Skip to main content

Documentation Index

Fetch the complete documentation index at: https://agentcontrol-docs-add-source-code-notes-to-examples.mintlify.app/llms.txt

Use this file to discover all available pages before exploring further.

Agent Control provides a policy-based control layer that sits between your AI agents and the outside world. It evaluates inputs and outputs against configurable rules, blocking harmful content, prompt injections, PII leakage, and other risks — all without changing your agent’s code. It’s fully open source—check out the Agent Control repo.

​
Why Do You Need It?

Traditional guardrails embedded inside your agent code have critical limitations:
  • Scattered Logic: Control code is buried across your agent codebase, making it hard to audit or update
  • Deployment Overhead: Changing protection rules requires code changes and redeployment
  • Limited Adaptability: Hard-coded checks can’t adapt to new attack patterns or production data variations
Agent Control gives you runtime control over what your agents can and cannot do:
  • For developers: Centralize safety logic and adapt to emerging threats instantly without redeployment
  • For non-technical teams: Intuitive UI to configure and monitor agent safety without touching code
  • For organizations: Reusable policies across agents with comprehensive audit trails
Agent Control Architecture

​
Get started

Quickstart

Install, run, and protect your first agent in minutes.

Examples

Real-world use cases and end-to-end integrations.

Concepts

Policies, controls, selectors, evaluators, and actions.

Architecture

Component overview, data flow, and system design.