> For the complete documentation index, see [llms.txt](https://murray-love-code.gitbook.io/murray-love-code-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://murray-love-code.gitbook.io/murray-love-code-docs/readme.md).

# README

> A practical framework, evaluation benchmark and implementation guide for governing AI agents that answer questions from enterprise data.

**Last reviewed:** 15 September 2026 · **Framework version:** 0.1.0

Enterprise Data Agent Governance is an open practitioner framework for deciding when an AI agent may answer a question from enterprise data, when it must request clarification or defer, and what evidence an organization should retain.

This knowledge base turns the canonical repository into a navigable implementation guide. GitHub remains the authoritative source for code, schemas, machine-readable records, releases and version history. Start with [the governing principle](/murray-love-code-docs/governing-principle.md), then use the [implementation checklist](/murray-love-code-docs/implementation-checklist.md) and [evaluation protocol](/murray-love-code-docs/evaluation/running-an-evaluation.md).

## Platform roles

| Property                                                                                    | Role                                             |
| ------------------------------------------------------------------------------------------- | ------------------------------------------------ |
| [GitHub](https://github.com/murraylovecode/enterprise-data-agent-governance)                | Canonical files, schemas and history             |
| [Public reference site](https://murraylovecode.github.io/enterprise-data-agent-governance/) | Main public overview                             |
| GitBook                                                                                     | Technical guidance and implementation paths      |
| Hugging Face                                                                                | Dataset viewer and machine-readable distribution |

## Important disclosures

Enterprise Data Agent Governance is an independently maintained open-source project built on the upstream Mnemiq foundation. It is not official Mnemiq documentation, does not claim a formal partnership and does not constitute independent certification or validation of any vendor. See Upstream Project and Enterprise Extension for the project relationship.

The framework is not legal, compliance or security advice. Organizations must adapt the controls to their own risk, data, jurisdiction and operating environment.

[Edit the canonical source on GitHub](https://github.com/murraylovecode/enterprise-data-agent-governance)


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://murray-love-code.gitbook.io/murray-love-code-docs/readme.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.