Connecting Metrics, Traces, and Logs for AI Coding Agents in Production
Al_Grigor · x · 2026-08-14
Passing tests and deploying code doesn't guarantee an app works in production. To determine if it actually operates successfully, developers need three connected elements: a metric showing a spike in error rates, a trace identifying the failed service, and a log explaining why the failure occurred.
In the upcoming AI Dev Tools Zoomcamp Workshop 4, the author will demonstrate how to instrument a single endpoint so that metrics, traces, and logs all capture the same failed request. Furthermore, he will introduce a coding agent that collects this evidence and proposes a diagnosis autonomously, without needing direct production access.
More from coding & agent
- Grok 4.6 Matches Claude Opus 5 in Data Mining Workflows at Lower Cost — kevinnbass · 2026-08-14
- GLM-5.3 Review: Big Improvement Over 5.2, Gap to Fable Narrows to 4% — cedric_chee · 2026-08-14
- Production Learnings from Building Agent Harnessing — trawasthi_ai · 2026-08-14
- RLBotics: Lightweight GPU-Accelerated RL Framework for Isaac Lab — rsasaki0109 · 2026-08-14
- Building an Agentic Software Factory: Breaking Review Bottlenecks with AI — Pavan_Belagatti · 2026-08-14
- Testing Claude Frontend Design Plugin: Noticeable UI Quality Boost — jordiponsdotme · 2026-08-14