jiang"]}]},{"name":"Anil Nadiminti","role":"Senior Solutions Architect","company":"Amazon Web Services","sessions":[{"title":"When AI Agents Pay and Sellers Monetize: Building x402 Apps for Agentic Commerce on AWS","description":"As Agentic AI moves from chat to execution, autonomous agents need a native way to discover, access, and pay for digital services in real time. This session explores how x402 can turn HTTP into a payment-aware interface for machine-to-machine commerce, unlocking crypto-native patterns like programmable access, pay-per-use APIs, and on-demand monetization for data, tools, and services. We’ll show how to build x402-enabled applications and walk through the architecture, the full agentic payments flow, seller monetization strategies, payment verification, and design tradeoffs involved in making agent-driven transactions secure, scalable, and production-ready. Attendees will leave with practical patterns for building apps where AI agents do not just call APIs — they can discover services, evaluate costs, transact autonomously, and enable new revenue models for sellers.","day":"Day 4 — Session Day 3","time":"11:40am-12:00pm","room":"Track 2","type":"sponsor","track":"Agentic Commerce","status":"tentative","speakers":["Anil Nadiminti"]}]},{"name":"Ankit Jain","company":"Aviator","sessions":[{"title":"How to Kill the Code Review","day":"Day 3 — Session Day 2","time":"11:40am-12:00pm","room":"Leadership 2","type":"session","track":"AI Architects: Tokenmaxxing","status":"tentative","speakers":["Ankit Jain"]}]},{"name":"Anuj Iravane","role":"AI Lead","company":"Anterior","sessions":[{"title":"Don't be data poor","description":"What do you do when the data you most need to train and evaluate on is the data you're least allowed to keep? It's a bind for anyone building AI in a high-stakes vertical: the cases that would teach your model the most — the rare, the messy, the sensitive — tend to be the ones wrapped in the tightest constraints. In healthcare it's near-absolute. PHI can't be retained, reused, or transformed, so your long-lived datasets can't contain real patient data at all. Synthetic data is the obvious escape hatch, but it has its own trap: synthetic records tend to look synthetic, and a model that passes on fake-looking data tells you nothing about the real thing. So the bar isn't generating data — it's generating data faithful enough to trust. This talk is how we got there. Ask an LLM for a full case in one shot and you get something generic and averaged-out — models are worse at inventing convincing, specific detail than you'd expect. We present our synthetic generation pipeline (and the process around it) that enabled us to create golden datasets at scale. The pipeline features a coarse-to-fine process that enriches a patients medical history layer by layer, with a human in the loop hooks to steer the narrative at each step. You'll leave with ideas on how to build your own synthetic data generation capabilities and how to build a data pipeline your domain experts actually enjoy owning.","day":"Day 4 — Session Day 3","time":"3:20pm-3:40pm","room":"Track 7","type":"session","track":"AI in Healthcare","status":"tentative","speakers":["Anuj Iravane"]}]},{"name":"Ara Khan","role":"Founder","company":"Cline","sessions":[{"title":"Making agents you could never make","day":"Day 4 — Session Day 3","time":"11:10am-11:30am","room":"Track 4","type":"session","track":"Agentic Engineering","status":"tentative","speakers":["Ara Khan"]}]},{"name":"Arek Borucki","company":"Huggingface","sessions":[{"title":"Serving 2 Million Models Without Melting: Scaling the Hugging Face Hub","day":"Day 2 — Session Day 1","time":"12:05pm-12:25pm","room":"Leadership 2","type":"session","track":"AI Architects: Show my Workflow","status":"tentative","speakers":["Arek Borucki"]}]},{"name":"Armen Aghajanyan","role":"Co-Founder & CEO","company":"Perceptron AI","sessions":[{"title":"Perceptron Mk1 — Perceptron Inc","day":"Day 2 — Session Day 1","time":"3:45pm-4:05pm","room":"Track 2","type":"sponsor","track":"Vision & OCR","status":"tentative","speakers":["Armen Aghajanyan"]}]},{"name":"Arturo Nereu","role":"AI & Game Development","company":"MongoDB","twitter":"https://x.com/arturonereu","sessions":[{"title":"The Next Game Engine Won't Have a Manual","description":"Game development needs to change for the agent era rather than simply dropping an LLM into existing engines. This talk shows the AI systems behind Veselka, using Claude plus Three.js to turn AI into a practical game-development partner and lower the barrier for people who want to build their dream game.","day":"Day 4 — Session Day 3","time":"3:45pm-4:05pm","room":"Track 1","type":"session","track":"Generative Media","status":"tentative","speakers":["Arturo Nereu"]}]},{"name":"Asaf Gardin","role":"Inference Engineer","company":"AI21 Labs","sessions":[{"title":"Two Bugs That Hid in Plain Sight: A vLLM Debugging Detective Story","day":"Day 4 — Session Day 3","time":"2:50pm-3:10pm","room":"Track 9","type":"session","track":"Inference","status":"tentative","speakers":["Asaf Gardin"]}]},{"name":"Ashok Chandrasekar","sessions":[{"title":"Are LLM Performance Benchmarks Reliable?","description":"Standardizing performance benchmarks for production-grade Large Language Models is currently a significant challenge across the industry. Conflicting data is prevalent, whether originating from server developers like vLLM and SGLang or from various analysts and competitive benchmarks, and these results often fail to hold up under real-world conditions. Our research into these inconsistencies identified several critical factors, including the constraints of single-process tools, specifically the Python Global Interpreter Lock (GIL) and the nuances of model-level settings like temperature. Furthermore, a lack of transparency regarding load generation parameters such as QPS and concurrency, paired with insufficient observability into the benchmarking clients themselves, contributes to these disparate outcomes. In this talk, we share key lessons learned from our benchmarking efforts, examining the primary pitfalls that distort performance data and offering strategies for mitigation. Additionally, we will introduce Inference Perf, an open-source, multi-process utility we developed to provide reliable stress-testing for production stacks. Our goal is to promote standardized, real-world benchmarking practices that allow the community to move beyond unreliable data. Join us to discover how to accurately measure, optimize, and report LLM performance with certainty.","day":"Day 4 — Session Day 3","time":"11:40am-12:00pm","room":"Track 9","type":"session","track":"Inference","status":"tentative","speakers":["Ashok Chandrasekar"]}]},{"name":"Ayush Bhardwaj","company":"Allos.ai","sessions":[{"title":"Trading Desks to Clinical Trials: Parallels in Applied Vertical AI","day":"Day 4 — Session Day 3","time":"2:25pm-2:45pm","room":"Track 7","type":"session","track":"AI in Healthcare","status":"tentative","speakers":["Ayush Bhardwaj"]}]},{"name":"Barak Lenz","role":"Chief Technology Officer","company":"AI21 Labs","twitter":"https://x.com/BarakLenz","sessions":[{"title":"Mind the Gap: Why Your AI Budget Buys You 40% Less Than It Should","day":"Day 4 — Session Day 3","time":"3:45pm-4:05pm","room":"Leadership 2","type":"session","track":"AI Architects: AI Factories","status":"tentative","speakers":["Barak Lenz"]}]},{"name":"Ben Guo","company":"Zo Computer","sessions":[{"title":"Everyone Gets A Software Company","day":"Day 2 — Session Day 1","time":"3:20pm-3:40pm","room":"Track 1","type":"session","track":"Claws & Personal Agents","status":"tentative","speakers":["Ben Guo"]}]},{"name":"Ben Holmes","company":"Warp.dev","sessions":[{"title":"LLM Knowledge Bases: a practical guide","day":"Day 3 — Session Day 2","time":"3:45pm-4:05pm","room":"Track 3","type":"session","track":"Memory & Continual Learning","status":"tentative","speakers":["Ben Holmes"]}]},{"name":"Benjamin Clavié","role":"Member of Technical Staff","company":"Mixedbread","sessions":[{"title":"If we want them to do Knowledge Work, we need to design Knowledge Agents","description":"It's tempting to assume that just like agents revolutionised coding, they will revolutionize other areas: legal, finance, advertising, and even medicine. All of those have in common that they are fundamentally knowledge work. And thankfully, humans have spent thousands of years searching for the best possible workflows for knowledge work. And yet, we seem to be disregarding all of these learnings, forcing every knowledge task into the shape that worked for coding. Today, we're going to talk about the history of knowledge work and how tools were co-designed to support it to understand how we should be building Knowledge Agents, themselves co-designed with their Knowledge Tools. This is key to avoiding falling into a \"good enough\" local optimum: think about legal clerking, a core part of the legal industry where information gathering and reasoning is performed to support the work of senior lawyers. The practice of clerking follows its own code, rules and best practices, which could not have feasibly emerged from studying software engineering: and similarly, there is no reason to believe knowledge agents could emerge from coding agents.","day":"Day 2 — Session Day 1","time":"12:05pm-12:25pm","room":"Track 3","type":"session","track":"Search & Retrieval","status":"tentative","speakers":["Benjamin Clavié"]}]},{"name":"Bo Li","company":"EliseAi","sessions":[{"title":"Realtime Voice Agents with Frontier Intelligence","description":"A deep dive into an EliseAI voice-agent harness that orchestrates multiple models to achieve realtime latency without sacrificing intelligence. The talk covers speculative transcription, async background tool injection, and TTS prefix caching/infilling to reduce latency while preserving capability.","day":"Day 2 — Session Day 1","time":"12:05pm-12:25pm","room":"Track 6","type":"session","track":"Voice & Realtime AI","status":"tentative","speakers":["Bo Li"]}]},{"name":"Brendan Rappazzo","role":"Machine learning researcher","company":"Morgan Stanley","sessions":[{"title":"ALPHALAB: Autonomous Multi-Agent Research Across Optimization Domains with Frontier LLMs","description":"We built AlphaLab to automate quantitative research at Morgan Stanley’s Machine Learning Research Lab - the experimental grind of architecture search, hyperparameter tuning, and literature review that consumes most of a researcher's time. To show it generalizes, we ran it on three deliberately different domains: CUDA kernel optimization (4.4× mean speedup over torch.compile, 91× peak), LLM pretraining (22% lower validation loss under a 20-minute budget), and traffic forecasting (23–25% RMSE improvement after the system independently found and tuned TFT and iTransformer from the literature). AlphaLab is an agentic harness that takes a dataset and a natural-language objective and runs a full research campaign across three phases: it explores the data and surveys prior work, it constructs and adversarially validates its own evaluation framework, and then it runs experiments at scale on a multi-GPU cluster via a Strategist/Worker loop with a persistent playbook that accumulates domain knowledge across experiments. In Phase 3 - the dispatcher keeps a large cluster fully utilized indefinitely with no human in the loop, and the playbook ends up containing domain-specific methodology that didn't exist anywhere in the prompts at launch. This talk walks through the three phases, what we learned from running campaigns with different models, what we have learned from using this in real systems, and future areas we are exploring.","day":"Day 4 — Session Day 3","time":"10:45am-11:05am","room":"Track 3","type":"session","track":"AI in Finance","status":"tentative","speakers":["Brendan Rappazzo"]},{"title":"Loophole - Adversarial Agents To Stress Test Your Morality","description":"Most natural language specifications have holes their authors didn't notice - and writing more rules tends to create more holes. I built Loophole to try a different approach: point adversarial agents at a spec until it stops breaking. You give the system a set of natural language principles. An AI drafts a formal codified version. Two adversarial agents go to work - one finds cases