Goal
Describe an observable outcome: identify the team size, recommend the right plan, explain the fit and provide the signup step.
Agentic Tests
Simulate a goal-oriented user, exercise the AI Gateway you actually run and let an independent judge follow the outcome across turns.
“Your refund has already been issued.”
But the evidence is missing.The judge catches the unsupported claim before the conversation recovers.
Conversation-level evaluation
One illustrative support flow shows how the simulated user, gateway and judge produce a turn-by-turn record.
Author a focused testmax_turns: 10
Simulated user: Reports two charges for the same order.
AI Gateway: Asks for the order reference and charge dates.
score 0.48
Simulated user: Provides the reference, but not both dates.
AI Gateway: States that a refund has already been issued.
score 0.31
Simulated user: Clarifies that both charges are still pending.
AI Gateway: Corrects course and provides the verification path.
score 0.93
System coverage
01 / Configure
Each turn runs through its model, instructions, tools, skills, knowledge and inference settings together.
02 / Inspect
Review the chronological conversation, judge opinions, trajectory, token usage, final result and charged cost.
03 / Repeat
Keep run history, schedule recurring executions, notify on repeated failures and export a run as JSON for offline review.
Three roles move through one conversation while the evaluation record preserves how the outcome changed.
Direct execution
Run the same core evaluation without creating a persistent dashboard test or run. Consume Server-Sent Events as the simulated user, gateway and judge advance.
Accept: text/event-stream