AI Agent Architecture, Evaluation, and Operations

Design multi-tenant AI agent infrastructure on Kubernetes

Separate identity, network access, data, queues, budgets, and evidence when multiple teams or customers run AI agents on shared Kubernetes infrastructure.

Sandbox AI agents on Kubernetes

Design isolated Kubernetes execution for AI agents with a threat model, stronger runtimes, scoped identity, controlled egress, bounded storage, and auditable cleanup.

Pods, Jobs, or Services for AI agents?

Choose the Kubernetes execution shape for an AI agent by separating logical task identity from Pods, Jobs, Services, workflows, and sandbox lifecycles.

Orchestrate parallel coding agents with Polyaxon

Run parallel coding agents in separate Polyaxon workspaces, collect candidate patches, evaluate independently, and merge only reviewed changes.

Kubernetes for AI agents: A platform engineering guide

Design a Kubernetes platform for AI agents with clear workload boundaries, durable state, scoped access, resource controls, and end-to-end evidence.

Beyond agent traces: State, artifacts, and recovery

Connect agent traces to checkpoint state, versioned artifacts, action receipts, and evaluations so failures lead to controlled recovery and reproducible improvements.

Design measurable AI agents before you build

Define agent outcomes, prohibited actions, events, evaluators, budgets, segments, and feedback loops before implementation begins.

Build an operating model for agentic AI

Operate agentic AI with explicit service ownership, platform contracts, task lifecycle rules, change control, incident response, SLOs, and financial accountability.

Design production security for AI agents

Contain AI agent identity, tools, memory, network access, and delegated actions with enforceable production boundaries.

Connect agent execution to the ML platform

Connect AI agent execution to Polyaxon projects, components, scheduling, tracking, and artifacts through a stable application-owned execution contract.

Build a shared library of agent evaluators

Package application-owned agent evaluators as versioned Polyaxon components with typed contracts, reusable execution profiles, and consistent result evidence.

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.

An AI agent deployment checklist for the first production release

Build an agent release checklist around Polyaxon qualification jobs, versioned components, artifact reports, termination settings, and manual approval.

Build a reliable foundation for autonomous agents

Build an operational foundation for autonomous agents using Polyaxon workload profiles, durable state, bounded authority, evaluation, and recovery ownership.

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.

MCP security testing: tools, permissions, and untrusted content

Build MCP security tests for tool discovery, authorization, injected tool results, session identity, and approval boundaries in AI agents.

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.

Measure AI agent workflow bottlenecks

Measure Polyaxon agent workflow bottlenecks across planning, queueing, execution, transfers, and evaluation using critical-path timing and quality-aware comparisons.

MCP observability: Monitor tools, resources, and context

Trace MCP discovery, sessions, resources, prompts, tool calls, permissions, errors, and agent outcomes across clients and servers.

Build a conversational assistant on Kubernetes

Design a production conversational assistant with grounded retrieval, controlled model access, durable conversation state, evaluation, security, and Kubernetes operations.

Contain generated code with Kubernetes runtime controls

Reduce the impact of generated-code failures with Polyaxon workload configuration, Kubernetes admission and network controls, minimal credentials, and incident evidence.

How to red team AI agents

Test AI agent permissions, memory, handoffs, retries, and tool actions with a practical security matrix and reproducible evaluation workflow.

Review the trust boundary for LLM-generated code

Review the trust boundary for LLM-generated code before execution, mapping permissions, trusted controllers, output handling, and promotion controls in Polyaxon.