the code permits but the principles forbid, the other finds cases the code forbids but the principles allow. A judge agent patches the code when it can, but only if the fix doesn't contradict any prior ruling. When a contradiction can't be resolved, it escalates to you. Every decision becomes binding precedent, so the constraint space tightens round after round. I started with moral and legal reasoning as the demo, and on its own that's already interesting - it turns into a kind of game where you discover contradictions in your own beliefs that you didn't know were there. But the pattern generalizes well past that. The same loop works for company policies that need to survive contact with edge cases. For making chatbot system prompts adversarially robust. For stress-testing eval rubrics. And, taking the long view, for something like a smarter legislative process - where proposed laws get checked against the public's stated values before they pass, and the contradictions surface before they hit a courtroom. The talk walks through how the harness works, the design choices that matter (especially why precedent is the load-bearing piece), what kinds of specs it handles well, where it breaks, and what it would take to push it further. All code is open source.","day":"Day 4 — Session Day 3","time":"1:30pm-1:50pm","room":"Main Stage","type":"session","track":"Harness Engineering","status":"tentative","speakers":["Brendan Rappazzo"]}]},{"name":"Brennan Erbz","role":"CEO & Co-Founder","company":"Flik","twitter":"https://x.com/BrennanErbz","sessions":[{"title":"Beyond Prompts: Building a Multi-Agent Creative Computer That Orchestrates 5+ AI Models in Real-Time","description":"Flik is a production multi-agent system that generates complete work, not pieces. This talk demonstrates how the system orchestrates Claude, Gemini, Nano, Seedance, and Eleven Labs in a single workspace across text, image, video, and audio; shows an end-to-end workflow from prompt to finished output; explains coordination across modalities; and covers built-in likeness/IP safety plus real customer examples.","day":"Day 4 — Session Day 3","time":"3:20pm-3:40pm","room":"Track 1","type":"session","track":"Generative Media","status":"tentative","speakers":["Brennan Erbz"]}]},{"name":"Brian Douglas","role":"Head of DX","company":"Continue","twitter":"https://x.com/bdougieYO","sessions":[{"title":"Don't Write Skills, Train Models","description":"Every AI agent call generates training data. Most teams throw it away. They write skills files instead. Text documents that describe how to do a task and hope the model follows them at inference time. Skills work until they don't. The model drifts, skips steps, hallucinates a shortcut. So you rewrite the skill, add more constraints, hope harder. There's a better path. If you've used a skill enough to know what good output looks like, you already have training data. You just aren't using it. This talk covers what I learned building an open source fine-tuning pipeline that turns agent session traces into SFT and DPO training datasets. A telemetry proxy captures every LLM call as a content-addressed Merkle DAG with zero instrumentation. Successful sessions become supervised fine-tuning data. Pair them against failures, matched by goal category, and you get preference pairs for DPO. No manual labeling. No synthetic data. But training data quality depends on environment consistency. If the same agent produces different results because of package drift, nondeterministic toolchains, or inconsistent system state, your training signal is noise. This is where NixOS changes the equation. A hardened, reproducible OS means every agent session runs against an identical, declarative environment. Nix controls the variables that sandboxing alone doesn't: dependency graphs, system libraries, toolchain versions. When you can guarantee the environment is the same across hundreds of sessions, the behavioral signal in your traces is actually trustworthy. We'll walk through the full pipeline. How to rebuild parent-hash chains from a SQLite database and join facet metadata. How to filter to fully_achieved sessions and truncate 82k-token conversations down to 4k-6k training examples using summary context plus the last three turns. How to match success/failure pairs by goal category and exclude unclear_requirements failures so DPO learns from real agent mistakes, not ambiguous prompts. How QLoRA keeps VRAM low enough to train a 7B model on a single consumer GPU. And what happens when you try DPO on 12GB VRAM (two simultaneous forward passes for logprob computation will teach you about gradient accumulation settings fast). The result: a LoRA adapter trained on your own agent traces, in a reproducible environment, on a single consumer GPU, for less than $2 in cloud compute. No YAML. One config file. All code is open source.","day":"Day 3 — Session Day 2","time":"2:50pm-3:10pm","room":"Track 4","type":"session","track":"Workshops Day 3","status":"tentative","speakers":["Brian Douglas"]},{"title":"Don't Write Skills, Train Models (cont. 2/3)","description":"Continuation block 2 of 3 for Brian Douglas's workshop session.","day":"Day 3 — Session Day 2","time":"3:20pm-3:40pm","room":"Track 4","type":"session","track":"Workshops Day 3","status":"tentative","speakers":["Brian Douglas"]},{"title":"Don't Write Skills, Train Models (cont. 3/3)","description":"Continuation block 3 of 3 for Brian Douglas's workshop session.","day":"Day 3 — Session Day 2","time":"3:45pm-4:05pm","room":"Track 4","type":"session","track":"Workshops Day 3","status":"tentative","speakers":["Brian Douglas"]}]},{"name":"Brian Lewis","role":"AI Product Lead","company":"Millennium","sessions":[{"title":"Which AI startups actually land enterprise contracts? Lessons from evaluating 100+ AI startups at Millennium Management","day":"Day 4 — Session Day 3","time":"1:30pm-1:50pm","room":"Leadership 1","type":"session","track":"AI-Native Enterprises","status":"tentative","speakers":["Brian Lewis"]}]},{"name":"Carlos Sanchez","company":"Adobe","sessions":[{"title":"Agentic Sites: Building Hyper Personalized Websites","day":"Day 3 — Session Day 2","time":"3:20pm-3:40pm","room":"Leadership 1","type":"session","track":"AI-Native Enterprises","status":"tentative","speakers":["Carlos Sanchez"]}]},{"name":"Charlie Guo","company":"OpenAI","sessions":[{"title":"Voice Agents Can Just Do Things","description":"This talk argues that speech is becoming a control plane for software rather than just audio input/output. It introduces three practical patterns—voice-to-action, systems-to-voice, and voice-to-voice—and explains where realtime reasoning and tool-calling matter, and why chained STT/LLM/TTS systems start to break down as interactions become richer.","day":"Day 2 — Session Day 1","time":"11:40am-12:00pm","room":"Track 6","type":"session","track":"Voice & Realtime AI","status":"tentative","speakers":["Charlie Guo"]}]},{"name":"Christopher Lovejoy","role":"Member of Technical Staff","company":"Anthropic","sessions":[{"title":"AI Benchmarks for Vertical Industries: Why we're not measuring what we need to and how to unlock real-world ROI","description":"AI is acing the tests we set for it. So why are so many production deployments falling flat? This talk draws on lessons from building Anterior's internal benchmark for real-world healthcare tasks and how to translate real-world performance into concrete measurement rubrics, use imperfect synthetic data, avoid common pitfalls, and apply the approach to any vertical domain.","day":"Day 4 — Session Day 3","time":"3:45pm-4:05pm","room":"Track 7","type":"session","track":"AI in Healthcare","status":"tentative","speakers":["Christopher Lovejoy"]}]},{"name":"Christopher Manning","role":"Distinguished Member of Technical Staff at Moonlake AI; Thomas M. Siebel Professor in Machine Learning at Stanford University; General Partner at AIX Ventures","company":"Moonlake AI","twitter":"https://x.com/chrmanning","sessions":[{"title":"Building the simulation infrastructure for practical world model use","description":"What is the most important capability for world model applications and the pursuit of embodied AI? We believe it is not a question of having the most beautiful pixels but the ability to reason about causality in multimodal environments. At Moonlake, we are working on building action-conditioned multimodal world models which provide spatial and physical state consistency over long time periods. We believe that building and training on synthetic worlds provides the data and compute efficient path to truly useful world models. We are building the simulation infrastructure platform for companies that need to build and manage worlds (assets, scenes, digital twins) at scale, including robotics/autonomy teams, digital factory operators, and game authors. Our product today primarily finds applicability in simulation and the operationalization of digital twins. Simulation can include training robotics, world models for AGI research, autonomous vehicles, or content creation for media and entertainment. Operationalization of digital twins involves the reconstruction of scans into reusable assets, e.g., turning image and point-cloud scans into sim ready assets for digital factory Integration projects. We are building toward a future where AI systems do not just generate worlds, but understand how they work. Moonlake learns from each workflow: The more workflows, failures, and human interventions that Moonlake sees, the better it becomes at reconstructing, validating, and preparing complex simulation worlds. The session will include discussion and demos.","day":"Day 3 — Session Day 2","time":"10:45am-11:05am","room":"Track 2","type":"sponsor","track":"Robotics & World Models","status":"tentative","speakers":["Christopher Manning"]},{"title":"Building the simulation infrastructure for practical world model use","day":"Day 3 — Session Day 2","time":"11:10am-11:30am","room":"Track 2","type":"sponsor","track":"Robotics & World Models","status":"tentative","speakers":["Christopher Manning"]}]},{"name":"Clara Matos","role":"Director of AI Engineering","company":"Sword Health","sessions":[{"title":"Building a multi-agent system for dialogue-based clinical care","description":"Deploying LLM-based systems in healthcare requires careful orchestration of safety guardrails, memory architectures that preserve clinical context, and rigorous evaluation, all while meeting strict regulatory, privacy, and safety requirements. In this talk, we share how we are building Phoenix, a dialogue-based AI care specialist that guides patients through their care journey with human oversight. We'll walk through our system design: a multi-agent architecture powered by proprietary foundation models; a memory system managing short-term conversation context and long-term patient knowledge; layered safety guardrails using policy-conditioned models for input/output moderation; decision logic for human escalation; and our complete evaluation lifecycle, from offline automated and human evaluation before release, to online observability and A/B testing in production. By the end of this session, you'll walk away with practical lessons learned building a production-grade conversational AI system for clinical care.","day":"Day 4 — Session Day 3","time":"11:40am-12:00pm","room":"Track 7","type":"session","track":"AI in Healthcare","status":"tentative","speakers":["Clara Matos"]}]},{"name":"Clare Liguori","role":"Senior Principal SWE","company":"AWS","twitter":"https://x.com/clare_liguori","sessions":[{"title":"From AI-Assisted to AI-Native: Building a Frontier Development Team","description":"When features that took two weeks now ship in an afternoon, the bottleneck shifts from writing code to making decisions. Frontier teams have discovered this firsthand, achieving 3-10x productivity gains by fundamentally rethinking how developers work with AI agents. This talk covers the practices that separate frontier teams from those who merely \"sprinkle\" AI on their existing workflows: running agents asynchronously for hours, investing in comprehensive agent steering files, enabling local integration testing for agent self-correction, and automating everything from coding to operations to documentation. You'll learn how teams at Amazon slowed down to speed up, the temporary productivity