Sandboxing
In one line
Sandboxing means running an agent in an isolated environment so its actions cannot reach the outside system — a baseline practice for any autonomous agent.
Going deeper
Sandboxing is the isolation technique that contains the blast radius when an agent does something unintended. It shows up as virtual machines, containers and virtualised browsers, and it is non-negotiable for agents that touch real systems — Computer Use, coding agents and so on.
The same principle applies to marketing automation. Instead of pointing the agent straight at the production CRM or ad console, run it against an isolated test account, validate behaviour, and then roll it into production in stages.
Sandboxing matters for evaluation too, not just security. Replaying agent behaviour inside an isolated environment makes runs reproducible, which is what you need for consistent scoring. That is why eval frameworks and sandboxes are usually designed together.
Related terms
Computer Use
Computer Use is the agent capability of perceiving the screen and driving the mouse and keyboard, letting an AI operate a computer the way a human would.
AI AgentCoding Agent
A coding agent writes, edits, runs and tests code on its own — Claude Code, Cursor and Devin are the canonical examples in 2026.
AI AgentPermission Model
A permission model defines which tools, data and actions an agent is allowed to touch — the core safety layer for any autonomous agent.
AI AgentAgent Evaluation
Agent evaluation is the test and metric framework for measuring how accurately and safely an agent completes its goals — distinct from plain LLM benchmarking.
AI AgentAutonomous Agent
An autonomous agent runs with minimal human input — it decomposes the goal, executes, evaluates and iterates on its own until the task is done.
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