Build an AI agent infrastructure stack with Polyaxon
Map an AI agent stack to Polyaxon services, jobs, sandboxes, queues, connections, tracking, and versioned components without conflating their responsibilities.
An AI agent stack needs more than an orchestration library. It needs somewhere to run the controller, an execution path for tools, durable state, and evidence that connects a decision to its result.
Polyaxon can provide a common operational context for these pieces. The useful architecture is not one giant agent container; it is a set of workloads with explicit contracts and ownership.
Map responsibilities to platform primitives
| Responsibility | Polyaxon building block | Your application or infrastructure supplies |
|---|---|---|
| Request handling and agent loop | Service | Agent framework, authorization, session logic |
| Finite preparation or evaluation | Job | Dataset preparation and evaluator implementation |
| Interactive command execution | Sandbox-enabled service | Tool policy and approved execution environment |
| Scheduling and placement | Queues and compute agents, where available | Cluster capacity and routing policy |
| External data and credentials | Connections | Scoped external permissions and rotation |
| Results and reproducibility | Tracking, artifacts, component versions | Quality criteria and promotion decisions |
A Polyaxon compute agent manages workload execution in a namespace or cluster. It is not the same thing as the AI agent that reasons about a task.
Follow one request across the stack
Consider an assistant that proposes a fix for a failing training pipeline. The request handler authenticates the engineer and identifies the relevant project. An approved collector retrieves a minimized failure bundle.
The agent loop proposes a change. A sandbox-enabled service gives it a workspace for editing and executing code under the selected runtime controls. An independent job evaluates the resulting patch on fixed fixtures.
Finally, the controller returns a report linking the patch, execution receipt, and evaluation run. The user approves promotion through the team's release process. No step needs unrestricted access to all the others.
This division also makes failures easier to interpret. A successful sandbox command with a failing evaluator is a different outcome from an unavailable service or an unauthorized data request.
Make state an explicit dependency
Keep session records in an application-managed durable store. Use connections to configure access where appropriate. Do not assume a running service's memory survives Pod replacement.
Store useful outputs through Polyaxon artifacts. Record source and dataset revisions with the run so another engineer can reconstruct the execution context.
A workspace volume, an artifact store, and a session database have different semantics. The volume supports active work; artifacts preserve outputs; the database supports mutable application state. Choosing one does not eliminate the others.
Establish platform defaults and enforced policy
Package reviewed workload configurations as components and use scheduling presets for reusable defaults. Include images, resource requests, timeouts, and required connections.
Defaults help teams start consistently, but they are not an immutable security boundary. Enforce mandatory restrictions through project permissions, available queue restrictions, Kubernetes admission, and network policy.
For commercial scheduling capabilities, queue configuration can separate development, evaluation, and production capacity. Measure waiting time and utilization before adding more pools.
Start with one complete vertical slice
Build a small path that accepts one request, runs one tool, saves one artifact, and produces one independently checked result. Add recovery for a stopped executor and an unavailable dependency before increasing autonomy.
That slice establishes the operational contract for the larger stack. New models and tools can then reuse the same Polyaxon execution and evidence patterns without rebuilding the platform around each framework.