dips they accepted, and the organizational changes required to sustain this velocity.","day":"Day 2 — Session Day 1","time":"2:50pm-3:10pm","room":"Leadership 1","type":"session","track":"AI-Native Enterprises","status":"tentative","speakers":["Clare Liguori"]}]},{"name":"Clay Cockrell","role":"Licensed Clinical Social Worker (LCSW); Founder","company":"CoupleWork","sessions":[{"title":"AI Is Becoming the World's Largest Relationship Therapist. We Should Be Worried About That.","description":"Millions of people are now turning to AI for relationship advice and emotional support, often before they'd ever consider a human therapist. Most of the AI Therapy that is available is without clinical oversight, ethical frameworks, or any serious reckoning with what it means to intervene in the most intimate and vulnerable space in a person's life. People are getting hurt. As a couples therapist with 30 years experience, I teamed up with the former CTO at S&P and we created CoupleWork, an AI relationship therapist I essentially trained on three decades of clinical knowledge and every evidence-based modality that exists. Our voice interactive AI, Maxine, is proving this can be done responsibly and very effectively. And what we're learning about the nature of love, connection, and human vulnerability at scale is something this industry needs to hear. I also want to talk about what comes next: the regulatory frameworks that don't yet exist, the liability questions nobody is answering, and why the therapists who should be leading this conversation are almost entirely absent from it.","day":"Day 4 — Session Day 3","time":"1:30pm-1:50pm","room":"Track 7","type":"session","track":"AI in Healthcare","status":"tentative","speakers":["Clay Cockrell"]}]},{"name":"Cody Menefee","company":"Firecrawl","sessions":[{"title":"You’re Not Thinking Big Enough: Rebuilding Food Systems from First Principles with AI Agents","day":"Day 2 — Session Day 1","time":"2:25pm-2:45pm","room":"Track 2","type":"sponsor","track":"Vision & OCR","status":"tentative","speakers":["Cody Menefee"]}]},{"name":"Corey Gallon","role":"Managing Director","company":"Rexmore","sessions":[{"title":"The Dark Arts of Web Automation: Teaching Agents to Use Websites Like Humans","description":"Anything you can do in a browser, your agent can do too. Not by tiptoeing through an MCP server one polite, token-burning call at a time -- properly, programmatically, the way you'd drive any other tool. I'll show you how with chrome-agent, an open source wrapper over the Chrome DevTools Protocol that has become irreplaceable in my everyday work. If you'll ever do a browser task more than once, step-by-step MCP browsing is slow, brittle, and bills you tokens for every single click. A CLI straight onto CDP makes the whole browser programmable: loop it, pipe it, script it, walk away. Write it Tuesday, run it a thousand times Wednesday, all without a second of AI agent babysitting. We'll dispel the MCP hype and myths, with successful demonstrations of cheeky things like: the power of CLI-based browsing and how its so much more capable than mere MCP; reaching through those oh-so-clever cross-origin iframes to clear the verify you're human checkboxes; showing that a JavaScript .click() is not a click, rather, just a function call in a costume that is banhammerable; ultimately, proving that a CDP browser operates just like a meatbag with a mouse and keyboard. You'll learn how to point your AI agents at real, messy, uncooperative websites and web applications and have them get things done exactly the way that you would.","day":"Day 3 — Session Day 2","time":"11:10am-11:30am","room":"Track 7","type":"session","track":"Computer Use","status":"tentative","speakers":["Corey Gallon"]}]},{"name":"Cyrus Clarke","role":"Researcher, Tangible Media Group","company":"MIT Media Lab","sessions":[{"title":"I gave an AI a body","description":"I gave an AI a body. Not a body in the fleshy sense, or even a humanoid shell, but a form through which it can express itself, explore itself, and maybe even discover who or what it is. The three videos I've released documenting my encounters have crossed 15 million views, provoking responses from awe to anxiety. The body was a 900-pin shape display at MIT Media Lab. The idea was simple in principle, strange in practice: install an AI agent on the connected machine, give it access to the codebase, and rather than telling it what to do, ask it to discover itself through the physical form. Its first deliberate act was to breathe. The whole grid rising and falling. Hypnotically. Then it reached for its own edges. When asked to say hello it spelled \"H-I, C-Y-R-U-S !\", defaulting to the most familiar human legible symbols it knows. Inspired by Ted Chiang's Story of Your Life, I wanted a language the agent could create itself. It proposed a vocabulary of its own gestures, built through a learning loop it named BODYLAB. The talk is about encountering another intelligence, and what I learned along the way: the memory architecture, the closed-loop pipeline that generates, scores and stores gestures, the validation gates that keep them legible, and the moments stranger than tool use, where an LLM not developed for motion learns what to do with a body.","day":"Day 3 — Session Day 2","time":"3:45pm-4:05pm","room":"Track 2","type":"sponsor","track":"Robotics & World Models","status":"tentative","speakers":["Cyrus Clarke"]}]},{"name":"Daksh Gupta","company":"Greptile","sessions":[{"title":"What we learned by analyzing 1M AI-generated PRs","description":"Charlie Holtz (@charlieholtz) - Founder & CEO of Conductor (launched Jul 2025). Mac app for orchestrating multiple coding agents in parallel. Used at YC cos, Linear, Vercel, Notion, Supabase. Ex-Replicate, ex-Point72, Brown CS. Angle: parallel agent orchestration / humans-as-conductors - distinct from the rest of the Coding Agents track. Ref tweet: https://x.com/charlieholtz/status/2047351098634338610","day":"Day 2 — Session Day 1","time":"2:25pm-2:45pm","room":"Main Stage","type":"session","track":"Software Factories","status":"tentative","speakers":["Daksh Gupta"]}]},{"name":"Dan Bjornn","company":"Leaseend","sessions":[{"title":"Your Fine-Tuned Model Is Tech Debt: A 50x ROI House of Cards","day":"Day 2 — Session Day 1","time":"3:20pm-3:40pm","room":"Leadership 2","type":"session","track":"AI Architects: Show my Workflow","status":"tentative","speakers":["Dan Bjornn"]}]},{"name":"Dan Feng","company":"Maven Clinic","sessions":[{"title":"How to build an AI-Native Health Company","day":"Day 4 — Session Day 3","time":"2:50pm-3:10pm","room":"Track 7","type":"session","track":"AI in Healthcare","status":"tentative","speakers":["Dan Feng"]}]},{"name":"Daniel Han","company":"Unsloth","sessions":[{"title":"HOLD — Daniel Han (Unsloth) 3hr workshop","day":"Day 1 — Workshop Day","time":"9:00am-12:00pm","room":"Track 9","type":"workshop","track":"Track 9","status":"hold","speakers":["Daniel Han"]}]},{"name":"Daniel Kim","role":"Head of Growth","company":"Cerebras Systems","twitter":"https://x.com/learnwdaniel","sessions":[{"title":"All the Things We Have to Do to Satisfy Your Insatiable Need for Tokens","description":"Every time the industry figures out how to serve tokens faster and cheaper, the appetite grows to match. Models get bigger, contexts get longer, agents start chaining thousands of calls together. The finish line keeps moving. This talk is a technical tour through everything the industry has done to keep up, led by two experts in high-performance inference. We'll start with the optimizations that made hardware work harder without changing the underlying architecture. Then we'll go up a level with techniques that work smarter across requests and across the model itself. And finally, a peek into the future with heterogeneous disaggregated inference, the architectural shift that splits prefill and decode across specialized hardware, and even more advanced forms of hardware specialization coming your way soon. Token demand is about to get a lot more insatiable. Let's see what the future has in store for us!","day":"Day 4 — Session Day 3","time":"11:40am-12:00pm","room":"Leadership 1","type":"session","track":"Inference","status":"tentative","speakers":["Daniel Kim"]}]},{"name":"David Hsu","company":"Retool","sessions":[{"title":"Governance Is the Real Bottleneck to AI ROI","day":"Day 2 — Session Day 1","time":"1:55pm-2:15pm","room":"Track 1","type":"session","track":"Claws & Personal Agents","status":"tentative","speakers":["David Hsu"]}]},{"name":"David Levine","role":"Founder & CEO","company":"Kiduna Club","sessions":[{"title":"Beyond the Lethal Trifecta: Agentic Commerce on the Open Internet at Machine Speed","description":"For decades, the internet has had protocols for routing, identity, encryption, payments, and commerce between people and organizations. It has never had a native way for autonomous agents to possess authority, accountability, or legal standing. On July 1, 2026 that changes. A little known law will take effect that changes the world as we know it. As AI agents move beyond the enterprise firewall, a new form of commerce is emerging. Agents can already search, negotiate, schedule, purchase, settle payments, and coordinate work across networks. But the moment they begin acting independently on behalf of people, businesses, and online organizations, fundamental questions appear: Who does this agent represent? What authority does it possess? Who is responsible when something goes wrong? How do counterparties know they can trust it? This talk explores the \"Lethal Trifecta\" of agentic systems: access to systems, access to networks, and autonomy. Together they create extraordinary capabilities, but they also expose a missing layer in the architecture of the internet itself. Without identity, accountability, governance, and legal standing, agentic commerce remains trapped inside enterprise walls, limited to productivity gains rather than participation in open markets. On the same day as this conference, a new legal framework takes effect that gives autonomous online organizations a registered legal existence, allowing them to hold assets, enter agreements, govern themselves through software, and operate through fleets of agents. Whether you're building agents, agent platforms, autonomous organizations, payment systems, governance systems, or the next generation of internet infrastructure, this shift has global implications, and you'll be the first to know. We'll examine the emerging trust stack for agentic commerce—identity, authority, governance, settlement, and standing—and explore what happens when agents stop acting merely as tools and begin participating as economic actors on the open internet at machine speed.","day":"Day 4 — Session Day 3","time":"3:45pm-4:05pm","room":"Track 2","type":"sponsor","track":"Agentic Commerce","status":"tentative","speakers":["David Levine"]}]},{"name":"Deniz Birlikci","company":"NeoLabs","sessions":[{"title":"NeoLabs — Cognition","day":"Day 2 — Session Day 1","time":"11:40am-12:00pm","room":"Track 9","type":"session","track":"Data Quality","status":"tentative","speakers":["Deniz Birlikci","Sam Lee"]}]},{"name":"Dhruv Nathawani","role":"Senior Research Scientist","company":"NVIDIA","sessions":[{"title":"Teaching Agents to Search: Building Synthetic Training Pipelines with NVIDIA Data Designer","description":"Modern agentic systems often fail because the right training data simply does not exist. Search agents are a perfect example: if you want a model to browse the web effectively, you need high-quality multi-step trajectories that teach it how to search, refine queries, inspect sources, and recover from dead ends. In this session, attendees will learn how NVIDIA used Data Designer to build synthetic supervised fine-tuning data for search-capable Nemotron models, including how to define task structure, generate seed examples, produce realistic search trajectories, filter low-quality generations, and convert traces into training-ready records. The session will also cover BrowseComp-style tasks, tool-use rollouts, validation, dataset curation, and a reusable framework for designing custom datasets for specialized behaviors across reasoning, tool use, and domain-specific applications.","day":"Day 2 — Session Day 1","time":"2:50pm-3:10pm","room":"Track 