Permission Model
In one line
A permission model defines which tools, data and actions an agent is allowed to touch — the core safety layer for any autonomous agent.
Going deeper
A permission model is how you balance agent autonomy and safety in one place. It spells out which tools the agent can call, which data it can read or write, and which actions require a human approval. Claude Code's per-tool approvals and OpenAI Assistants' tool whitelists are simple examples.
For marketing the practical question is how to cut up specific privileges — CMS publish, ad spend, customer data lookup. Too broad and a single mistake gets expensive; too narrow and the agent stops being useful.
The current direction is 'least privilege' as the default, with just-in-time elevation through a human approval when more access is genuinely needed. MCP, UCP and similar protocols are starting to treat permission delegation as a core design concern, and the standardisation conversation moved fast through 2026.
Related terms
Sandboxing
Sandboxing means running an agent in an isolated environment so its actions cannot reach the outside system — a baseline practice for any autonomous agent.
AI AgentHuman-in-the-Loop
Human-in-the-loop (HITL) is the design pattern where an agent runs autonomously but routes critical decisions through a human for review and approval.
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 AgentMCP
MCP (Model Context Protocol) is the open standard introduced by Anthropic for connecting LLMs to external tools and data sources in a consistent way.
AI AgentComputer 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.
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