Agent Armor — Govern AI agent actions with zero trust…

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About this project

Agent Armor provides a robust framework for managing the actions of AI agents by implementing a zero-trust governance approach. With features like an 8-layer governance pipeline and detailed auditing capabilities, organizations can confidently control, approve, and monitor every action taken by AI systems before it occurs.

Agent Armor: Zero-Trust Governance for AI Agents

Agent Armor provides a comprehensive zero-trust governance runtime that helps users manage the actions of AI agents. As AI agents gain access to critical resources such as shell commands, file systems, databases, and APIs, the need for effective governance becomes paramount. Agent Armor empowers users with the ability to control, audit, and approve every action taken by AI agents before it occurs.

Key Features

8-Layer Governance Pipeline: A robust pipeline that evaluates actions with a clear understanding of risk.
Audit Trail: Keeps a detailed history of decisions, enabling thorough reviews and compliance.
Risk Scoring: Automatically assesses the risk associated with each action to ensure secure operations.
Operational Security Features: Includes response scanning for sensitive information, rate limiting, and threat intelligence to mitigate risks.
Storage Options: Default SQLite backend with optional PostgreSQL support for scalability.
Structured Logging: Offers configurable logging capabilities to optimize monitoring and debugging.

Community Scope

The current version, v0.3.0, introduces several features:

Full governance infrastructure and security measures for the operational framework of AI agents.
Integration with existing protocol standards and compliance measures ensuring safe AI execution environments.
User-friendly dashboard providing real-time metrics, audit browsing, and operational control.

Governance Process

Agent Armor evaluates AI actions with three options:

Allow: Permit the action to proceed unimpeded.
Review: Require human intervention for approval before executing the action.
Block: Prevent the action from being executed.

Example Usage

User interactions with Agent Armor involve API calls for inspecting actions or scanning responses. Below are examples of how to interact with the API:

# Inspect a safe action
curl -X POST http://localhost:4010/v1/inspect \
  -H "Authorization: Bearer <key>" \
  -H "Content-Type: application/json" \
  -d '{"agentId": "agent-01","workspaceId": "ws-demo","framework": "framework-name","protocol": "mcp","action": {"type": "file_read","toolName": "filesystem.read","payload": {"path": "README.md","intent": "read documentation"}}}'

# Scan a tool response for leaked credentials
curl -X POST http://localhost:4010/v1/response/scan \
  -H "Authorization: Bearer <key>" \
  -H "Content-Type: application/json" \
  -d '{"requestId": "scan-1","agentId": "agent-01","toolName": "tool-name","responsePayload": {"secret": "your-secret-here"}}'

By ensuring every action taken by AI agents is governed, Agent Armor significantly enhances security and accountability in environments where AI executes actions that could have far-reaching implications.