Runtime Feedback Guides
Long-form guides on shipping AI-generated code you can trust: the case for a runtime feedback loop, and the wiring to build one. Written for the people doing the work, refreshed as the data changes.
How to Trust What Your Agents Ship
Why AI-generated code fails in production after passing every pre-deploy gate, what your runtime platforms can’t tell you, and the four-stage architecture (distill, enrich, explain, remember) that closes the runtime feedback loop. Grounded in the 2026 survey data on the AI code trust wall.
Read the guide →The Agentic Engineer’s Guide to Runtime Feedback
Claude Code, Cursor, Codex, and Copilot all close the loop from prompt to commit; none can see what the code did after deploy. What runtime context an agent actually needs, how to wire it in through MCP, and the verified-ship workflow that replaces deploy-and-hope.
Read the guide →What Production Teaches Your Agents
Runtime feedback as an improvement engine: how production signal informs test coverage, feature priority, and the next prompt, and how a knowledge graph of every triage compounds into team-level velocity.
Skip ahead: Dstl8 is the runtime feedback platform both guides describe. Free 14-day trial, no credit card. brew install control-theory/dstl8/dstl8 && dstl8 setup