What an evaluation suite for an agentic workflow should contain

An agentic workflow can retrieve context, choose tools, draft actions, and request approval. That means evaluation has to cover more than answer quality. If you only spot-check a few happy-path prompts, you will learn too late where the system breaks.

Start by defining expected behavior in plain language. What should the workflow do with complete inputs? What should it do when evidence is missing? When should it escalate instead of acting? Those statements become the backbone of the suite.

Next, build a failure taxonomy. Common categories include incorrect classification, weak grounding, wrong tool selection, unsafe action proposals, missed escalations, and brittle handling of incomplete data. Every test case should map to one or more of those categories.

The dataset should mix representative production-like cases, known edge cases, and adversarial or misuse cases where relevant. Synthetic examples help, but they are not a substitute for anonymized or sanitized examples drawn from real operating conditions.

Release thresholds belong in the plan before launch. Decide what pass rate, escalation quality, and critical-failure rate are acceptable. Without thresholds, every demo looks good enough and every regression becomes a debate.

After launch, keep the suite as a regression gate for prompt, model, and tool changes. Evaluation is not a one-time launch checklist. It is part of operating the workflow.