AI Agent Security / Head to head / Sandboxing vs Runtime monitoring

Sandboxing vs runtime monitoring for AI agents

A sandbox limits what a compromised agent can touch on the machine it runs on: files, processes, network destinations. It does not know an attack happened. Runtime monitoring notices the attack in the tool calls, but cannot stop code the agent already ran on the host. Agents that execute code need the sandbox; agents that call APIs with real permissions need monitoring, because the damage happens through legitimate access the sandbox allows.

Side by side

Marks follow the shared criteria. Sandboxing and Runtime monitoring are in the main comparison table.

DimensionSandboxingRuntime monitoring
What it protectsThe host and its secrets.The systems the agent's tools can reach.
Detects attacksNo: contains them.Yes: poisoning, injection, drift, exfiltration.
ExfiltrationPartial: egress allowlists.Strong: reads arguments and outputs.
Remote and SaaS agentsNot applicable.Covered where the calls pass through it.
Deployment effortMedium: easy for servers you run, hard inside desktop apps.Medium: needs a place in the call path.

Use them together

Coding agents are the clearest case for both: run them in a sandbox with file system and network limits, and route their tool calls through a gateway with runtime scanning. Anthropic describes the sandbox side for Claude Code; gVisor and Firecracker are the common building blocks.

Which suits whom

  • Agents that write and run code, or local MCP servers you do not trustSandbox them first.
  • Agents with API access to production systems or customer dataRuntime monitoring on every call; Runlayer scans definitions, outputs and sessions.

More comparisons