Build an AI agent governance workflow in Polyaxon

An agent release includes more than a model. Its behavior also depends on prompts, tools, retrieval sources, credentials, and the environment in which it executes actions. Polyaxon gives teams a common workflow for recording those inputs, evaluating a candidate, reviewing its evidence, and scheduling the approved operation.

For example, an internal support agent might start by summarizing failed training runs, then gain the ability to retry an approved job. That change in authority deserves a release review even if the model stays the same. The review should show which cases were evaluated, what the candidate can access, and who is responsible for its operation.

Define a release that reviewers can reproduce

Package the evaluation procedure and agent launcher as reusable Polyaxon components. Keep their code revision, image digest, prompt configuration, tool schema, and case-manifest revision explicit. Record references to data and policy versions rather than placing secrets inside configuration.

The Component Hub links runs to component versions and supports registering a version from a successful run. Use specific version references for release evaluation and avoid changing a referenced version in place. Version labels are useful for navigation, but a defensible release record also identifies the underlying code and image.

Give evaluation and deployment different connections. An offline test should use representative fixtures or narrow read access. Production write access belongs only on the operation that needs it, after the qualification and review stages.

Put evidence before the release operation

A Polyaxon DAG can express the relationship between evaluation, policy checks, and deployment. This example assumes your team has registered the three referenced components and configured their required inputs:

version: 1.1
kind: component
name: agent-release-review
run:
  kind: dag
  operations:
    - name: evaluate
      hubRef: agent-evaluation:v3
    - name: qualify
      hubRef: agent-release-policy:v2
      dependencies: [evaluate]
      trigger: all_succeeded
    - name: deploy
      hubRef: agent-deployment:v4
      dependencies: [qualify]
      trigger: all_succeeded
      isApproved: false

The qualification component is code you implement. It should verify that required cases ran, evaluate the agreed thresholds, and fail when evidence is incomplete or the candidate violates release policy. Polyaxon's dependency and trigger rules then keep deployment from starting unless qualification succeeds.

If the deployment needs an artifact or release identifier from an upstream operation, pass it through an output parameter reference so the exact evaluated candidate is promoted. A shared mutable file named latest.json weakens that connection even when the DAG itself is correct.

Make manual approval a concrete review

The manual approval flag pauses an operation until it is approved through the UI, CLI, or API. Reviewers can inspect evaluation metrics in the comparison dashboard and open the case-level reports attached to each run before approving the deployment operation.

Review quality and authority together: whether the agent completes the intended task, which tools it can call, whether its connections changed, and whether denied-action cases behaved correctly. Retain the review disposition and any accepted exceptions as part of your release evidence.

An approval flag is a workflow control. Users can override the flag at submission, and approval can be issued programmatically. Configure who can submit or approve production work through platform permissions and your release automation. Pausing a deployment operation does not automatically require approval for each subsequent tool call inside the running agent; that belongs in the application.

Connect the approved release to its execution policy

Use presets to select the workload identity, resources, termination behavior, and connections used by the production launcher. Keep those deployment settings versioned with the release process. In commercial deployments, restrict production queue access and direct the approved workload to the intended compute agent.

The application should authenticate its user, authorize tool arguments, and validate approval for consequential actions immediately before execution. Polyaxon schedules and tracks the operation; it does not infer the business authority of a model-generated request.

Likewise, connections simplify access to external resources, but their IAM and database permissions determine what the agent can actually do. A governance review should inspect those permissions, not just the connection names listed in the launcher.

Turn production findings into the next evaluation

Retain task outcomes, tool denials, latency, cost, and human corrections with enough context to reconstruct failures without exposing unnecessary user content. Use the lineage view to follow tracked inputs, artifacts, and dependent operations back to the evaluated release.

When an incident reveals a new failure case, add a sanitized reproduction to the evaluation manifest and run it against the baseline and candidate. That connects governance to normal Polyaxon iteration: version a change, evaluate it, inspect the difference, and approve a specific deployment. Retirement follows the same ownership model, with explicit removal of schedules, tool access, and credentials that the release no longer needs.