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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.

Set up a Polyaxon code execution workspace

Create a bounded Polyaxon Python workspace, wait for readiness, execute a command, retrieve a report, and stop the sandbox service explicitly.

How to evaluate LLM guardrails

Compare LLM guardrails using attack blocking, legitimate task success, false refusals, latency, cost, and explicit handling of errors.

SLOs for AI applications and agents

Define service-level objectives for AI quality, task success, safety, latency, availability, and cost using measurable user-centered indicators.

Run ML workloads on your existing Kubernetes cluster

Plan a Polyaxon deployment on an existing Kubernetes cluster, covering GPUs, storage, identity, networking, workload ownership, and recovery.

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.

GPU cluster scheduling tools compared

Compare Kueue, KAI Scheduler, Volcano, Coscheduling, and Slurm Bridge by responsibility, workload fit, and Polyaxon integration path.

AI red teaming metrics: measuring failures and coverage

Measure AI red teaming with explicit attack success rates, attempt budgets, coverage, false refusals, severity, and evaluator uncertainty.

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.

How to evaluate LLM routers for cost, quality, and latency

Test LLM routing policies with task-level quality, cost, latency, fallbacks, route stability, and model-aware production evidence.

Self-hosted vs. managed AI inference

Choose between self-hosted and managed AI inference using data policy, model access, latency, utilization, reliability, staffing, and cost per accepted outcome.

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 improve GPU utilization

A practical guide to improving GPU utilization by diagnosing queue delays, input bottlenecks, resource fragmentation, sharing, and recovery overhead.

Red teaming RAG systems

Test RAG systems for poisoned documents, cross-tenant retrieval, stale permissions, citation leaks, and unauthorized tool actions.

What is an AI gateway?

An AI gateway centralizes model access, routing, resilience, policy, cost controls, and telemetry across production LLM applications and agents.

GPU utilization metrics: allocation, activity, and throughput

Learn which GPU utilization metrics explain capacity, device activity, memory pressure, and useful ML throughput—and how to avoid misleading averages.

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.

Prompt versioning for production AI systems

Treat prompts as versioned production artifacts with lineage, evaluations, promotion workflows, rollback, ownership, and runtime observability.

From notebooks to repeatable ML jobs

Move notebook experiments into repeatable ML jobs with explicit inputs, versioned code, reproducible containers, and Polyaxon tracking.

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.

Scale agentic AI without breaking the infrastructure

Scale AI agents with admission control, dependency-aware concurrency, durable state, bounded authority, backpressure, and outcome-based capacity planning.

What is GPU orchestration?

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

How to test prompt injection in LLM applications

Build prompt injection tests for user input, retrieved documents, and tool responses, with checks for data access and actual side effects.