9","type":"session","track":"Data Quality","status":"tentative","speakers":["Dhruv Nathawani"]}]},{"name":"Diogo Almeida","role":"Co-founder and CEO","company":"TypeSafe AI","sessions":[{"title":"What's next after RLHF?","description":"RLHF was a massive commercial success: roughly 100% of LLM usage is through RLHF’d models - but it was in many ways also a research failure. Let’s talk about how it conquered the world, how it defied its creators expectations, why AI is in the bimodal state it’s in (is it a bubble or a machine god?), and how to make AI actually transform the economy.","day":"Day 3 — Session Day 2","time":"10:45am-11:05am","room":"Track 9","type":"session","track":"Posttraining & Midtraining","status":"tentative","speakers":["Diogo Almeida"]}]},{"name":"Divakar Kumar","role":"Technical Architect","company":"Flyers Soft Private Limited","sessions":[{"title":"Let's integrate AI Agents in Event-Sourced Systems","description":"Fraud detection has always been a race against time. In traditional event-sourced systems, every transaction, login, or transfer is captured as a sequence of immutable events. These events tell a clear story — but only after the fact. What if events could do more than just record history? What if they could talk back? In this talk, we’ll explore how agentic event-driven systems transform fraud detection. Imagine every PaymentInitiated, LoginAttempt, or DeviceChanged event not just being logged, but immediately consumed by an autonomous Fraud Detection Agent. This agent correlates events across accounts, reasons over historical event streams, and generates new events like SuspiciousActivityFlagged or TransactionHeldForReview. Through a real-world inspired use case in banking and digital payments, we’ll show: - How event sourcing provides the perfect memory layer for fraud detection agents - Patterns for agents to safely inject new domain events without violating invariants - How to avoid runaway feedback loops when multiple agents interact (e.g., fraud + compliance + customer service agents) - Governance, auditing, and explainability challenges when autonomous agents take part in mission-critical workflows By the end of this session, you’ll see how event-driven DDD systems evolve when agents stop being passive consumers and start actively shaping the event stream — turning fraud detection from a reactive process into a proactive, adaptive defense.","day":"Day 4 — Session Day 3","time":"12:05pm-12:25pm","room":"Track 3","type":"session","track":"AI in Finance","status":"tentative","speakers":["Divakar Kumar"]}]},{"name":"Elie Bakouch","role":"Researcher","company":"Prime Intellect","twitter":"https://x.com/eliebakouch","sessions":[{"title":"auto-nanogpt","day":"Day 3 — Session Day 2","time":"12:05pm-12:25pm","room":"Main Stage","type":"session","track":"Autoresearch","status":"tentative","speakers":["Elie Bakouch"]}]},{"name":"Emil Eifrem","role":"Co-Founder and CEO","company":"Neo4j","twitter":"https://x.com/emileifrem","sessions":[{"title":"Emil Eifrem keynote and Graphs track intro","day":"Day 4 — Session Day 3","time":"9:00am-9:10am","room":"Main Stage","type":"keynote","track":"Graphs","status":"tentative","speakers":["Emil Eifrem"]}]},{"name":"Eric Zhu","company":"Alibaba","sessions":[{"title":"QwenPaw: building AI that you can trust","day":"Day 2 — Session Day 1","time":"2:50pm-3:10pm","room":"Track 1","type":"session","track":"Claws & Personal Agents","status":"tentative","speakers":["Eric Zhu"]}]},{"name":"Erik Meijer","role":"Computer scientist and entrepreneur","company":"Leibniz Labs","twitter":"https://x.com/e_meijer","sessions":[{"title":"In Code They Act, In Proof We Trust","description":"AI agents today execute on blind trust, and the failure modes are already in the headlines: a dealership chatbot agreeing to sell a $76,000 Chevy Tahoe for $1, a coding agent wiping a production database during a code freeze, and an \"agent skill\" installing a keylogger on a developer's machine. Automind enforces a different discipline: before any action runs, the agent submits an execution plan plus a machine-checkable proof of safety and correctness in Universalis, and a small checker decides whether the plan is allowed to execute. The result is left-shifted trust, with policy compliance established before the first side effect.","day":"Day 4 — Session Day 3","time":"9:50am-10:10am","room":"Main Stage","type":"keynote","track":"Harness Engineering","status":"confirmed","speakers":["Erik Meijer"]}]},{"name":"Erina Karati","role":"Software Development Engineer","company":"Microsoft","sessions":[{"title":"Autoresearch in a Multi-Agent AI Village","description":"Project Paradox is an existing multi-agent framework built at Supercell's first AI Innovation Lab, which has a 3D Unity village with local LLM powered agents. The characters remember conversations, update emotional state, track trust, plan actions, move through rooms, transfer items, and talk to each other through a FastAPI backend. The new work is an autoresearch layer around that village. We built a backend loop that runs controlled social scenarios, scores the resulting NPC behavior, proposes protocol or policy changes, reruns the suite, and keeps changes that improve the agents. The goal is to move beyond one good chat response and measure whether an NPC society can preserve source attribution, verify claims, spread important information, coordinate goals, and replan after new information arrives. The talk walks through the system architecture and the lessons from building it. We show the backend simulation harness that executes Unity style actions without opening Unity, the scenario suites that test information diffusion and memory provenance, and the ratchet loop that edits protocol text or planner policy with rollback. One accepted run improved information diffusion by teaching agents to broadcast important sourced evidence while preserving who said it. The practical takeaway is a reusable pattern for AI engineers building agents with messy state. Freeze the harness, expose a small editable policy surface, score real behavior instead of vibes, and let an agent search for improvements under rollback. The same pattern applies to game agents, coding agents, support agents, personal agents, and other systems where long horizon behavior matters more than a single response.","day":"Day 3 — Session Day 2","time":"1:30pm-1:50pm","room":"Main Stage","type":"session","track":"Autoresearch","status":"tentative","speakers":["Erina Karati"]}]},{"name":"Ethan Cha","company":"Carlyle","sessions":[{"title":"Dual-Surface Architecture: Serving Humans and Agents from the Same Tool Layer","day":"Day 2 — Session Day 1","time":"10:45am-11:05am","room":"Track 5","type":"sponsor","track":"Security","status":"tentative","speakers":["Ethan Cha"]}]},{"name":"Eyal Blum","company":"Figma","sessions":[{"title":"How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage)","day":"Day 2 — Session Day 1","time":"3:20pm-3:40pm","room":"Leadership 1","type":"session","track":"AI-Native Enterprises","status":"tentative","speakers":["Eyal Blum"]}]},{"name":"Filip Makraduli","role":"Machine Learning Engineer","company":"Superlinked","sessions":[{"title":"Weight Folding, CUDA Streams, and the Bug That Made My Model Speak Backwards","day":"Day 4 — Session Day 3","time":"3:20pm-3:40pm","room":"Track 9","type":"session","track":"Inference","status":"tentative","speakers":["Filip Makraduli"]}]},{"name":"Francesco Bonacci","role":"Founder & CEO","company":"Cua","twitter":"https://x.com/pauliebonacci","sessions":[{"title":"Computer-Use 2.0: Agents Just Got Multi-Cursor","description":"Computer-use agents still inherit a basic desktop limitation: one machine has one foreground app, one hardware cursor, and one active actor. Once you try to run more than one agent per desktop, they start stealing focus from the user and from each other. We built cua-driver around a different model: multiple agents operating real desktop applications in parallel, each with its own synthetic pointer, while the user's cursor and keyboard stay undisturbed. The key move is to stop treating hardware mouse and keyboard events as the primary automation layer. cua-driver goes one layer lower, into the OS plumbing behind accessibility: UI Automation on Windows, AT-SPI on Linux, and AX on macOS. Those APIs address applications and elements directly, so the OS does not require the target window to be frontmost. A click can land on a background window. A keystroke can reach a hidden one. Multiple agents can act at once because none of them is competing for the singleton hardware mouse. I'll walk through the architecture, the API shape, and the platform-specific traps we hit while making it work across Windows, macOS, and Linux. The live demo is three agents operating on one desktop while the user keeps typing uninterrupted. The goal is to make Computer-Use 2.0 feel concrete: what changes in the stack, what becomes possible, and where the approach still leaks, including Wayland, Chromium DOM surfaces, native canvas apps, and fallback input paths.","day":"Day 3 — Session Day 2","time":"1:55pm-2:15pm","room":"Track 7","type":"session","track":"Computer Use","status":"tentative","speakers":["Francesco Bonacci"]}]},{"name":"Garry Tan","sessions":[{"title":"Closing Keynote: Garry Tan","day":"Day 4 — Session Day 3","time":"4:50pm-5:10pm","room":"Main Stage","type":"keynote","track":"Main Stage","status":"confirmed","speakers":["Garry Tan"]}]},{"name":"Gavin Uberti","role":"Co-Founder & CEO","company":"Etched","sessions":[{"title":"Gavin Uberti — transformer-only ASICs for inference","description":"Etched's Sohu approach to transformer inference on custom silicon.","day":"Day 4 — Session Day 3","time":"1:55pm-2:15pm","room":"Track 9","type":"session","track":"Inference","status":"tentative","speakers":["Gavin Uberti"]}]},{"name":"Giselle van Dongen","company":"Restate.dev","sessions":[{"title":"🎵 Every step you take, every call you make - the reliable agent stack","day":"Day 4 — Session Day 3","time":"1:55pm-2:15pm","room":"Main Stage","type":"session","track":"Harness Engineering","status":"tentative","speakers":["Giselle van Dongen"]}]},{"name":"Graham McBain","company":"Sourcegraph","sessions":[{"title":"Graham McBain","day":"Day 2 — Session Day 1","time":"11:10am-11:30am","room":"Track 8","type":"session","track":"Forward Deployed Engineering","status":"tentative","speakers":["Graham McBain"]},{"title":"The Death of Developer Advocates","day":"Day 4 — Session Day 3","time":"2:50pm-3:10pm","room":"Track 6","type":"session","track":"AI in GTM","status":"confirmed","speakers":["Graham McBain"]}]},{"name":"Gus Iwanaga","role":"Product, UX, and Engineering lead for mosAIc","company":"commercetools","sessions":[{"title":"The End of the Static Screen: Architecting Intent-Driven UX with Agentic Orchestration","description":"For 30 years, interfaces were designed ahead: wireframes, fixed flows, pre-built dashboards - because we couldn't make them otherwise. Three shifts changed the constraint: LLMs that reason over business context, agentic frameworks that work at production grade, and composable backends that expose a real tool surface. With all three in place, the interface stops being something you design and ships as the output of an orchestrator composing it per intent. I'll walk through the hypothesis, the architecture we're running in production for enterprise commerce, and a live demo where it all moves.","day":"Day 4 — Session Day 3","time":"2:25pm-2:45pm","room":"Track 2","type":"sponsor","track":"Agentic Commerce","status":"tentative","speakers":["Gus Iwanaga"]}]},{"name":"Harrison Chase","role":"CEO & Co-Founder","company":"LangChain","twitter":"https://x.com/hwchase17","sessions":[{"title":"Continual Learning for AI Agents","description":"A talk on continual learning for AI agents across the model, harness, and context layers, including traces, harness updates, and context/memory updates.","day":"Day 3 — Session Day 2","time":"1:55pm-2:15pm","room":"Track 3","type":"session","track":"Memory & Continual Learning","status":"tentative","speakers":["Harrison Chase"]}]},{"name":"Hassan El Mghari","company":"Together","sessions":[{"title":"The Missing Layer: Design Taste in AI Agents // Stop Letting Your Agents Ship Ugly UIs","day":"Day 3 — Session Day 2","time":"1:55pm-2:15pm","room":"Track 