You’re right about the harness being the issue. It’s really down to giving it functions specific to your use case that will let it surgically read/modify files, rather than needing to consume entire project folders. I built my own and for Python files some of the most helpful functions I provide are equivalent to:

inspect_function(filename, function, class)

replace_function(<same>)

call_graph(<same>)

And a few other convenient ones. Beyond that it’s trickery like if a function returns more than N lines I omit the result and auto-reply “Your function call was too verbose.” Typically that’s stuff like recursively listing every file in a repo to “see what it’s working with” or similar. When it emits the next call in response to it I clip the previous attempt (and my response) off the conversation and attach the new call/result as if that’s what it did in the first place. I also log that event so if the same type of thing happens often enough I’ll create a special function to address it, or modify an established one so it’s not tempted to do it again.

Language models don’t know what they know, they know what has been said. Even if you give it an entire Python environment it won’t reach for AST, but if you give it a function called Python_AST() it’ll use it every time.

I’ve never seen an off-the-shelf harness that approached it that way.