This happens way too often in my experience and I tried a lot of things. If the agent has access to an existing codebase it will always try to stay as close to it as possible. It doesn’t diverge from the initial direction the project/code went into, even if it’s already clear that this direction goes nowhere. Somehow agents aren’t able to detach themselves from existing code to come up with completely new solutions.
Sometimes I feel like it would be best to only give the agent access to clear API boundaries (say public interfaces to certain modules) and let it work out a new system from scratch given the expected inputs and outputs. Then plug this independent solution into the system. Of course it can still overfit these interfaces but it’s less than having access to the entire codebase.
Thinking about it, maybe it’s possible to let one agent extract those interfaces, then use them as grounding for a new session.
One less nice way of achieving the same is to tell the agent once in a while that it should think completely from scratch (from first principles). But this relies heavily on instruction following in the reasoning part, which sometimes works and sometimes fails.
We have this running in production. Can get pretty expensive and slow. We are trying to replace this with cheaper and faster methods that don’t hammer our LLM and elastic search endpoints as much.
tl;dr: There is an open source project named Ignis which bridges the Electron APIs Obsidian uses and makes it a web app. For syncing, both official Obsidian Sync and LiveSync has headless cli.
Sometimes I feel like it would be best to only give the agent access to clear API boundaries (say public interfaces to certain modules) and let it work out a new system from scratch given the expected inputs and outputs. Then plug this independent solution into the system. Of course it can still overfit these interfaces but it’s less than having access to the entire codebase.
Thinking about it, maybe it’s possible to let one agent extract those interfaces, then use them as grounding for a new session.
One less nice way of achieving the same is to tell the agent once in a while that it should think completely from scratch (from first principles). But this relies heavily on instruction following in the reasoning part, which sometimes works and sometimes fails.
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