Keeping a data catalog or asset inventory up to date is one of those tasks that everyone agrees is important and almost nobody has time for. Architects, data engineers and platform teams know that an outdated documentation of relationships between systems is worse than an empty one, because people start trusting information that no longer reflects reality.
This is exactly the kind of repetitive, detail heavy work that AI Agents are good at, and it is why more organizations are starting to use them to keep their asset inventories current automatically.
The idea sounds simple at first. Point an AI Agent at your data lake, your automation platform, or your integration layer, and let it figure out what exists, document it, and push that information into Asset Navigator. In practice, this runs into a wall almost immediately.
To analyze data lakes, flows and pipelines properly, an agent typically needs broad access. In many cases that means something close to admin rights on the source systems. For a large corporation, that is a hard sell. Security and compliance teams are rightly cautious about giving an external platform that level of access, even if the intention is purely to read metadata and generate documentation.
The risk is not really about what the agent will do with the access. It is about the principle of handing out broad permissions to a third party tool, and the audit and governance headaches that follow.
This is the reason many promising cataloging projects stall before they even start. The value of automated documentation is obvious, but the access model required to get there makes security teams nervous.
We built our platform with this exact concern in mind. Asset Navigator can run on premise, or inside the customer's own secure private cloud. That alone removes a lot of the hesitation, because the data and the processing never leave the organization's own environment.
But the real shift happens through our API. Instead of granting our platform any access to your systems, the corporation creates and manages its own AI Agent, inside its own environment, using its own access controls.
That agent reads whatever it has been authorized to read from the source systems, and simply calls our API to upload the results into Asset Navigator. We never touch the source systems at all. The corporation decides what the agent can see, how it authenticates, and what gets sent, and all of that stays entirely within their own security decisions.
This turns the conversation with security teams from "please give our platform access to your infrastructure" into "we are building and running our own agent, under our own rules, and it only ever pushes data out through a defined API." That is a much easier conversation to have with a governance board, because the access decision never leaves the corporation's hands.
A few concrete examples make this easier to picture.
Documenting Power Automate flows automatically. You can set up an AI Agent or a scheduled flow that reads every Power Automate flow inside a production environment and uploads the results into Asset Navigator on a daily basis.
It does not stop at listing flow names. The agent can read the underlying logic and use AI to generate actual documentation, including which connectors are used, what triggers the flow, and how it fits into the broader automation landscape.
Instead of relying on someone remembering to document a flow after building it, the documentation writes itself, every day, without extra effort from the people who built it.
Cataloging views in a Data Lake. In other cases, the scope can be much narrower. An agent can be configured to read only the views inside a data lake, without touching raw tables or sensitive data underneath. Those views get uploaded into Asset Navigator so architects and engineers can see at a glance what already exists and what can be reused.
This is often exactly what is needed to reduce duplicate work, since a large share of new data pipelines are built because nobody knew a similar dataset already existed.
In both examples, the organization defines the boundaries up front. The agent works within those boundaries, and everything it collects becomes visible and searchable inside Asset Navigator, kept fresh through regular automated runs rather than occasional manual updates.
The technical capability to read flows, pipelines and data lake structures with AI has existed for a while. What has been missing is a way to do this that corporate security teams are comfortable approving. By keeping the AI Agent entirely inside the corporation's own environment, and letting our API only receive what the corporation chooses to send, this barrier largely disappears.
The result is a catalog that stays accurate without turning documentation into a full time job for someone, and a rollout that security teams can actually get behind, because they never had to give up control in the first place.
If your organization has been holding back on automated cataloging because of access concerns, it is worth looking again at what an on premise or private cloud setup with a governed API can offer. The gap between wanting good documentation and being allowed to build it might be smaller than it looks.
Curious what's hiding in your own team's dependencies? Asset Navigator maps how your apps, data lakes, flows, and AI agents connect — so the next "simple" ticket doesn't turn into an incident report.
