Orchestration articles

Multi-cluster GPU orchestration

Design multi-cluster GPU orchestration around workload eligibility, data locality, queue routing, recovery, and clear dispatch ownership.

Operating long-running AI agents

Choose jobs, services, queues, and capacity controls for agent workloads using task duration, dependency limits, resume latency, and execution cost.

Durable execution for AI agents

Build agent recovery around committed checkpoints, versioned inputs, safe side effects, and approval state so interrupted tasks can continue reliably.

Designing a control plane for AI agents

Separate live agent requests from versioning, evaluation, policy, rollout, identity, evidence, and recovery across the application lifecycle.

Designing the runtime layer for AI agents

Design agent execution around durable state, safe tool retries, approval waits, and recovery, with clear responsibilities for Polyaxon and your agent framework.

What is GPU orchestration?

Understand how GPU orchestration connects workflows, queues, resource placement, and recovery across shared ML infrastructure.

Operate dynamic agent plans with bounded execution

Operate dynamic AI agent plans with Polyaxon execution, explicit policy checks, cumulative budgets, durable request state, and independent evaluation.

Design reliable ML pipeline orchestration

Design ML pipelines with explicit dependencies, resource placement, safe caching, bounded retries, and complete evaluation evidence in Polyaxon.

Manage AI coding-agent sandboxes with Polyaxon

Manage Polyaxon sandbox-enabled services from versioned environment templates through readiness checks, process execution, file persistence, and resource cleanup.

Manage the lifecycle of a Polyaxon sandbox service

Create a Polyaxon sandbox service, wait for readiness, track command execution, preserve outputs, configure timeouts and culling, and stop resources when work finishes.

Choose execution patterns for AI agents with Polyaxon

Use Polyaxon jobs, sandbox-enabled services, schedules, and tracking to match agent tasks to their execution, persistence, and capacity requirements.

Choose an AI agent architecture for production

Map agent architectures to Polyaxon jobs, services, and DAGs, with concrete workload configuration and a repeatable architecture-comparison workflow.

LLM agents: architecture and production operations

Map LLM agent reasoning, tools, execution, evaluation, and release workflows to Polyaxon services, sandboxes, jobs, components, and DAGs.

Single vs. multi-cluster Kubernetes for ML platforms

Choose between one Kubernetes cluster and multiple clusters using isolation, failure domains, data locality, accelerator access, operations, and cost.