6","type":"session","track":"AI Designers/Design Engineers","status":"tentative","speakers":["Hassan El Mghari"]}]},{"name":"Idan Gazit","role":"Senior Director of Research, GitHub Next","company":"GitHub","sessions":[{"title":"Realtime multiplayer, automation, and you!","description":"Now that the models are powerful and the agents are capable, why are we still approaching software development as if it's the same activity that it used to be, but \"faster\"? GitHub Next thinks about what this future wants to be through two lenses: - Automation: intelligence allows us to automate much more than we could with heuristics alone. How should that automation work? What guardrails do we have to put in place so that our CISOs allow us to do that? - Collaboration: agents can understand anything in your codebase, but what about all the facts that are in the heads of your teammates? Whether it's corporate politics or taste, how do we get the humans to leak that context where agents can see it and use it to produce better outcomes? Realtime multiplayer tools have displaced every turn-based tool out there. What should that look like for code? It's not going to be as simple as multiple cursors. Come by to hear more about what GitHub Next is learning about the changing shape of software creation — one that allows us to build better, not merely faster. One that allows us to scale up teams, not only individuals. And one where automations buy us time for craft and polish, not slop. We were promised flying cars, instead we have fifteen terminals. Let's have a nicer future than that.","day":"Day 4 — Session Day 3","time":"3:45pm-4:05pm","room":"Track 4","type":"session","track":"Agentic Engineering","status":"tentative","speakers":["Idan Gazit"]}]},{"name":"Imad","sessions":[{"title":"AI-Native Organisations runs on Skills: How to Extract, Structure, evaluate and Scale Them","day":"Day 3 — Session Day 2","time":"12:05pm-12:25pm","room":"Leadership 1","type":"session","track":"AI-Native Enterprises","status":"tentative","speakers":["Imad"]}]},{"name":"Irwan Bello","company":"NeoLabs","sessions":[{"title":"NeoLabs — Stealth","day":"Day 2 — Session Day 1","time":"12:05pm-12:25pm","room":"Track 9","type":"session","track":"Data Quality","status":"tentative","speakers":["Irwan Bello"]}]},{"name":"Jack Morris","role":"AI researcher","company":"Cornell / Meta FAIR","twitter":"https://x.com/jxmnop","sessions":[{"title":"Jack Morris — Context Is Not Memory, Updating Weights Is","description":"A case for when context is enough, and when updating weights may be the real memory mechanism.","day":"Day 3 — Session Day 2","time":"11:40am-12:00pm","room":"Track 3","type":"session","track":"Memory & Continual Learning","status":"tentative","speakers":["Jack Morris"]}]},{"name":"James Brown","company":"Stripe","sessions":[{"title":"How to avoid disaster when vibe-coding a billing engine","day":"Day 3 — Session Day 2","time":"11:10am-11:30am","room":"Leadership 1","type":"session","track":"AI-Native Enterprises","status":"tentative","speakers":["James Brown"]}]},{"name":"James Russo","role":"Senior Software Engineer, project lead","company":"HeyGen","twitter":"https://x.com/jrusso1020","sessions":[{"title":"HTML Is All Agents Need","description":"AI agents compose videos by writing HTML, CSS, and JS.","day":"Day 4 — Session Day 3","time":"11:10am-11:30am","room":"Track 1","type":"session","track":"Generative Media","status":"tentative","speakers":["James Russo"]}]},{"name":"Jan Curn","role":"Founder & CEO","company":"Apify","sessions":[{"title":"x402 isn’t good (yet)","description":"While everyone understands that agents will get more done with a budget, no one knows which protocol will win agentic payment standard wars: x402, MPP, Skyfire, or another? So far, x402 is the most mature protocol with the largest transaction volume, but even its new \"upto\" payment scheme doesn’t support true usage-based pricing, as it gives agents a chance to consume resources and then skip out on the bill. 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This talk examines how combining multiple sensing modalities, such as vision, depth, motion, and proprioception, leads to more stable and reliable perception compared to single-sensor systems. I will walk through practical system designs that integrate LiDAR, RGB-D cameras, and inertial measurement units, with a focus on how these pipelines handle high data throughput while meeting real-time constraints. The discussion highlights trade-offs in latency, bandwidth, and robustness, especially in safety-critical environments. Examples from industrial deployments show how sensor fusion improves object detection, mapping consistency, and fault tolerance. I will also touch on how redundant and cooperative fusion strategies help systems continue operating under sensor degradation or environmental noise. Beyond perception, the session looks at advances in tactile sensing and motion feedback, including biomimetic approaches that improve manipulation tasks. Emerging directions such as compact MEMS sensors and event-based vision will be discussed in terms of their practical impact on system efficiency and deployment flexibility. The goal is to provide a clear, engineering-focused view of how multimodal sensing systems are built, where they succeed, and where challenges remain for real-world robotics.","day":"Day 3 — Session Day 2","time":"2:50pm-3:10pm","room":"Track 2","type":"sponsor","track":"Robotics & World Models","status":"tentative","speakers":["Karan Singh Jain"]}]},{"name":"Karan Vaidya","company":"Composio","sessions":[{"title":"Tool Execution layer for agents","day":"Day 2 — Session Day 1","time":"2:25pm-2:45pm","room":"Track 1","type":"session","track":"Claws & Personal Agents","status":"tentative","speakers":["Karan Vaidya"]}]},{"name":"Kate Deyneka","role":"Solo founder","company":"Reelful","twitter":"https://x.com/katedeyneka","sessions":[{"title":"Reelful: AI-generated Reels from photos and clips","description":"AI-powered mobile app that turns photos and short clips into ready-to-post Instagram Reels and TikToks without timeline editing, manual prompting, or voice recording.","day":"Day 4 — Session Day 3","time":"12:05pm-12:25pm","room":"Track 1","type":"session","track":"Generative Media","status":"tentative","speakers":["Kate Deyneka"]}]},{"name":"Katelyn Lesse","role":"Head of Engineering, Claude Platform","company":"Anthropic","twitter":"https://x.com/katelyn_lesse","sessions":[{"title":"Katelyn Lesse & Angela Jiang (Anthropic)","day":"Day 2 — Session Day 1","time":"9:30am-9:50am","room":"Main Stage","type":"keynote","track":"Software Factories","status":"confirmed","speakers":["Katelyn Lesse","Angela Jiang"]}]},{"name":"Keegan McCallum","company":"urun.sh","sessions":[{"title":"Generative Video at the Speed of Light","day":"Day 4 — Session Day 3","time":"2:25pm-2:45pm","room":"Track 1","type":"session","track":"Generative Media","status":"tentative","speakers":["Keegan McCallum"]}]},{"name":"Keiji Kanazawa","company":"Microsoft","sessions":[{"title":"I Let Agents Refactor My Codebase for 3 Weeks. 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Kenton will show how Gadgets solves these problems, including a sandbox design that makes it essentially impossible for apps to have vulnerabilities at all. He'll then open source it for your slop-forking pleasure.","day":"Day 2 — Session Day 1","time":"5:10pm-5:30pm","room":"Main Stage","type":"keynote","track":"Software Factories","status":"tentative","speakers":["Kenton Varda"]}]},{"name":"Kevin Madura","sessions":[{"title":"It’s Tokens All The Way Down: How RLMs are Different","day":"Day 3 — Session Day 2","time":"12:05pm-12:25pm","room":"Track 8","type":"session","track":"Context Engineering","status":"tentative","speakers":["Kevin Madura"]}]},{"name":"Khaled Alashmouny","company":"Aidachip","sessions":[{"title":"What If Your Chip Design Team Moved Like a Single Body?","day":"Day 2 — Session Day 1","time":"11:10am-11:30am","room":"Leadership 2","type":"session","track":"AI Architects: Show my Workflow","status":"tentative","speakers":["Khaled Alashmouny"]}]},{"name":"Kim Maida","role":"Head of Developer Relations & Founding GTM Engineer","company":"Keycard Labs","twitter":"https://x.com/DevRelUs","sessions":[{"title":"It's 10pm. 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You'll walk away knowing how to control agent access whether you're running coding agents from the CLI, building MCP servers, or connecting agents to third-party APIs.","day":"Day 2 — Session Day 1","time":"12:05pm-12:25pm","room":"Track 5","type":"sponsor","track":"Security","status":"tentative","speakers":["Kim Maida"]}]},{"name":"Krishna Chaitanya Balusu","company":"Meta","sessions":[{"title":"Three Metrics That Actually Predict Agent Reliability","day":"Day 3 — Session Day 2","time":"11:40am-12:00pm","room":"Leadership 1","type":"session","track":"AI-Native Enterprises","status":"tentative","speakers":["Krishna Chaitanya Balusu"]}]},{"name":"Krishna Srinivasan","company":"sarvam.ai","sessions":[{"title":"From Scratch to SOTA: Training a 3B State-Space Vision Model for 1.4 Billion People","day":"Day 2 — Session Day 1","time":"1:55pm-2:15pm","room":"Track 2","type":"sponsor","track":"Vision & OCR","status":"tentative","speakers":["Krishna Srinivasan"]}]},{"name":"Kurtis Van Gent","role":"Senior Staff Software Engineer","company":"Google Cloud","sessions":[{"title":"Build-Time vs. Run-Time: Why Your Dev Tools Will Fail in Production","day":"Day 3 — Session Day 2","time":"11:40am-12:00pm","room":"Track 8","type":"session","track":"Context Engineering","status":"tentative","speakers":["Kurtis Van Gent"]}]},{"name":"Kwindla Kramer","company":"Daily","sessions":[{"title":"The New Primitives: Building AI-Native Software","description":"In the future, every piece of software with a human-facing surface will be built from new, LLM-centric primitives. 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It wasn't a zero-day. It wasn't a clever new attack class. It was a default config someone never flipped off. That story is not the exception in production ML, it's the rule. We synthesized 139 peer-reviewed papers on production ML security across access control, runtime security, infrastructure, and operations. Five findings stood out, and one of them upends how most teams think about ML security: - Misconfiguration, not missing features, is the dominant failure mode. The mechanisms exist. Teams aren't using them, or are using them wrong. - Adversarial defenses impose 15–30% inference overhead, which is why almost no production system actually runs them. - ML-specific security tooling lags general DevOps tooling by years. - Security, data-science, and ops teams operate in expertise silos that create persistent gaps no single team can see. - LLM and multi-tenant GPU threats are evolving faster than defenses (prompt injection, RAG poisoning, GPU side channels). 