Create an Agent Builder eval

Use the shared Instance AI eval harness. Agent cases use an Agent-specific authoring directory, dataset, and LangTracer suite.

Required LangTracer preflight

Run this check before sourcing, drafting, or writing an eval. Run it from packages/@n8n/instance-ai:

pnpm exec dotenvx run -f ../../../.env.local -- \
  sh -c 'test -n "${LANGTRACER_URL:-}" && test -n "${LANGTRACER_API_KEY:-}"'

If the check fails, stop before creating an eval file. Ask the user to:

1. Generate a key on the LangTracer API page.

2. Add these variables to the repository root .env.local file:

   LANGTRACER_URL=https://lang-tracer.n8n-maintenance.workers.dev
   LANGTRACER_API_KEY=<generated-key>
   

3. Confirm when the environment is ready.

Do not ask the user to paste the key into chat. Do not print or inspect its value. Rerun the check after the user confirms. Continue only when it passes.

Non-negotiable routing

• Author the case at packages/@n8n/instance-ai/evaluations/data/agents/<slug>.json.
• Set "datasets": ["agents"].
• Push general Agent Builder cases to Instance AI capabilities — agents. Its suite slug is agents.
• Do not commit the case JSON. LangTracer is the durable source of truth.
• Commit changes to this skill, the harness, and CI when applicable.

The disk runner loads both data/agents/ and data/workflows/. A misplaced Agent case can therefore pass locally. That does not make the location correct.

Decide what the case proves

Write the smallest user request that exercises the changed behavior.

• Use processExpectations for the Instance AI conversation and final response.
• Use outcomeExpectations for the created Agent artifact and its configuration.
• Use executionScenarios only when the built Agent must run to prove the behavior.
• Declare only the credentials that the build must see.

The harness captures the Agent configuration and authored skills. It supplies them to the expectation judge. A scenario-less Agent case is valid when process or outcome expectations can prove the behavior.

Use the substitution test for every expectation. A correct alternative build must pass. A build that misses the requested behavior must fail.

For multi-turn, seeded, or capability-gap cases, follow the case-shape and calibration rules in create-instance-ai-eval. This skill overrides its workflow directory and suite guidance for Agent cases.

Draft the case

Start with this shape:

{
  "description": "The Agent Builder behavior this case guards.",
  "conversation": [
    { "role": "user", "text": "Build me an Agent that ..." }
  ],
  "complexity": "simple",
  "tags": ["agent", "agent-build", "<capability>"],
  "credentials": [{ "type": "<credentialType>", "name": "<display name>" }],
  "processExpectations": [
    "The final response ..."
  ],
  "outcomeExpectations": [
    "A standalone Agent was created and no workflow was created.",
    "The Agent ..."
  ],
  "datasets": ["agents"]
}

Keep the prompt in the user's voice. Do not tell Instance AI which internal tools or configuration fields to use unless that choice is the behavior under test.

Validate and run locally

Read local-setup.md when the machine does not already have an eval instance and environment file.

From packages/@n8n/instance-ai:

pnpm exec tsx -e "import { loadAgentEvalTestCasesWithFiles } from './evaluations/data/agents/index.ts'; const matches = loadAgentEvalTestCasesWithFiles('<slug>'); if (matches.length !== 1) throw new Error('Expected exactly one Agent eval case, found ' + matches.length); console.log(matches[0].fileSlug)"

pnpm eval:instance-ai \
  --base-url http://localhost:5680 \
  --filter <slug> \
  --tier agents \
  --concurrency 1 \
  --keep-workflows \
  --verbose

Use eval:instance-ai for a new disk case. eval:agents reads the published LangTracer suite and is for running cases that are already there.

Inspect the transcript, the rendered Agent artifact, and each judge reason. Do not accept a green result when a conditional expectation never occurred. Do not weaken an expectation to hide a real Agent Builder defect.

CI coverage

The Instance AI PR gate checks the files changed by the PR. A change under packages/cli/src/modules/agents/ selects the Instance AI capabilities — agents suite through its agents slug. It also selects the agents dataset and uses an absolute pass gate. The run uses a suite-scoped LangSmith cohort. It does not write to the workflow dataset or compare against the workflow baseline. Other Instance AI changes select the baseline suite and its pr dataset.

The gate runs when a PR opens, reopens, or becomes ready for review. It does not run for each new push. Use the PR gate's manual dispatch after a later push.

Credential behavior

Declared credentials are real n8n credential records with placeholder data. The eval thread limits the builder to those credential IDs.

Agent Builder model catalog requests return deterministic fake models during an eval. They do not decrypt the placeholder model credential or call its provider. Production model catalog requests remain live.

This mock covers catalog lookup only. A builder call_agent action and an Agent executionScenario run the target Agent model. They need a working provider credential such as EVAL_OPENAI_API_KEY. A build-only case does not need one.

Calibrate and publish

1. Run the case once with --concurrency 1.
2. Confirm that Instance AI called build-agent.
3. Confirm that the expected Agent artifact was captured.
4. Classify each red as a product gap, harness limitation, or non-determinism.
5. Use --iterations 5 before adding a case to a gating tier.
6. Preview the LangTracer change.
7. Push it to agents.

pnpm exec dotenvx run -f ../../../.env.local -- \
  pnpm eval:langtracer-push --suite agents --dry-run --changed

pnpm exec dotenvx run -f ../../../.env.local -- \
  pnpm eval:langtracer-push --suite agents --changed

The push needs LANGTRACER_URL and LANGTRACER_API_KEY. Generate a key on the LangTracer API page. Report the case and suite as clickable LangTracer links. Delete the local JSON after a successful push.

Completion checklist

• The LangTracer environment preflight passed before authoring.
• The case is in data/agents/ and uses the agents dataset.
• The strict loader accepts it.
• A local run captures a standalone Agent.
• The transcript proves each process expectation was exercised.
• The rendered Agent artifact proves each outcome expectation.
• Repeated runs are stable enough for the selected tier.
• The case is pushed to Instance AI capabilities — agents with suite slug agents.
• Agent Builder PR changes select the agents suite in CI.
• The local case JSON is not committed.