Evaluate AI agent infrastructure reliability
Evaluate AI agent infrastructure reliability through task-level outcomes, recovery behavior, Polyaxon execution evidence, and explicit operational ownership.
Infrastructure reliability should be measured in completed agent tasks, not only in whether an API endpoint responds. A request can be accepted while its executor never becomes ready, its output cannot be collected, or its side effect is repeated after a retry.
Use Polyaxon run records and application evidence to evaluate the entire path. The platform's status is one signal in a broader reliability contract.
Define success from the user's perspective
For a coding assistant, success might mean producing a valid patch and a complete evaluation report within the expected session window. For a data-analysis agent, it may mean returning a verified calculation with the correct dataset reference.
Record the denominator: all accepted requests, including timeouts and interrupted sessions. Excluding failures because they produced no final artifact makes reliability look better than the user experience.
Track infrastructure completion separately from task quality. An executed program can complete correctly while the agent's answer remains unsuitable.
Identify the dependency chain
List the controller, Polyaxon API, scheduling path, image registry, execution cluster, input store, model provider, and output store. Decide which failures the application can tolerate and which require a clear stop.
Use run timelines and logs to locate platform delays. Add request-level instrumentation for provider calls and output verification.
A dashboard showing healthy Pods does not establish that the complete task path is healthy. Conversely, a model-provider outage should not automatically be diagnosed as a sandbox failure.
Evaluate recovery, not just availability
| Failure scenario | Required behavior |
|---|---|
| Executor never becomes ready | Deadline and explicit failure |
| Client disconnects after submission | Existing request can be found |
| Process fails mid-task | Partial outputs identified and not promoted |
| External write has uncertain outcome | Reconciliation before retry |
| Service is replaced | Durable state can reconstruct the task |
| Artifact collection fails | Result marked incomplete |
Exercise these scenarios in an authorized environment with representative inputs. Record both the recovery outcome and the effort needed to investigate it.
Termination and retry settings help bound workload behavior, but they do not create application-level exactly-once execution.
Review capacity and change management
Measure queue waiting time under normal demand and bursts. Where available, queue concurrency and quotas can protect workload classes from uncontrolled fan-out.
Keep image and component revisions with the results. Compare reliability before and after changes to dependencies, scheduling policies, or the execution backend.
A proposed improvement should include rollback and a clear owner. Reliability often degrades when a shared configuration changes without an equivalent workload evaluation.
Build a decision record from evidence
Document observed task completion, recovery time, unresolved failure modes, support ownership, and applicable service commitments. Do not substitute an advertised SLA for your own recovery observations.
Use Polyaxon artifacts to retain sanitized reports and run comparisons to inspect alternatives.
The resulting decision is more useful than a feature scorecard alone. It explains how the infrastructure behaves when something goes wrong and whether the team can restore useful agent work predictably.