Agent Sandboxing
Isolating agent execution in restricted environments that limit access to system resources, networks, and data. Sandboxing prevents agents from performing unintended or harmful actions beyond their authorized scope.
Agent sandboxing is a security-first approach to agent deployment. Since agents can execute code, call APIs, and interact with systems, they need strict boundaries to prevent accidental or malicious misuse. A sandbox restricts what resources the agent can access (file system, network, databases), what actions it can perform (read-only vs. read-write), and how much of each resource it can consume (CPU, memory, API calls).
For teams deploying agents that execute code or interact with production systems, sandboxing is essential. Container-based sandboxes (like Docker or Firecracker) provide strong isolation for code execution. API-level sandboxes use token scoping and permission policies to limit what external services the agent can access. Network sandboxes restrict which endpoints the agent can reach. Implement defense in depth: even if the model generates malicious code, the sandbox prevents it from causing harm. Common sandbox implementations include E2B for cloud code execution, Docker containers for custom environments, and policy-as-code frameworks for API access control.
Related Terms
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.