Johnny Montoya

Executable Skills: Teaching an Agent How Your Company Works

The problem, in YC's words (February 2026): "Every company has scattered know-how — in people's heads, in old emails, in Slack threads, in support tickets, and in databases. The company works because humans vaguely remember where that knowledge is. But AI agents do not operate that way." The solution is a Company Brain: a system that extracts knowledge from all those fragmented sources, structures it, keeps it up to date, and turns it into an executable skills archive for AI. This workshop builds, in two hours, a minimal but real Company Brain with pure Python. It is not theory: the code comes from Centinela, a platform already in production serving agricultural operations in Colombia and beverage distributors in Bolivia, and combines the same pieces that at LaHaus have allowed me to bring voice agents to 2.5k+ calls/month with a real SLA. Stack we will touch (all Python or accessible from Python): - Chainlit ≥ 2.9 as conversational interface and thread/step data layer - deepagents (0.4.x) as orchestrator for stateful agentic loops - langchain-anthropic + fallback to Gemini via langchain-google-genai - e2b-code-interpreter as a real sandbox (not mock) to run Python with auditable side effects - Supabase (Postgres + RLS) as multi-tenant control plane - asyncpg for a conversation persistence layer on PostgreSQL - MCP (Model Context Protocol) via langchain-mcp-adapters to plug in external tools