Connect agent execution to the ML platform

An agent's execution layer should not become a separate island from the ML platform. The code it runs, datasets it touches, and evaluations it produces are part of the same engineering lifecycle as training and deployment.

Connect that work to Polyaxon through stable execution contracts. This preserves platform visibility while allowing the agent framework to evolve independently.

Treat execution as a platform operation

An agent framework decides when to call a tool. The execution adapter decides whether the request is allowed and which reviewed workload should perform it.

Use a Polyaxon job for a fixed analysis or evaluation. Use a sandbox-enabled service when the workflow needs repeated commands and workspace files.

Keep the adapter's public contract focused on permitted operations and input references. Resolve project, queue, credentials, and component versions in trusted code.

This separation lets multiple agents reuse the same operational capability without each implementing its own cluster integration.

Preserve a common identity across the lifecycle

Associate the application request, agent step, Polyaxon run, source revision, and output artifact. Record these references when the work is created rather than trying to reconstruct them from logs later.

For an assistant evaluating a model change, the evidence should connect the proposed candidate to the dataset and evaluator used. A report without those references is difficult to compare or reproduce.

Use tracking metadata for compact identifiers and artifacts for approved detailed outputs. Keep sensitive content out of ordinary run names and tags.

Reuse platform controls without overstating them

Connections configure external resources for workloads. They do not automatically implement per-tool authorization.

Commercial queues provide routing, priorities, concurrency, and quota controls. They do not guarantee that capacity is instantly available.

Sandbox process access enables remote commands inside the selected service. It does not introduce a separate kernel or independently evaluate the generated program's safety.

These distinctions make the integration stronger: each control has an explicit purpose, and missing requirements can be assigned to application or infrastructure owners.

Define the adapter's lifecycle behavior

A request should move through authorization, submission, readiness, execution, output collection, and cleanup. Preserve an explicit outcome at each stage.

If a client disconnects, the controller should find the existing request. If execution has an uncertain external side effect, reconcile it before retrying. If a service expires, reconstruct useful state from approved checkpoints.

Use termination settings to bound workloads, while keeping request-level budgets and idempotency in the application.

Promote useful work back into the platform

A successful exploratory script can become a reviewed component. Its dependencies move into an image, its inputs become explicit, and its evaluator becomes part of the normal workflow.

The agent no longer needs to regenerate that implementation for every request. It can call the accepted capability and focus exploration on genuinely new questions.

That is the practical value of connecting execution to the ML platform: agent work gains the same reproducibility, scheduling context, and evidence trail as the rest of the team's engineering work.