ai-coding-agents-observability-evals

# AI Coding Agents Observability And Evals Use this skill to design or review the feedback loop around a coding-agent runtime: traces, replayable transcripts, eval packs, regression gates, tool-call grading, latency and cost accounting, and production failure triage. This skill covers how you operate a coding-agent product after the core runtime exists. It does not replace the runtime skills themselves. ## ASCII Flow ```text agent session | v trace events prompts + model turns + tool calls + permissions + file diffs + costs | v replayable transcript stable IDs + redaction + source/runtime correlation | v eval pack golden tasks + graders + regression gates + cost/latency budgets | v release decision pass | investigate | rollback | update eval coverage ``` ## Quick Reference | Question | Read | Outcome | |----------|------|---------| | What should the trace and telemetry model include? | [`references/trace-and-telemetry-model.md`](references/trace-and-telemetry-model.md) | Durable trace schema, session correlation, event stages, and replay boundaries | | How should evals, regressions, and cost controls work? | [`references/evals-regression-and-cost-ops.md`](references/evals-regression-and-cost-ops.md) | Golden tasks, scorecards, failure grading, and cost-aware release gates | | How do I use the eval/trace substrate to improve the harness itself? | [`references/harness-self-evolution.md`](references/harness-self-evolution.md) | Closed-loop harness e...