Ahmad Awais

Developing Taste in Coding Agents: Applied Meta Neuro-Symbolic RL — Ahmad Awais, CommandCode

Nov 24, 2025 · 20:52

Ahmad Awais, founder of Langbase, introduces CommandCode, a coding agent that learns a developer's personal taste and preferences to generate code that feels like their own. Built on Langbase's infrastructure (deploying over 350K agents and 1.2B monthly runs), CommandCode uses a Meta Neuro-Symbolic architecture with reinforcement learning to continuously capture a programmer's invisible architecture of choices—such as preferring TypeScript, PNPM, TSUP, and Commander over other tools—without requiring explicit rule files. Awais demonstrates how CommandCode automatically applies these learned preferences when building a CLI, contrasting it with Claude Code which generates generic output. He argues that taste models represent the next frontier, enabling agents to evolve with the developer and even share tastes across teams or borrow preferences from other developers like design engineers.

Why the Best AI Agents Are Built Without Frameworks (Primitives over Frameworks) — Ahmad Awais, CHAI

Jun 3, 2025 · 27:06

Ahmad Awais, founder of LangBase/CHAI, argues that production AI agents like Perplexity, Cursor, v0, Lovable, Bolt, and CHAI itself should be built using AI primitives rather than frameworks. He demonstrates live coding a 'Chat with PDF' agent using LangBase primitives (memory, parser, chunker, threads). Awais presents eight agent architectures—augmented LLM, prompt chaining, agent router, parallel agents, orchestrator-worker, evaluator-optimizer, tool-calling, and memory-based—all built with plain code and no framework. He claims frameworks are bloated and slow, while primitives offer flexibility and automatic scaling (e.g., memory storing terabytes). Awais shows how to compose multiple LLMs (Gemini, DeepSeek Llama, Claude) in a router agent, and notes that his team has built agents for OCR, image analysis, and deep research using primitives and external tools like Mistral OCR.