Make it observable
Agentic systems are easier to run when you can see what they are doing. Emit structured events for inputs, tool calls, decisions, outputs, and errors. Add trace IDs so you can replay a session end‑to‑end. Include prompts and tool parameters in the trace when possible, redacting sensitive fields. Observability is what turns a confusing failure into a quick fix.
Keep a living scorecard
Track success rate, override rate, latency, and top failure reasons. Review weekly. Trends matter more than a single number. When overrides drop and latency holds, you are improving. Split metrics by workflow and by customer segment so you notice drift where it starts, not after it spreads.
Version prompts and policies
Treat prompts, schemas, and policies as versioned artifacts. Record which version was used for each run. When behavior shifts, you need to know what changed. Store versions in your repo or a dedicated registry and require a rollout note for each change. A simple changelog saves hours during incidents.
Budget the retries
Retries rescue flaky runs, but they cost time and money. Set hard limits and use different strategies: quick retry for transient errors, guided retry when validation fails, human review when confidence is low. Track why retries happen and fix root causes, especially slow or unreliable tools that create backpressure.
Build a clean rollback
Feature flags per customer, per workflow, and sometimes per action. Roll out changes to a small cohort first. If something drifts, roll back in seconds. Practice rollbacks in a staging environment so the team is confident and fast when it matters.
Train the system with real misses
Sample failures, label the cause, and update tools or policies before you chase models. Most gains come from better inputs and clearer contracts, not from swapping models. Keep a small backlog of “quality fixes” and ship one or two each week; the compounding effect is large.
Bottom line
Production is about rhythm: observe, review, adjust, and ship small fixes. Boring systems make outsized impact. Your users will notice reliability more than novelty.
Implementation checklist
- Emit structured traces with IDs and timestamps
- Maintain a weekly scorecard with core metrics
- Version prompts, schemas, and policies
- Define retry and escalation strategies
- Use feature flags and staged rollouts
- Review failure samples and close the loop
- Document runbooks for common incidents and drills
Metrics to watch
- Success and override rates by workflow
- p95 latency and cost per run
- Incidents per week and time to rollback
- Share of runs on latest policy/prompt versions
- Retry rate and top retry causes
Common pitfalls to avoid
- No trace data until something breaks
- Infinite retries that hide deeper issues
- Big‑bang changes without flags
- Chasing model upgrades before fixing inputs
- Unowned metrics without a clear weekly review
