We've tackled this problem slightly differently where I work. We have AI agents contribute in a large legacy codebase, and without proper guidance, the agents quickly get lost or reimplement existing functionality.

To help the agents understand the codebase, we indexed our code into a graph database using an AST, allowing the agent to easily find linked pages, features, databases, tests, etc from any one point in the code, which helped it produce much more accurate plans with less human intervention and guidance. This is combined with semantic search, where we've indexed the code based on our application's terminology, so when an agent is asked to investigate a task or bug for a specific feature, it'll find the place in the code that implements that feature, and can navigate the graph of dependencies from there to get the big picture.

We provide these tools to the coding agents via MCP and it has worked really well for us. Devs and QAs can find the blast radius of bugs and critical changes very quickly, and the first draft quality of AI generated plans requires much less feedback and corrections for larger changes.

In our case, I doubt that a general purpose AST would work as well. It might be better than a simple grep, especially for indirect dependencies or relationships. But IMO, it'd be far more interesting to start looking at application frameworks or even programming languages that provide this direct traversability out of the box. I remember when reading about Wasp[0] that I thought it would be interesting to see it go this way, and provide tooling specifically for AI agents.

[0] https://wasp.sh/