Agent Governance Toolkit
Policy enforcement and execution sandboxing for autonomous AI agents.
Deterministic policy enforcement and zero-trust identity for AI agent deployments.
This toolkit provides deterministic policy enforcement, zero-trust identity, and execution sandboxing for autonomous AI agents. It is designed for engineers shipping multi-agent systems to production who need verifiable control over agent actions.
What it is
It intercepts tool calls, message sends, and agent delegations in application code before model intent reaches the wire. It evaluates YAML-based policies through an integrated engine, logging decisions to an audit trail and raising errors when actions violate defined rules.
Who it's for
It suits backend and security engineers deploying autonomous AI agents that interact with external tools or databases. It does not suit teams relying solely on prompt-level safety instructions or those lacking Python/multi-language application integration experience.
Key features
Deterministic Policy Enforcement
Intercepts tool calls in application code to block unauthorized actions before execution.
Zero-Trust Identity
Tracks specific agent actions within multi-agent systems sharing credentials.
Multi-Language SDKs
Provides policy engines and wrappers for Python, TypeScript, .NET, Rust, and Go.
Tamper-Evident Auditing
Logs decisions with active policies, requested actions, and rationale for compliance.
Up and running in 3 steps
Our honest verdict
What's great
- Blocks unauthorized agent actions structurally rather than relying on prompt instructions.
- Covers the OWASP Agentic Top 10 with full and partial framework mappings.
- Offers native integration across Python, TypeScript, .NET, Rust, and Go environments.
- Enforces granular policies down to specific parameter values and tool functions.
Where it falls short
- Public preview release status means potential breaking changes prior to general availability.
- Requires maintaining YAML policy files and managing integration hooks across application code.
- Transitioning from older deprecated distributions requires manual package and rule updates.
Pricing & license
Free under its MIT licence. There is no paid hosted option mentioned in the README, so the only cost is running and maintaining it yourself.
Open remote AI-training roles
880 open remote AI-training roles, updated November 15, 2026; most pay $20–$175 an hour. Zekai lists them free; the application goes to the employer’s platform.
Alternatives
Use it, or have us set it up
FAQ
How does this prevent prompt injection attacks?
It intercepts tool calls in deterministic application code before execution, making forbidden actions structurally impossible regardless of model behavior.
Can I use this with languages other than Python?
Yes, SDKs and integration wrappers are available for TypeScript, .NET, Rust, and Go.
What happens when a policy rule is violated?
The execution raises a GovernanceDenied exception and logs the blocked action to the audit trail.
