Runtime Feedback Guides
Long-form guides on shipping AI-generated code you can trust: the case for a runtime feedback loop, the wiring to build one, and the new failure classes that make it non-optional. 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 →The Engineer’s Guide to Invisible LLM Failures
LLM APIs fail in ways that return HTTP 200: silent content filters, body-level refusals, mid-stream 529s, plus moderation verdicts dressed as validation errors. The full taxonomy, a signature table covering every error string, and the observability posture that catches what status codes can’t.
Read the guide →Skip ahead: Dstl8 is the runtime feedback platform these guides describe. Free 14-day trial, no credit card. brew install control-theory/dstl8/dstl8 && dstl8 setup