AI observability for coding agents (ANSWERED)
AI observability extends classic APM.
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Observability and evaluation for coding agents — traces, cost/quality metrics, alerts, and private-repo bakeoffs.
You cannot scale Claude Code, Cursor, or Codex CLI without observability. Interviews ask what to trace (LLM calls, tools, diffs), which metrics matter (success rate, $/merged PR, revert rate, security blocks), and how to continuously evaluate agents on private codebases.
AI observability sits at the intersection of production AI, platform engineering, and eval engineering. Candidates who only talk about token dashboards miss the point — quality, cost, and security must be observed together.
This guide covers production telemetry and CI evaluation suites used to compare coding agents beyond anecdotes.
ObservabilityEvaluationCoding AgentsMetrics
Questions in this guide
Deep explanations with architecture diagrams for every question below.
Evaluating coding agent quality in CI (EXPLAINED)
Treat agent models/prompts/tools like dependencies.
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