Agent Sandbox

Sandboxing has long been a containment pattern for code execution. For AI agents, the sandbox extends from process and filesystem boundaries to tool catalogs, network egress, and credential scope. Strong sandboxes turn many catastrophic incidents into recoverable ones.

Agent sandboxes typically constrain:

• Filesystem: Read and write access limited to scoped directories.
• Network: Egress restricted to explicit allowlists and domains.
• Tool catalog: Only approved tools and MCP servers are reachable.
• Credentials: Short-lived secrets bound to the sandbox session.
• Resource limits: CPU, memory, and request quotas that prevent runaway behavior.

Modern agent sandboxes also produce telemetry that feeds detection and posture management. The combination of containment and observability is what lets organizations adopt high-autonomy agents without giving up control.

Sandboxing strategies also have to anticipate the long tail of edge cases that agents discover at runtime, which is why operating sandboxes well requires investment in observability as much as in containment. Mature programs continuously refine sandbox boundaries based on what agents try to do, not just on what designers expected them to do.

How PointGuard AI Helps

PointGuard's Agent Governance Mesh applies runtime sandboxing in concert with AI Runtime Guardrails, combining environment-level isolation with behavior-level controls. The combined containment and behavioral defenses are what let organizations safely raise agent autonomy in step with business demand.

Learn More

• OWASP Top 10 for Agentic Applications
• NIST SP 800-207 Zero Trust Architecture
• MITRE ATLAS