AI Agents articles

Build and evaluate LLM function calling

Connect bounded LLM tools to Polyaxon sandboxes, validate calls in the host, retain execution receipts, and compare tool behavior in repeatable evaluation runs.

Operate remote code execution with Polyaxon

Operate remote code execution through Polyaxon with explicit session ownership, command receipts, artifact collection, and safe retry decisions.

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.

Give coding agents controlled access to Polyaxon operations

Build a coding assistant around Polyaxon run queries, sandbox commands, component versions, and reviewable operations that preserve project context and execution evidence.

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.

Reduce AI agent startup latency with Polyaxon

Reduce Polyaxon agent startup latency by measuring queueing, image preparation, initialization, and readiness before tuning capacity or workspace reuse.

Build and evaluate AI agent guardrails in Polyaxon

Use Polyaxon to version guardrail experiments, compare case-level outcomes, constrain execution, and require evidence before promoting an agent release.

Choose a code execution API for AI workflows

Compare code execution API shapes and implement clear request, result, cancellation, and artifact contracts for Polyaxon agent workflows.

Cache pipeline steps without hiding LLM regressions

Cache deterministic Polyaxon preparation steps while forcing fresh LLM evaluations, using explicit dependency identities and separate cache policies.

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.

Choose compute platforms for AI agent jobs

Choose compute platforms for AI agent jobs by execution shape, state, resource needs, and operational responsibility, using Polyaxon as the workload context.

Design tenant isolation for AI execution in Polyaxon

Connect Polyaxon projects, access controls, compute queues, workload identities, and connection catalogs into a practical tenant isolation design.

Benchmark coding-agent sandboxes with Polyaxon

Benchmark coding-agent sandboxes with controlled workloads, reproducible Polyaxon runs, latency distributions, recovery scenarios, and quality-adjusted results.

Reduce container escape risk in Polyaxon workloads

Use Polyaxon scheduling presets, workload identities, isolated compute, and run lineage to reduce and investigate container escape risk in AI execution.

Run browser automation in Polyaxon sandboxes

Package a browser runner as a Polyaxon service, execute reviewed cases through SandboxClient, retrieve screenshots and reports, and compare agent behavior across releases.

Secure AI agent runtime execution with Polyaxon

Design Polyaxon agent execution around bounded runs, scoped connections, workload identities, controlled compute environments, and recorded runtime evidence.

Configure Polyaxon sandboxes for AI development and execution

Use Polyaxon services, sandbox access, queues, presets, connections, and artifact workflows to configure repeatable AI development and execution environments.

Choose state lifetimes for AI agent workspaces

Choose appropriate lifetimes for agent process memory, sandbox files, persistent volumes, artifacts, and application state in Polyaxon.

Separate agent planning, execution, and evaluation

Structure Polyaxon agent systems around separate planning, execution, and evaluation contracts with explicit state and promotion decisions.

Investigate runtime threats in Polyaxon agent workloads

Correlate Polyaxon run records with external runtime-security events to investigate agent workloads, preserve evidence, and coordinate containment.

Stop expensive agent evaluation sweeps early

Use Polyaxon failure and metric early-stopping rules to bound agent evaluation sweeps while preserving complete evidence and avoiding premature quality decisions.

Use MCP and function calling in Polyaxon agents

Choose direct function tools or MCP adapters for Polyaxon agents based on reuse, authorization, lifecycle, and execution contracts.

Connect MCP servers to Polyaxon workflows

Design application-owned MCP tools that submit and inspect Polyaxon workflows while preserving authorization, bounded inputs, and run-level evidence.

Define trusted execution boundaries for AI workloads

Distinguish confidential computing from workload isolation, and define how Polyaxon scheduling and evidence fit a trusted execution design.