Debug by Tracing Symptoms Back to the Failing Step
Explore methods for diagnosing AI workflow failures by systematically tracing symptoms like wrong routing, invalid JSON, or unsupported retrievals back to the specific workflow step causing the issue. Learn how to use run histories, validation reports, routing decisions, and retrieval logs as evidence to pinpoint problems and implement preventative controls such as validation gates and retry limits. This lesson equips you to debug workflows reliably and design robust failure handling mechanisms.
A support ticket says, Please update my credit card and resend the invoice, and the workflow routes it to technical escalation, skipping the billing playbook. The observable symptom is in the run history, where routing.next_action becomes escalate and the ticket lands in the wrong queue even though the intake text is clearly billing.
The fix does not start by rewriting prompts. It starts by treating the run log as the single source of truth, then walking backward from the first wrong output to the step that produced it. This lesson works through that method against three symptoms that can each show up in this workflow: a wrong route, invalid JSON, and a draft that cites policy no retrieved chunk supports.
One debugging loop across three symptoms
With a multi-step workflow, a symptom is only useful if it can be pinned to a specific step output. For the support workflow intake → triage call → validation gate → routing → retrieval (optional) → draft generation → approval gate → side effects (notifications/logging), each boundary produces evidence that can be inspected without guessing.
Before changing anything, we pull the same evidence types every time.
Captured trigger payload, meaning the exact intake text and metadata.
The LLM ...