You cannot safely improve an agent you cannot inspect.
Why it matters
Agent failures are often cross-cutting: prompt, retrieval, memory, tool result, model routing, approval flow, or business logic. Evals tell you whether quality changed. Observability tells you why. Production systems need both from the start.
Build this
- Offline eval suites for known tasks, edge cases, regressions, and adversarial inputs.
- Online quality signals from user outcomes, reviewer labels, retries, edits, and abandonment.
- Traces that link model calls, context blocks, tool calls, workflow states, approvals, and artifacts.
- Dashboards for quality, latency, errors, spend, refusal rate, tool failure rate, and escalation rate.
Watch for
- Demo examples used as evals without expected outputs or scoring rules.
- Logs that record final text but not context, tool calls, or model versions.
- Quality measured only by thumbs-up feedback from users who bothered to click.
- Alerts for infrastructure errors but not quality regressions.
Proof it works
- A prompt, model, retrieval, or tool change runs against a baseline before release.
- A bad production answer can be traced to the exact context and tool evidence used.
- Human review labels feed back into eval cases or product metrics.
Implementation checklist
01
Start with task-specific evals instead of one generic quality score.
02
Instrument the full request path before adding more agent autonomy.
03
Store traces with enough IDs to connect product events and backend logs.
04
Promote real incidents into regression tests.