> ## Documentation Index
> Fetch the complete documentation index at: https://agentcontrol-docs-add-source-code-notes-to-examples.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Agent Control
> Runtime guardrails for AI agents — configurable, extensible, and production-ready.
**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](https://github.com/agentcontrol/agent-control).
## 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
## Get started
Install, run, and protect your first agent in minutes.
Real-world use cases and end-to-end integrations.
Policies, controls, selectors, evaluators, and actions.
Component overview, data flow, and system design.