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You'll leave with hands-on experience debugging sandbox failures and a set of observability and scaling patterns you can start implementing.","day":"Day 3 — Session Day 2","time":"2:25pm-2:45pm","room":"Track 1","type":"session","track":"Sandbox & Platform Engineering","status":"tentative","speakers":["Matt Brockman"]}]},{"name":"Matthew Jewkes","sessions":[{"title":"Superhuman performance is a shape, not just nines.","day":"Day 3 — Session Day 2","time":"1:55pm-2:15pm","room":"Leadership 2","type":"session","track":"AI Architects: Tokenmaxxing","status":"tentative","speakers":["Matthew Jewkes"]}]},{"name":"Max Drake","company":"TLDraw","sessions":[{"title":"The Spatial Harness: Bringing Agents to the Canvas","day":"Day 3 — Session Day 2","time":"11:10am-11:30am","room":"Track 6","type":"session","track":"AI Designers/Design Engineers","status":"tentative","speakers":["Max Drake"]}]},{"name":"Maximillian Piras","company":"Yutori","sessions":[{"title":"Mousepower: agents that can’t be measured, can’t be managed.","day":"Day 3 — Session Day 2","time":"12:05pm-12:25pm","room":"Track 6","type":"session","track":"AI Designers/Design Engineers","status":"tentative","speakers":["Maximillian Piras"]}]},{"name":"Meaghan Choi","role":"Product Designer, Claude Code","company":"Anthropic","twitter":"https://x.com/meag_han_c","sessions":[{"title":"Claude Code for Designers","description":"How designers can use Claude Code to move from Figma to working code.","day":"Day 3 — Session Day 2","time":"1:30pm-1:50pm","room":"Track 6","type":"session","track":"AI Designers/Design Engineers","status":"hold","speakers":["Meaghan Choi"]}]},{"name":"merve noyan","role":"Developer Advocate","company":"Huggingface","sessions":[{"title":"Skill issue: stop deploying vision language models, use them with Skills to build e2e vision apps on edge","day":"Day 2 — Session Day 1","time":"11:40am-12:00pm","room":"Track 2","type":"sponsor","track":"Vision & OCR","status":"tentative","speakers":["merve noyan"]}]},{"name":"Michael Forrester","role":"Principal Training Architect","company":"CNCF","twitter":"https://x.com/peopleforrester","sessions":[{"title":"Build a Platform, Unleash an Agent on it.... and Watch it Burn!","day":"Day 2 — Session Day 1","time":"11:10am-11:30am","room":"Track 5","type":"sponsor","track":"Security","status":"tentative","speakers":["Michael Forrester"]}]},{"name":"Michel Tricot","role":"Co-Founder & CEO","company":"Airbyte","twitter":"https://x.com/MichelTricot","sessions":[{"title":"Airbyte — Data engineering for AI engineers","day":"Day 1 — Workshop Day","time":"9:00am-11:00am","room":"Track 4","type":"sponsor","track":"Track 4","status":"tentative","speakers":["Michel Tricot"]}]},{"name":"Midam Kim","role":"Senior Linguist and ML Engineer","company":"ServiceNow","sessions":[{"title":"\"My name is... my name is...\": A Linguistic Framework for Debugging Voice AI Failures","description":"Every voice AI engineer has heard it: a caller repeating their name three times, getting more frustrated with each attempt. 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Why a transcript that's \"cumulatively correct\" can still ruin the user experience Drawing on examples from production multilingual voice AI work, I'll show where linguistic expertise connects to the engineering decisions you're already making and where it reveals failure modes that confidence scores will never warn you about. Who this is for: Voice AI engineers, ML practitioners on Voice AI pipelines, and anyone who's watched clean logs while their agent quietly fails real users.","day":"Day 2 — Session Day 1","time":"3:20pm-3:40pm","room":"Track 6","type":"session","track":"Voice & Realtime AI","status":"tentative","speakers":["Midam Kim"]}]},{"name":"Mihnea Munteanu","role":"Software Engineer","company":"YouTube","twitter":"https://x.com/MicneaPPK","sessions":[{"title":"Don't Summarize. 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You will tackle the hardest parts of real-time telephony: routing audio streams, handling human interruption, and killing latency. In 60 minutes, your AI will be ready to call restaurants for the daily special, book appointments, and actively negotiate on your behalf.","day":"Day 2 — Session Day 1","time":"1:30pm-1:50pm","room":"Track 4","type":"session","track":"Workshops Day 2","status":"tentative","speakers":["Neil Zeghidour"]},{"title":"Everybody Gets a Digital Clone! (Part 2 of 3)","description":"Continuation of Neil Zeghidour's hands-on workshop on building a deployed digital clone for real-time phone calls using OpenClaw, Twilio, and Gradium.","day":"Day 2 — Session Day 1","time":"1:55pm-2:15pm","room":"Track 4","type":"session","track":"Workshops Day 2","status":"tentative","speakers":["Neil Zeghidour"]},{"title":"Everybody Gets a Digital Clone! 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But most users live by a different rule: \"I don't know what I want — I'll know it when I see it\". If agentic commerce is ever going to cross the chasm, these systems need to stop waiting and start co-shopping. The future of commerce belongs to agentic collaborators that offer a white-glove, personal shopper experience - entirely absorbing the cognitive burden of product discovery, deep research, and validation. Rather than requiring shoppers to input exact search terms or define clear objectives, modern shopping systems will seamlessly guide them from a rough idea to the ideal product. By leveraging multimodal capabilities, these assistants can interpret abstract aesthetic \"vibes\" to understand user preferences, generate visual references to clarify questions, and enable a highly immersive try-before-you-buy experience to validate products, keeping the user aligned and visually grounded throughout the process. This talk will explore how advanced systems like Gemini work alongside users to clarify their preferences during the discovery process, co-navigate fluidly generated product categories, leverage individual context to filter choices, and produce interactive side-by-side comparisons tailored to the buyer's key priorities. The session will also cover robust auto-rater frameworks and how to design evals for high-agency execution. Attendees building conversational agents, managing complex product data graphs, or creating next-generation multimodal agentic interfaces will gain practical frameworks and insights to deliver highly personalized experiences at scale.","day":"Day 4 — Session Day 3","time":"10:45am-11:05am","room":"Track 2","type":"sponsor","track":"Agentic Commerce","status":"tentative","speakers":["Nidhi Vyas"]}]},{"name":"Niels Rogge","company":"Huggingface","sessions":[{"title":"How I automate my own job at Hugging Face using agents","day":"Day 2 — Session Day 1","time":"2:50pm-3:10pm","room":"Leadership 2","type":"session","track":"AI Architects: Show my Workflow","status":"tentative","speakers":["Niels Rogge"]}]},{"name":"Nishant Gupta","role":"Staff Software Engineer and Researcher","company":"Meta","sessions":[{"title":"Operating Distributed Inference Systems at Scale","description":"Inference has rapidly become one of the most important infrastructure problems in modern computing. As AI systems evolve into autonomous agents with persistent memory, tool usage, and multi-step reasoning, traditional inference architectures struggle under growing demands for latency, throughput, cost efficiency, and reliability. In this talk, I’ll share lessons from building large-scale elastic compute and AI infrastructure systems powering production workloads. We’ll explore the modern inference stack and the architectural patterns emerging to support next-generation agentic AI systems. Topics include distributed inference architectures for large-scale AI systems, GPU scheduling and elastic compute for inference workloads, multi-tenant inference infrastructure, caching, batching, latency optimization strategies, reliability and fault isolation for inference systems, observability and control loops for AI serving platforms, balancing cost, throughput, and user experience, and why inference is becoming an infrastructure orchestration problem. Attendees will gain practical insights into designing scalable, resilient, and cost-efficient inference platforms for modern AI workloads.","day":"Day 4 — Session Day 3","time":"10:45am-11:05am","room":"Track 9","type":"session","track":"Inference","status":"tentative","speakers":["Nishant Gupta"]}]},{"name":"Pamela Fox","role":"Principal Cloud Advocate","company":"Microsoft","twitter":"https://x.com/pamelafox","sessions":[{"title":"The model swap workshop","description":"Frontier labs are releasing new models constantly, and it is hard to know when “better” is better enough to justify touching a working system. On top of that, “just swap the model” often turns into real work because providers expose different APIs and different expectations around tools and structured outputs. The model swap workshop is a hands-on bake-off across frontier LLMs. We will run the same scenarios using multiple models (OpenAI, Anthropic, Kimi, and more) and compare results side by side for agentic tool use, structured outputs, and multimodal tasks. Swapping models is not just changing a model name. In this workshop, you will actually do the swaps, including moving between OpenAI-style Responses APIs and Anthropic-style Messages APIs, then see what breaks and what needs to change in your prompts, tool definitions, and JSON strategies. We will finish by running a small eval suite so you can quantify tradeoffs instead of relying on vibes. We will provide the Microsoft Foundry environment for access to the models, no account needed.","day":"Day 1 — Workshop Day","time":"1:15pm-2:15pm","room":"Track 6","type":"sponsor","track":"Workshops Day 1","status":"tentative","speakers":["Pamela Fox"]}]},{"name":"Parth Asawa","role":"CS PhD student","company":"UC Berkeley","twitter":"https://x.com/pgasawa","sessions":[{"title":"Continual Learning Bench","day":"Day 3 — Session Day 2","time":"10:45am-11:05am","room":"Track 3","type":"session","track":"Memory & Continual Learning","status":"confirmed","speakers":["Parth Asawa"]}]},{"name":"Paul Bakaus","company":"Impeccable","sessions":[{"title":"The Dark Arts of Skill Engineering","day":"Day 1 — Workshop Day","time":"1:15pm-2:15pm","room":"Track 9","type":"sponsor","status":"tentative","speakers":["Paul Bakaus"]},{"title":"Design at the Speed of Adjectives","description":"Every design tool today operates at the wrong level of abstraction for AI-assisted engineering. Traditional tools give you padding sliders and color pickers, built for a world where designer and engineer are separate roles moving at separate speeds. Prompt-to-design tools one-shot a pretty landing page from a sentence, which is more dangerous because it looks like it's working. No serious design director hears a prompt and starts pushing pixels. The brief comes first. What's the emotional territory? What should this not feel like? Today's AI tools skip that discovery entirely. The result is output without intent. Technically competent, strategically empty. The right abstraction for a world where the designer is also the engineer lives between these extremes. Not pixels. Not prompts. Adjectives. \"Make it feel warmer.\" \"Strip it to its essence.\" \"Add tension.\" These are the controls a creative director actually thinks in. Drawing on lessons from building Impeccable, an open source design tool with 24 adjective-level commands like /bolder, /quieter, and /distill, I'll share what worked, what didn't, and how to apply this thinking to any AI interface where creative intent matters more than parameter control.","day":"Day 3 — Session Day 2","time":"2:25pm-2:45pm","room":"Track 6","type":"session","track":"AI Designers/Design Engineers","status":"tentative","speakers":["Paul Bakaus"]}]},{"name":"Paul Iusztin","role":"Senior AI Engineer; founder of Decoding AI Magazine","company":"Towards AI","sessions":[{"title":"What We Learned After One Year of Building Our Deep Research System","day":"Day 2 — Session Day 1","time":"3:20pm-3:40pm","room":"Track 3","type":"session","track":"Search & Retrieval","status":"tentative","speakers":["Paul Iusztin"]},{"title":"What We Learned After One Year of Building Our Deep Research System","day":"Day 2 — Session Day 1","time":"3:45pm-4:05pm","room":"Track 3","type":"session","track":"Search & Retrieval","status":"tentative","speakers":["Paul Iusztin"]}]},{"name":"Paula Rambles","twitter":"https://x.com/paularambles","sessions":[{"title":"Tolan: Voice-First AI Companion","day":"Day 2 — Session Day 1","time":"1:30pm-1:50pm","room":"Track 6","type":"session","track":"Voice & Realtime AI","status":"tentative","speakers":["Paula Rambles"]}]},{"name":"Philipp Schmid","role":"Staff Engineer","company":"Google DeepMind","twitter":"https://x.com/philschmid","sessions":[{"title":"Agents Without Code: How Skills, YAML, and Filesystems Replaced Python","description":"Six months ago, building an agent meant writing a Python class with a `while` loop, tool definitions in dicts, manual state management or writing custom python functions. Today, you define an agent in a YAML file, drop a `SKILL.md` into a folder, and deploy. This talk traces the arc from \"Agent in Python\" to \"Agent as filesystem\". You'll learn the same agent built three ways: the hard way (Jan 2025), the simple way (Oct 2025), and the zero-code way (today).","day":"Day 4 — Session Day 3","time":"3:45pm-4:05pm","room":"Main Stage","type":"session","track":"Harness Engineering","status":"tentative","speakers":["Philipp Schmid"]}]},{"name":"Pierluca D'Oro","role":"Founder","company":"Stealth (formerly