Ahmad Awais is the founder and chief executive of Command Code and Langbase, companies addressing complementary problems in AI engineering: building dependable agents and teaching them how their users actually write software.
His career began in open-source web development, with contributions to WordPress, Node.js, React, and Next.js. He created the Shades of Purple editor theme with Maedah Batool and developed create-node-cli, an open-source generator for Node.js command-line applications. His work also included developer education through NodeCLI and VSCode.pro and contributions to open-source software associated with NASA’s Ingenuity Mars helicopter.
Awais was principal developer advocate at Cloudinary before becoming a vice president leading developer relations and developer experience at RapidAPI. After experimenting with GPT-3 coding tools beginning in 2020, he left RapidAPI to found Langbase. Its infrastructure supplies agent components such as document parsing, retrieval, persistent threads, memory, and workflow execution. Chai, subsequently renamed Command.new, extended that foundation into natural-language agent creation; Command Code applies it to personalized software development.
- Composable AI primitives: Awais advocates assembling agents from focused services for memory, retrieval, tools, and orchestration, with application behavior expressed in ordinary JavaScript or TypeScript. His argument against heavyweight agent frameworks emphasizes inspectable workflows that remain adaptable as models improve. Practical patterns include specialist-model routing, parallel workers, and evaluator-feedback loops.
- Coding agents with acquired taste: Awais distinguishes code that merely works from code reflecting a developer’s preferences for architecture, dependencies, testing, and maintainability. Command Code learns from instructions, accepted suggestions, rejected outputs, and subsequent edits. For his own command-line projects, that means selecting TypeScript, pnpm, tsup, Commander, and Vitest; separating commands into individual files; and applying preferred version-flag conventions automatically.
- Continuous preference learning: His approach combines language models with reinforcement learning and structured neuro-symbolic reasoning, using inspectable taste files to represent preferences that can change over time. As he demonstrates with personalized coding agents, those learned conventions could eventually be shared across teams, projects, and specialties, allowing coding assistants to absorb engineering judgment without requiring developers to document every decision explicitly.
