Map the system first.
Agents propose the rebuild. You approve it.

Your structure lives in compiled plugins, not in your data.

AI agents handle a lot of Dynamics questions well. They read your data, run queries, summarize records. But what writes a field, what a plugin really does, how a change cascades, none of that lives in the data. It lives in compiled plugin code, workflow XAML, and flow JSON, the blobs Dataverse can hand an agent but can't interpret. So the agent guesses, or tells you it can't see. Pathix parses all of it, once per scan, and exposes the result as an MCP server.

The whole graph, callable by any agent.

Point Claude Desktop, Copilot, Cursor, or your own orchestration at your Pathix deployment and it answers dependency, security, and inventory questions natively, with structured confidence in every response and no SQL required of the agent. Same scan, same graph, the same deterministic answer, not a natural-language guess over your data. Eighteen typed tools:

The dependencies static analysis can't prove alone.

What the deterministic walker can't prove from the IL, an optional AI sweep proposes as a candidate edge: labeled AI-derived, coupled to its evidence, and net-new. It never overrides a proven edge, and every one is a click from its citation. This is how Pathix catches the dynamic and late-bound writes other tools miss, without the “the AI made it up” problem.

Every plugin, workflow, and flow, in plain English.

Point the optional narration at a component and Pathix decompiles it and explains what it does: its trigger, its logic, the exact columns it writes and reads. Each narration shows the model that produced it and a confidence tier, and it is built to be confirmed in review, not taken on faith.

The plan an agent built from the graph, yours to judge.

Both came out of the cold run with no plan and junior-developer prompting, raw and unedited: the full migration plan, and a developer work-item spreadsheet ready to hand a build team. The plan caught that five of six plugins silently swallow their errors, so the production rollups may already be wrong, and its instruction is blunt: recompute on migrate, do not trust-migrate.

Built from a live Pathix scan of a synthetic demo environment, all six plugins decompiled at High confidence. Behavioral narration is a reading of decompiled code that the plan says to confirm against source before retiring a step. The agent produced a plan, not an execution.

Bring your own AI provider, inside your own boundary.

Choose Azure OpenAI, OpenAI, or Anthropic. Your key is stored encrypted in Key Vault, never logged, and persists across restarts, so there is no single AI vendor you depend on, and you opt environments into AI summarization one at a time. AI is off by default. For sovereign and government deployments, bind AI to a provider inside your boundary, such as Azure OpenAI Service in Azure Government; the exact configuration and impact level are scoped per engagement.

The headless wave is coming to Dynamics. You'll need ground truth to ride it.

As agentic coding makes custom front-ends cheap, more teams will run Dataverse headless, with agent-built UIs on top. The risk is that an agent rebuilding your environment can't see the logic and security model buried in the current one. You can't rebuild what you can't see. Understanding the system as it is built is the first, non-negotiable step of modernizing it, and it is the step Pathix already does today.

Optional, in your tenant, never the source of truth.

The full security posture →