How to Test and Evaluate Agentic AI Workflows
Agentic workflow evaluation must test the complete trajectory: evidence, reasoning steps, routing, tool calls, approvals, policy compliance, recovery and business outcome.
Translate the purpose into measurable claims
State what the workflow should complete, for which cases, within which constraints and with what acceptable consequence. Separate quality, safety, security, service and business claims.
Build representative and difficult cases
Use historical cases across common routes and meaningful subgroups. Add missing evidence, conflicting sources, unusual combinations, policy changes, tool failures and adversarial instructions. Preserve a holdout set for release decisions.
Evaluate each step and the full trajectory
Measure retrieval relevance, extraction, routing, tool selection, argument validity, approval use and final outcome. A workflow may reach the correct answer through an unsafe path or fail despite every component looking acceptable in isolation.
Test repeated runs and boundary conditions
Run important cases several times to identify unstable behaviour. Test maximum volumes, long contexts, rate limits, timeouts, expired credentials and unavailable systems.
Red-team the action surface
Test prompt injection, poisoned knowledge, permission escalation, tool misuse, data leakage and attempts to bypass approval. Verify that external content cannot redefine the workflow's instructions or authority.
Compare with baselines
Compare the agentic design with the current process, a simpler workflow and human performance where appropriate. Report cost, latency, error, rework, escalation and business outcomes rather than one accuracy score.
Use production traces to improve the test set
Turn real failures, near misses, overrides and novel cases into regression tests. Re-run affected evaluations whenever prompts, models, rules, tools, integrations or knowledge sources change.