How you know it’s working
You don’t need a full “AI ops” department on day one. You need a short list of signals that tell you whether the system is healthy — and a few failure shapes you can recognize quickly.
Healthy signals
- Task success rate — for defined jobs, did the user get the right outcome without rework?
- Latency p95 — not average: tail latency is what people feel.
- Cost per successful task — ties quality to money (see what it costs).
- Tool error rate — timeouts, 4xx/5xx from APIs, rate limits.
- Escalations — how often humans must intervene; should trend down as prompts and tools improve.
Common failure shapes
| Symptom | Likely cause | First fix |
|---|---|---|
| Right sometimes, wildly wrong sometimes | vague instructions / missing eval criteria | tighten the spec; add examples |
| Slow and expensive | everything routed to a huge model | route by tier |
| Forgets context mid-run | long tool chains without summarization | summarize + checkpoint state |
| “Done” but nothing happened | tool silently failed | surface errors to the model; retry policy |
| Stuck repeating | missing stop condition | cap steps; add a critic/reviewer step |
What “good visibility” means
If you run more than one agent or long chains, you want tasks, spend, and an audit trail — the difference between debugging with data vs. guessing in chat transcripts.
Minimal stack idea
- Structured logs for each agent step (what tool, what args, what result code).
- Traces if you can (OpenTelemetry is common) — nice-to-have, not a blocker.
- Weekly review of top failures — cheapest “continuous improvement” loop.
Related
Last updated on