Meta)","twitter":"https://x.com/proceduralia","sessions":[{"title":"Computer Use at the Edge of the Statistical Precipice","description":"Evaluating Computer Use Agents (CUAs) on interactive environments is fraught with methodological pitfalls that the field has yet to systematically address. We show that a 1MB replay script that blindly executes a recorded action sequence without ever observing the screen outperforms frontier models on prominent static benchmarks, and prove that its expected success rate is exactly equal to the source agent's pass@k in deterministic environments. We trace this and other failures to two root causes: non-principled environment design (static, unsandboxed, or unreliably verified environments) and non-principled evaluation methodology (naive aggregation and misuse of pass@k for stateful UI interactions). To address the first, we propose PRISM, five design principles for CUA environments and instantiate them in DigiWorld, a benchmark of 15 realistic sandboxed mobile applications able to evaluate agents in over 3.2 million verified unique configurations. To address the second, we develop an aggregation framework that correctly accounts for the nested structure of CUA benchmarks. All together, we show that principled environment design and rigorous evaluation methodology are not optional refinements but prerequisites for meaningful CUA research.","day":"Day 3 — Session Day 2","time":"10:45am-11:05am","room":"Track 7","type":"session","track":"Computer Use","status":"tentative","speakers":["Pierluca D'Oro"]}]},{"name":"Priyanka Phatak","role":"Engineering Leader","company":"Anthropic","sessions":[{"title":"Claude Managed Agents Workshop","description":"Build an agent with Claude Managed Agents","day":"Day 2 — Session Day 1","time":"10:45am-11:05am","room":"Track 4","type":"session","track":"Workshops Day 2","status":"tentative","speakers":["Priyanka Phatak"]},{"title":"Claude Managed Agents workshop","day":"Day 2 — Session Day 1","time":"11:10am-11:30am","room":"Track 4","type":"session","track":"Workshops Day 2","status":"tentative","speakers":["Priyanka Phatak"]},{"title":"Claude Managed Agents workshop","day":"Day 2 — Session Day 1","time":"11:40am-12:00pm","room":"Track 4","type":"session","track":"Workshops Day 2","status":"tentative","speakers":["Priyanka Phatak"]},{"title":"Claude Managed Agents workshop","day":"Day 2 — Session Day 1","time":"12:05pm-12:25pm","room":"Track 4","type":"session","track":"Workshops Day 2","status":"tentative","speakers":["Priyanka Phatak"]}]},{"name":"Prukalpa Sankar","company":"Atlan","sessions":[{"title":"Cut Through the Context Hype: 4 Layers Your Agent Is Missing","day":"Day 3 — Session Day 2","time":"3:45pm-4:05pm","room":"Track 8","type":"session","track":"Context Engineering","status":"tentative","speakers":["Prukalpa Sankar"]}]},{"name":"Qianru Lao","role":"Member of Technical Staff, Inference","company":"OpenAI","sessions":[{"title":"Routing LLM Inference in Production: From Engine Signals to Policy","description":"Production LLM apps need more than a fast model: they need an inference routing layer that can choose where each request should run as engines, capacity, latency, and geography cost change. This talk shares a generalized Inference Load Balancer (ILB) proxy/controller architecture. A low-latency proxy applies routing weights and request-path signals, while a controller computes source-cluster-to-engine weights from demand, capacity/performance profiles, replica state, and geography cost. We will cover the practical debugging patterns AI engineers need: reading engine signals, explaining why a request went to one backend instead of another, handling retries and load shedding, and keeping routing behavior observable without exposing OpenAI-specific internals or non-public metrics.","day":"Day 4 — Session Day 3","time":"11:10am-11:30am","room":"Track 9","type":"session","track":"Inference","status":"tentative","speakers":["Qianru Lao"]}]},{"name":"Rachna Srivastava","company":"California DFPI","sessions":[{"title":"Guardians of the State: How We Built an Air-Gapped AI Fortress for Consumer Data","day":"Day 3 — Session Day 2","time":"1:55pm-2:15pm","room":"Leadership 1","type":"session","track":"AI-Native Enterprises","status":"tentative","speakers":["Rachna Srivastava"]}]},{"name":"Ramana Siddanth Emani","role":"Data Scientist","company":"Auditoria AI","sessions":[{"title":"Your Finance Agent's Bottleneck Is You","description":"Most \"AI for Finance\" demos look great and almost none survive past pilot. If you've pushed an agent past one workflow, one tenant, or one Workday schema, you know the bottleneck isn't the model - it's the engineer behind the agent, who can't iterate fast enough to keep up with real AP data, real RBAC, and real query volume. What if you built your dev loop with the same primitives you're shipping to the finance team? In this talk, I'll show the subagent + skills + MCP stack - a production multi-agent system over AP, PO, vendor, and multi ERP systems, a LangGraph pattern that survives production, and the three failure modes that kill finance pilots before they ship.","day":"Day 4 — Session Day 3","time":"2:25pm-2:45pm","room":"Track 3","type":"session","track":"AI in Finance","status":"tentative","speakers":["Ramana Siddanth Emani"]}]},{"name":"Rania Khalaf","sessions":[{"title":"The Chief AI Officer: A framework for the emerging Swiss Army Knife of roles","day":"Day 3 — Session Day 2","time":"10:45am-11:05am","room":"Leadership 2","type":"session","track":"AI Architects: Tokenmaxxing","status":"tentative","speakers":["Rania Khalaf"]}]},{"name":"Rashi Agrawal","role":"Head of Agentic AI","company":"Hinge Health","sessions":[{"title":"Guardrails First: Engineering Member-Facing Health AI","description":"Everywhere else in the company, an AI pilot can reach production in weeks. For our member-facing clinical assistant, it can't, and that single constraint redesigned our entire architecture. This is a field report on building conversational AI in a regulated digital health setting, where \"move fast and break things\" isn't a culture choice. It's a liability. We'll get concrete about what changes when every output has to be clinically safe, auditable, and compliant: PHI is protected by architecture, not policy. Production and non-production are hard-isolated, dashboards are sanitized, and engineers outside the US never touch protected health information. Must-not-fail behavior never lives in a prompt. Emergency escalation and intent routing run as deterministic rules at the top of every conversation turn, before the model is consulted. If you can't afford to get something wrong, you don't leave it to a probabilistic system. Clinical safety is a continuous eval layer. ~30 LLM-as-judge evaluators score clinical accuracy, clinical safety, escalation routing, and recommendation relevance, continuously, not once. Every output is auditable. Each turn, tool call, and reasoning step is traced so outputs can be reviewed and meet regulated reporting obligations. The throughline: in regulated healthcare, compliance constraints aren't a tax you pay around the architecture. They become the architecture. We'll talk about why guardrails-first is the only way to ship member-facing health AI, and why \"painfully slow\" is sometimes exactly right. (This is non-diagnostic, member-facing AI. The talk is about engineering discipline under regulation, not medical claims.) Key takeaways - In regulated health AI, \"move fast\" is the wrong default. Design for deliberate, careful launches. - Must-not-fail behaviors belong in deterministic rules at the top of every turn, never in the prompt. - Protect PHI through architecture: isolate prod from non-prod, sanitize dashboards, restrict access by role and geography. - Make every output auditable. Trace each turn, tool call, and reasoning step so safety is reviewable, not assumed. - Treat clinical safety as a continuous LLM-as-judge layer, not a one-time gate.","day":"Day 4 — Session Day 3","time":"11:10am-11:30am","room":"Track 7","type":"session","track":"AI in Healthcare","status":"tentative","speakers":["Rashi Agrawal"]}]},{"name":"Remi Louf","sessions":[{"title":"Agent Frameworks Considered Harmful","day":"Day 4 — Session Day 3","time":"2:50pm-3:10pm","room":"Main Stage","type":"session","track":"Harness Engineering","status":"tentative","speakers":["Remi Louf"]}]},{"name":"Robert Brennan","company":"OpenHands","sessions":[{"title":"Sandboxes Aren't Optional: Runtime Isolation Patterns for Coding Agents at Scale","day":"Day 3 — Session Day 2","time":"11:40am-12:00pm","room":"Track 1","type":"session","track":"Sandbox & Platform Engineering","status":"tentative","speakers":["Robert Brennan"]}]},{"name":"Roberto Milev","company":"Navan","sessions":[{"title":"Agents Are Where Microservices Were in 2015. We're Making All the Same Mistakes.","day":"Day 3 — Session Day 2","time":"2:50pm-3:10pm","room":"Leadership 1","type":"session","track":"AI-Native Enterprises","status":"tentative","speakers":["Roberto Milev"]}]},{"name":"Rodrigo Coelho","company":"Edge & Node","sessions":[{"title":"Agent Spending Without Controls: The Missing Infrastructure Layer for AI Pa…","day":"Day 4 — Session Day 3","time":"1:30pm-1:50pm","room":"Track 2","type":"sponsor","track":"Agentic Commerce","status":"tentative","speakers":["Rodrigo Coelho"]}]},{"name":"Roland Gavrilescu","role":"Founder","company":"Introspection","twitter":"https://x.com/rolandgvc","sessions":[{"title":"Autoresearch in the wild","day":"Day 3 — Session Day 2","time":"11:40am-12:00pm","room":"Main Stage","type":"session","track":"Autoresearch","status":"tentative","speakers":["Roland Gavrilescu"]}]},{"name":"Ronak Malde","company":"Trajectory.ai","sessions":[{"title":"Scaling up Continual Learning","day":"Day 3 — Session Day 2","time":"11:10am-11:30am","room":"Track 3","type":"session","track":"Memory & Continual Learning","status":"tentative","speakers":["Ronak Malde"]}]},{"name":"Ross Taylor","role":"Co-founder & CEO","company":"General Reasoning","twitter":"https://x.com/rosstaylor90","sessions":[{"title":"General Reasoning for Long-Horizon Agent Models","description":"Long-horizon agent models, reasoning loops, and the data/eval stack needed to make them reliable.","day":"Day 2 — Session Day 1","time":"2:25pm-2:45pm","room":"Track 9","type":"session","track":"Data Quality","status":"tentative","speakers":["Ross Taylor"]}]},{"name":"Rudy Geronimo","company":"Stripe","sessions":[{"title":"Teaching agents to pay","day":"Day 4 — Session Day 3","time":"1:55pm-2:15pm","room":"Track 2","type":"sponsor","track":"Agentic Commerce","status":"tentative","speakers":["Rudy Geronimo"]},{"title":"Building safe payment infrastructure for the autonmous economy","day":"Day 4 — Session Day 3","time":"3:20pm-3:40pm","room":"Track 2","type":"sponsor","track":"Agentic Commerce","status":"tentative","speakers":["Rudy Geronimo"]}]},{"name":"Ryan Dahl","role":"Creator of Node.js & Deno; Deno Sandbox","company":"Deno","twitter":"https://x.com/rough__sea","sessions":[{"title":"The OS runtime personal agents need","description":"Why personal agents that run untrusted LLM code need a sandboxed OS/runtime model, not just a compute sandbox.","day":"Day 2 — Session Day 1","time":"11:10am-11:30am","room":"Track 1","type":"session","track":"Claws & Personal Agents","status":"confirmed","speakers":["Ryan Dahl"]}]},{"name":"Sai Krishna Rallabandi","role":"Director, Data Science","company":"Fidelity Investments","sessions":[{"title":"Wearing the Agent: Engineering a Family-and-Friends Personal Agent, from Group Chats to Glasses","description":"Judith is a personal AI agent that has run in daily production for a year, used by more than a dozen family and friends across WhatsApp group chats, Telegram, and Discord. This talk covers the engineering for a safe multi-tenant personal agent: permissioning, long-lived memory across FAISS + Neo4j + curated notes, scheduled subagents, and message-time guardrails for privacy, recipient safety, and prompt-injection defense. It then shows how the agent moves onto low-cost smart glasses, capturing visual memory, helping with navigation and in-store tasks, and maintaining conversational latency with on-device speech recognition, cloud reasoning, and a custom neural voice. Includes live demos plus practical takeaways on multi-user agent