How to Evaluate Agentic Workflows
Understand that evaluating agentic workflows requires tracking goal alignment across full decision chains.
We'll cover the following...
- Why should you bundle support failures instead of reviewing them one at a time?
- How do you turn a support-failure bundle into a testable hypothesis?
- How do you turn real, messy support conversations into clean, reproducible tests?
- How do you choose whether a fix belongs in prompting, a tool definition, or workflow logic?
- How do you run micro-experiments and fold successful fixes into the full evaluation loop?
- What’s next?
Evaluation is valuable when it leads to measurable, repeatable improvements in a support assistant’s behavior. Logs, traces, and error dashboards can surface failures or anomalies, but they do not resolve them on their own. Quality improves through a tight feedback loop around failures. This includes identifying recurring patterns, forming testable hypotheses, validating small changes, and encoding the fixed behavior in the evaluation suite to prevent regressions.
At this point, the assistant is no longer just chatting; it has become a full-fledged conversation. It is acting by choosing tools, making decisions, escalating when needed, and driving multi-step workflows. That shift is what makes the system agentic rather than purely conversational.
This lesson introduces the improvement loop using concrete examples from support workflows such as order lookups, subscription changes, refund checks, and device troubleshooting. Instead of treating every failure as a one-off debugging task, you learn how to group repeated problems, create minimal reproduction tests from messy conversations, and run small experiments that directly reduce the assistant’s first-failure rate.
Why should you bundle support failures instead of reviewing them one at a time?
Individual traces can vary significantly. One user may be canceling a subscription, another is troubleshooting a device, and a third is checking the shipping status. Despite this surface-level variation, many failures stem from the same underlying system issue. For example, dozens of traces may show the assistant invoking a refund tool before verifying whether the customer is within the store’s return window. In other cases, the assistant may repeatedly mishandle partial order numbers such as “#482–” because it ...