Internal reference systems and prototypes that show how we build production AI — architecture, what gets logged, where evals run, and the failure modes we design for. Clearly labelled; not client case studies.
A control layer for agent runs, costs, evals, approvals, and failures.
Ingestion to hybrid retrieval to reranking to cited answers.
A bounded LangGraph agent with explicit state, tools, and approvals.
Repo instructions, skills, MCP, and a GitHub Actions review loop.
A TypeScript MCP server exposing internal tools behind auth.
Routes tasks across OpenAI, Claude, Gemini, and Grok with fallback and cost control.
Build logs, agentic engineering decisions, agent failures, evals, and what survives real users. Sent weekly, never more.