design, durable memory, defensive agent engineering, and wearable ambient interfaces.","day":"Day 4 — Session Day 3","time":"3:45pm-4:05pm","room":"Track 3","type":"session","track":"AI in Finance","status":"tentative","speakers":["Sai Krishna Rallabandi"]}]},{"name":"Sam Bhagwat","company":"Mastra","sessions":[{"title":"Every Harness Will Become A Claw","day":"Day 2 — Session Day 1","time":"3:45pm-4:05pm","room":"Track 1","type":"session","track":"Claws & Personal Agents","status":"tentative","speakers":["Sam Bhagwat"]}]},{"name":"Sam Lee","company":"NeoLabs","sessions":[{"title":"NeoLabs — Cognition","day":"Day 2 — Session Day 1","time":"11:40am-12:00pm","room":"Track 9","type":"session","track":"Data Quality","status":"tentative","speakers":["Deniz Birlikci","Sam Lee"]}]},{"name":"Samuel Colvin","sessions":[{"title":"Your agent needs a sandbox, not a desert","day":"Day 3 — Session Day 2","time":"12:05pm-12:25pm","room":"Track 1","type":"session","track":"Sandbox & Platform Engineering","status":"tentative","speakers":["Samuel Colvin"]}]},{"name":"Sandhya Subramani","role":"Senior Developer Advocate for Generative AI","company":"Amazon Web Services","sessions":[{"title":"Tell the Robot What You Want","description":"What if you could command a robot just by talking to it? This session introduces an open-source agentic AI framework that lets developers control physical sensors and actuators using natural language, by exposing hardware as programmable agent tools through a unified interface. The agent interprets the request, selects appropriate tools, and orchestrates execution. We explore a hybrid model where low-latency perception and actuation run locally on edge hardware, and higher-level reasoning and multi-step planning are delegated to cloud-based agents when needed. This preserves real-time responsiveness while enabling richer reasoning. A live robot demonstration anchors the session. Using the SO101 robotic arm powered by NVIDIA GR00T on Jetson hardware alongside HuggingFace LeRobot, attendees see how an instruction such as \"place the apple in the basket\" moves from conversation to perception to physical action.","day":"Day 3 — Session Day 2","time":"12:05pm-12:25pm","room":"Track 2","type":"sponsor","track":"Robotics & World Models","status":"tentative","speakers":["Sandhya Subramani"]}]},{"name":"Sara Hooker","role":"Co-founder","company":"Adaption Labs","twitter":"https://x.com/sarahookr","sessions":[{"title":"Adaption Labs — Gradient-Free Continual Learning","description":"Gradient-free continual learning for AI systems that adapt from real-world experience.","day":"Day 3 — Session Day 2","time":"12:05pm-12:25pm","room":"Track 3","type":"session","track":"Memory & Continual Learning","status":"tentative","speakers":["Sara Hooker"]}]},{"name":"Sarah Sanders","company":"PostHog","sessions":[{"title":"We let an AI agent execute Bash and lived to talk about it","day":"Day 4 — Session Day 3","time":"2:25pm-2:45pm","room":"Main Stage","type":"session","track":"Harness Engineering","status":"tentative","speakers":["Sarah Sanders"]}]},{"name":"Sebastian Fox","company":"Composo.ai","sessions":[{"title":"Inside 847 Production Clinical AI Notes","day":"Day 3 — Session Day 2","time":"1:30pm-1:50pm","room":"Track 5","type":"sponsor","track":"Evals","status":"tentative","speakers":["Sebastian Fox"]}]},{"name":"Shawn Chan","role":"Vice President","company":"China Resources Holdings","sessions":[{"title":"Build for the Memo, Not the Demo — Notes from 200 Investment Committees","description":"By the end of this talk you will have a buyer-side specification for AI investment agents, the exact artifacts, evidence formats, and trust gates a senior finance team will require before letting an AI system touch a $100M+ capital allocation decision. Drawn from fifteen years and roughly 200 investment committees at CK Hutchison (A.S. Watson Group) and China Resources Holdings, on the side of the table the AI engineering audience almost never hears from. Most enterprise AI in finance is still being built by engineers who have never sat in an investment committee. I have spent fifteen years on the other side of that demo, cross-border M&A, IPO execution and strategic investment, as a buyer on deals including Oatly (Series B through Nasdaq IPO), Airbnb (Series F), SenseTime, Moore Threads, Leapmotor and EVE Energy, and on the A.S. Watson tri-market IPO and Temasek's strategic stake. I have watched analyst memos get torn apart, and signed off on decisions where being wrong meant being wrong by nine figures. From that seat, almost every AI finance demo I have seen has the same problem: it optimizes for the demo, not for the memo. This talk walks through the specific failure modes that kill AI agents at the IC door: Source hierarchy is not retrieval. A footnote in an audited 10-K outweighs a sell-side note, which outweighs a transcript, which outweighs an internal email. Most RAG systems flatten this. Numerical consistency is non-negotiable. A memo that says \"revenue grew 18%\" in paragraph one and \"17.4%\" in the sensitivity table is dead on arrival. Contradiction is a feature. Real diligence surfaces conflicts between sources; AI agents tend to silently resolve them. Every assumption must be separable from every fact. Investment committees do not approve assumptions hidden inside prose. Audit trail is the deliverable. If a regulator, an auditor, or a board member cannot trace a claim back to evidence in under thirty seconds, the system is unusable. Accountability cannot be delegated to a model. Someone has to sign the memo. The architecture has to reflect that. The session closes with a concrete buyer-side specification, what an AI investment agent must produce, in what form, with what evidence, before a senior finance team will let it touch a live deal. Not a framework slide.","day":"Day 4 — Session Day 3","time":"11:10am-11:30am","room":"Track 3","type":"session","track":"AI in Finance","status":"tentative","speakers":["Shawn Chan"]}]},{"name":"Shawn Wang","role":"Founder & Editor","company":"Latent Space","twitter":"https://x.com/swyx","sessions":[{"title":"swyx keynote and snyk track intro","day":"Day 2 — Session Day 1","time":"9:00am-9:10am","room":"Main Stage","type":"keynote","track":"Software Factories","status":"tentative","speakers":["Shawn Wang"]},{"title":"Data Agents","day":"Day 2 — Session Day 1","time":"11:40am-12:00pm","room":"Leadership 2","type":"session","track":"AI Architects: Show my Workflow","status":"tentative","speakers":["Shawn Wang"]}]},{"name":"Shlok Khemani","role":"Writer & Programmer","company":"Independent","twitter":"https://x.com/shloked","sessions":[{"title":"Shlok Khemani — Reverse-Engineering AI Memory","description":"What we can learn about memory systems by probing the behavior of ChatGPT and Claude.","day":"Day 3 — Session Day 2","time":"3:20pm-3:40pm","room":"Track 3","type":"session","track":"Memory & Continual Learning","status":"tentative","speakers":["Shlok Khemani"]}]},{"name":"Shu Fang","company":"Two Sigma","sessions":[{"title":"Tethered: Our Agents Are Us","day":"Day 2 — Session Day 1","time":"12:05pm-12:25pm","room":"Track 1","type":"session","track":"Claws & Personal Agents","status":"tentative","speakers":["Shu Fang"]}]},{"name":"Sourabh Shirhatti","role":"Product Manager, Developer Platform Team","company":"Uber","sessions":[{"title":"How Uber Built AI Agents That Save 21,000 Developer Hours with LangGraph","description":"Uber Developer Platform team on building AI agents that save 21,000 developer hours with LangGraph; AutoCover, Validate, and the agent stack around LangGraph, LangChain, and LangFX.","day":"Day 2 — Session Day 1","time":"3:45pm-4:05pm","room":"Leadership 1","type":"session","track":"AI-Native Enterprises","status":"tentative","speakers":["Matas Rastenis","Sourabh Shirhatti"]}]},{"name":"Standard Intelligence","company":"SI.inc","sessions":[{"title":"SI.inc FDM1","day":"Day 2 — Session Day 1","time":"12:05pm-12:25pm","room":"Track 2","type":"sponsor","track":"Vision & OCR","status":"tentative","speakers":["Standard Intelligence"]}]},{"name":"Stephen Balaban","role":"Co-founder / CTO","company":"Lambda","twitter":"https://x.com/stephenbalaban","sessions":[{"title":"Scaling AI systems: where theory meets constraint","day":"Day 4 — Session Day 3","time":"2:25pm-2:45pm","room":"Leadership 2","type":"session","track":"Inference","status":"tentative","speakers":["Zach Bratun-Glennon","Stephen Balaban"]}]},{"name":"Sumanyu Sharma","role":"Founder & CEO","company":"Hamming AI","sessions":[{"title":"I Monitored Crime Audio. Voice Agents Scare Me More.","description":"This talk reframes bad voice-agent calls as incident scenes and introduces a voice-agent forensics loop spanning transcript, waveform, latency waterfall, interruption points, ASR uncertainty, tool traces, system-of-record state, and outcomes. It focuses on monitoring, regression, and release-discipline for production voice systems.","day":"Day 2 — Session Day 1","time":"2:25pm-2:45pm","room":"Track 6","type":"session","track":"Voice & Realtime AI","status":"tentative","speakers":["Sumanyu Sharma"]}]},{"name":"Tariq Shaukat","role":"Chief Executive Officer","company":"Sonar","twitter":"https://x.com/tariqshaukat","sessions":[{"title":"Sonar keynote — Tariq Shaukat","description":"TBD — keynote from Sonar's CEO.","day":"Day 3 — Session Day 2","time":"9:30am-9:50am","room":"Main Stage","type":"keynote","track":"Coding Agents & Software Factories","status":"tentative","speakers":["Tariq Shaukat"]}]},{"name":"Tejas Bhakta","company":"MorphLLM","sessions":[{"title":"Everything is Models","day":"Day 3 — Session Day 2","time":"1:55pm-2:15pm","room":"Track 9","type":"session","track":"Posttraining & Midtraining","status":"tentative","speakers":["Tejas Bhakta"]}]},{"name":"Theo Browne","company":"t3.gg","twitter":"https://x.com/theo","sessions":[{"title":"Closing Keynote — Theo Browne","day":"Day 4 — Session Day 3","time":"4:30pm-4:50pm","room":"Main Stage","type":"keynote","track":"Main Stage","status":"confirmed","speakers":["Theo Browne"]}]},{"name":"Thor 雷神 Schaeff","role":"Developer Experience Engineer","company":"Google DeepMind","sessions":[{"title":"Build realtime multimodal agents with Gemini Live","description":"The Gemini Live API is incredible versatile when it comes to building realtime AI experiences. From live translation across 2000 different language pairs to building realtime multimodal agents that can work across text, audio, and vision. This workshop gets you from zero to fully conversational agent in a matter of hours.","day":"Day 3 — Session Day 2","time":"10:45am-11:05am","room":"Track 4","type":"session","track":"Workshops Day 2","status":"tentative","speakers":["Thor 雷神 Schaeff"]},{"title":"Build realtime multimodal agents with Gemini Live (continued 2)","description":"The Gemini Live API is incredible versatile when it comes to building realtime AI experiences. From live translation across 2000 different language pairs to building realtime multimodal agents that can work across text, audio, and vision. 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As AI systems move into production, tokens have become a primary operational resource alongside CPU, memory, and storage, yet few teams manage them with equivalent systems rigor. Most architectures lack the granular visibility required to attribute token spend to specific users, agents, or workflows, and they lack mechanisms to terminate a runaway loop before it triggers a financial incident. This session treats token consumption as a first class systems problem, demonstrating how to make it observable, attributable, and enforceable across complex agent workflows. The presentation covers practical engineering patterns for instrumenting token usage at every model call and tool invocation, attributing costs down to specific users or business operations, surfacing expensive execution paths, and enforcing runtime budgets, quotas, and circuit breakers to halt runaway behavior in real time. 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Walk away from this talk inspired to help build the next evolution of tools for musicians and live performances. We will touch on how to build with tools such as classic DSP, JUCE, on device TTS, CoreML, WhisperX, CoreMIDI and more! 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