AI agents

Evaluate AI sandbox security for code execution: isolation models, filesystem controls, network egress, secrets handling, audit logs, and real risk scenarios.

A security evaluation guide covering which events AI agent sandbox audit logs must capture, retention policies, log integrity, and how to surface logs for incident response.

Understand the architectural difference between a code interpreter and an agent runtime, and learn which workload characteristics push you toward each.

A practical security guide for teams enabling AI agents to install packages in sandboxes: allowlists, version pinning, registry mirrors, egress controls, and audit logging.

Requirements checklist for AI-generated code sandboxes: isolation, lifecycle API, concurrency, observability, resource limits, and backend integration.

A practical evaluation checklist for AI developers comparing sandbox providers: API surface, SDK compatibility, session lifecycle, packages, network, and pricing.

Compare Daytona alternatives by checking workspace state, isolation, region and deployment options, APIs, human access, observability, and pricing fit.

Evaluate Firecracker microVMs for AI agent sandboxes, including isolation, lifecycle overhead, egress, packages, secrets, and audit controls.

Evaluate open-source AI agent sandbox options by checking isolation, workspace state, egress, packages, secrets, logs, and operations.

Learn how RL agent sandboxes support repeatable trials with isolated state, resets, snapshots, telemetry, limits, and review boundaries.

Run Claude Code-style or managed coding agents in isolated workspaces with scoped files, shell policy, network controls, logs, and review.

Run MCP servers in an isolated MCP sandbox to scope filesystem, secrets, and network. Learn when MCP server isolation shifts the agent trust boundary.

Learn how to design a coding agent sandbox for repo checkout, commands, tests, diffs, secrets, logs, artifacts, cleanup, and human review.