A Checklist for Workflow Observability
Five questions to ask before you ship an AI workflow to production.
Meghan Chen
Team Member
Shipping AI workflows is no longer just about correct prompts—it is about ensuring every execution can be inspected, replayed, and trusted. This checklist captures the signals we monitor for every production deployment inside AGNT5.
1. Do you have traceability across steps?
Each workflow step should emit structured events that tie back to the triggering execution. We rely on AGNT5’s journal to stitch together durable state, tool calls, and human approvals into a single timeline.
2. Can you explain failures quickly?
When a workflow escalates to an operator, you need the why. Capture runtime metadata, input payloads, and the reasoning tokens returned by your models. Pair this with deterministic retries so you can replay safely.
3. Are guardrails observable, not just enforced?
Guardrails that run silently might block progress without surfacing the reason. Log both the guardrail configuration and the evaluation scores produced during execution.
4. What does “healthy” look like day to day?
Define baselines for latency, token usage, and human-in-the-loop interventions. Once you know the expected shape of your workload, anomaly detection becomes straightforward.
5. How do you close the feedback loop?
Observability is only useful if it feeds back into roadmap decisions. Schedule time to review metrics, tag interesting executions, and translate findings into improved prompts, tools, or routing logic.
If you want a deeper walkthrough, check out the observability guide in our docs. We will share more dashboards and runbooks in future posts.