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Cognitive Cell: Building a Route-Select-Render Control Layer for Workflow AI
Learn to build a control layer for AI workflows, separating routing, selection, and rendering. Discover practical lessons on evaluation, baselines, and packaging research for reuse.
I built Cognitive Cell, a public Python package and HTTP sidecar that sits in front of an LLM and decides whether an input should be recorded, clarified, analyzed, planned, answered directly, or escalated. The accepted v9 stack is router-v4 → selector-v5 → finalizer-v9. I will show the live package, CLI, HTTP sidecar, architecture, traces, evaluation files, and the messy journey from a research prototype to a PyPI package.
Live demo elements:
- Python package: pip install “cognitive-cell[server]”
- CLI: cognitive-cell –event-json …
- HTTP sidecar: cognitive_cell.
server.app
- /health endpoint
- /v1/sidecar endpoint
- trace output
- evaluation CSV/JSONL artifacts
- ablation results