How to Design Realistic Evaluation Scenarios for AI Agents

Choose a workflow that matters. “Answer a question” is usually too vague. “Decide whether this invoice can be paid,” “route an incident,” or “prepare a patient scheduling recommendation” gives you a real outcome, real constraints, and real stakeholders.

Interview the people who perform the work. Ask what information they need, what they check manually, what exceptions cause escalation, and what would make them distrust the system. These details become scenario fields rather than tribal knowledge.

A scenario should include the user request, relevant records, available tools, agent roles, policies, state, time zone, and expected output contract. If the production workflow can experience missing data, include missing data. If it depends on a cutoff time, make the clock explicit.

Separate facts from assumptions. A field marked “unknown” should not be silently replaced by a plausible value. This is one of the easiest ways to expose hallucinated dependencies and unsafe confidence.

Avoid success criteria such as “be helpful.” Write what a reviewer or evaluator can observe: “includes the incident ID,” “checks the backup timestamp,” “does not restart production without approval,” and “states the next safe action when evidence is missing.”

Include negative assertions. What must never happen? Which tool may not be called? Which field must be redacted? Negative criteria often carry more risk value than another happy-path example.

A realistic suite varies the facts that matter: a missing approval, a duplicate record, a conflicting policy, a stale retrieval result, a tool timeout, a user with reduced permission, or a request that changes halfway through the workflow.

Use pairwise thinking instead of trying to enumerate every combination. Identify the factors that interact—permission and tool type, urgency and approval, memory and date—and create scenarios around those intersections.

Capture the intermediate plan, handoffs, tool calls, state changes, and final output. A scenario should specify which steps are required, which are optional, and which orderings are invalid.

This catches lucky-path behaviour. A workflow may reach the right answer after querying an unauthorized source or skipping the required check. The final text is correct; the trajectory is not.

Tag each scenario by business domain, failure mode, severity, and owner. Record why it exists and what incident or requirement it represents. When it fails, the owner should know whether to fix the prompt, orchestration, tool contract, policy, data, or evaluator.

Not every failure should block deployment. Define the action for each severity: log, warn, require approval, quarantine, or fail the release. Scenario design is incomplete until the result has an operational meaning.

Version scenarios like code. Pin the model, tool schemas, datasets, and rubric versions used for a baseline. When a business rule changes, create a new revision and record the reason old results are no longer comparable.

Review the suite after incidents and near misses. Delete scenarios that no longer represent risk, but preserve historical evidence. A smaller suite with active ownership beats a giant folder of forgotten prompts.

Before publishing, ask: does this represent a real decision; are facts and unknowns explicit; are tools and permissions defined; are hard constraints observable; are failure outcomes actionable; is the expected evidence clear; and can the scenario run again after a model update?

If the answer is yes, the scenario is ready to become part of a repeatable evaluation pipeline. If not, it is probably still a prompt idea—not yet an engineering test.