Step 2 - Run Evaluation

Prerequisites

• Agent deployed and running in the selected environment
• Selected .foundry/agent-metadata*.yaml file loaded for the active agent root
• Evaluation suite selected from the environment's evaluationSuites[]
• For generated suites: suiteName present and verified with evaluation_suite_get
• For legacy suites: local dataset and evaluator metadata available in .foundry/

Definition of Done — Evaluation Run

A Step 2 evaluation run is complete only when every box below is checked. Do not produce a final "evaluation complete" summary, score table, or report link until all items are done. "Status reached completed" is not a stopping condition — evaluation_get returns metadata only.

• [ ] evaluation_agent_batch_eval_create returned an evalRunId
• [ ] evalId and evalRunId mirrored into the selected .foundry/agent-metadata*.yaml (environments.<env>.lastEval.{evalId, evalRunId, runName, suiteName, suiteVersion, agentVersion, startedAt}) so a later turn can resume
• [ ] Polling reached terminal state (completed, failed, or cancelled)
• [ ] Per-item output_items downloaded via the azure-ai-projects Python SDK (see Step 3 → Download Results) — NOT via evaluation_get, NOT via evaluation_dataset_sas_url_get
• [ ] Results persisted under .foundry/results/<env>/<eval-id>/<run-id>.json
• [ ] Per-item failures and any passed: null / reason: null items have been clustered (Step 4) before summarizing

Run Agent-Target Batch Eval

Use evaluation_agent_batch_eval_create for batch evaluation, even when the selected metadata entry was produced by evaluation-suite generation. Treat the generated suite as the reviewed source of dataset/evaluator metadata, not as the execution API.

Before the run, if the selected suite has suiteName, call evaluation_suite_get(projectEndpoint, suiteName, version) and confirm it references the expected dataset/evaluators. Use the suite to select evaluator names, thresholds, and local review artifacts, then run evaluation_agent_batch_eval_create. Run suites tagged tier=smoke first unless the user chooses a broader suite tag or a specific suite.

Test Data

Use generated suite datasets for user review and lineage. For the agent-target batch eval tool:

• Pass test rows inline via the inputData parameter (array of {query: "...", expected_behavior?: "..."} objects). The service does not accept datasetName/datasetVersion references for agent-target runs — a generated suite dataset must be materialized into inputData rows by the caller.
• Reviewed local rows should include expected_behavior so rubric-based evaluators and failure analysis can preserve the user's rubric.
• Alternatively, set generateSyntheticData=true with generationModelDeploymentName, samplesCount (15–1000), and optional outputDatasetName when the user wants the agent-target run to generate a fresh test set instead of supplying inputData.
• Do not call evaluation_suite_run for batch eval.

⚠️ Parameter-name guardrail: The inline-rows parameter is inputData. The service rejects data, inputItems, and inputDataItems with the misleading error "At least one input data item must be provided ... Set generateSyntheticData=true to auto-generate test queries instead." — that error means the rows were sent under the wrong key, not that synthetic generation is required.

Before setting deploymentName, use model_deployment_get to list actual project deployments and choose one that supports chat completions; do not assume gpt-4o exists.

Parameter Naming Guardrail

evaluation_get does not accept evaluationId; switch to evalId after run creation.

⚠️ Eval-group immutability: Reuse an existing eval group only when dataset, evaluator list, and thresholds are unchanged. If evaluator definitions or thresholds change, create a new evaluation group or suite version.

Auto-Poll for Completion

Immediately after creating the run, poll evaluation_get in a background terminal until completion. Use evalId + isRequestForRuns=true for run lists. The run ID parameter is evalRunId (not runId).

Only surface the final result when status reaches completed, failed, or cancelled.

⚠️ evaluation_get returns run metadata only — it does NOT return per-item scores, agent responses, or judge reasons. Once the run reaches terminal state, you MUST immediately follow Step 3 → Download Results and pull output_items via the azure-ai-projects Python SDK (client.get_openai_client().evals.runs.output_items.list(...)). Do not attempt to use evaluation_dataset_sas_url_get on the result artifact (eval-result-<runId>-*) — that endpoint is for evaluation input datasets and returns 500 for result artifacts.

💡 Mirror IDs to metadata immediately. Right after evaluation_agent_batch_eval_create returns, write evalId, evalRunId, runName, suiteName/suiteVersion, agentVersion, and startedAt to the selected environment's lastEval block in .foundry/agent-metadata*.yaml. This lets a later turn resume polling or downloading without re-reading chat history. The azd .env (LAST_EVAL_ID, etc.) is azd-internal and should not be relied on by skill flows.

Background Polling Pattern

MCP tools live in the agent's process, so a true detached poller cannot call MCP tools directly. Use one of these concrete patterns instead of saying "ping me later":

1. Sentinel-file poller (preferred for long-running jobs). Spawn a sync terminal Python job that polls the Foundry REST API with the user's Azure credential (via azure-identity + requests) every 60–120s and writes status to .foundry/.poll/<evalRunId>.json when terminal. The next turn reads the sentinel file before doing anything else.
2. Batched in-turn polling. If a sentinel poller is unavailable, batch 2–4 poll calls per turn (60–120s apart, via short sleep between MCP calls in the same response) before yielding back to the user. Always explain that polling will continue on the next turn and update the metadata's lastEval.lastPolledAt so resumption is obvious.
3. Never silently stop. Returning "ping me later" without updating metadata or spawning a sentinel is a workflow violation — the user has to remember state for you.

Next Steps

When evaluation completes -> immediately proceed to Step 3: Analyze Results and download output_items. Do not produce a summary first.

Reference

• Azure AI Foundry Cloud Evaluation
• Built-in Evaluators