I think this space is very untapped. Models are interesting, but I am absolutely obsessed with some things I've been researching/working on for the past few years:

Fractal tool discovery: tool taxonomy where an agent can "drill deeper" to find what specific tool it's looking for. Helps if/when polluting context with a zillion (mostly unnecessary) tools.

Leveraging splay trees: this is my favorite data structure and I think relatively unused in the context of agents/harnesses. A lot of times, recently-used workflows/tool-chains will be used again, so having those at the top of the search hierarchy is an awesome optimization.

Virtual containerized notebooks: models working in sandboxed (WASI) Python notebooks is incredible. Even local models (if given enough time) will usually converge on a good solution. Being able to mount tools/resources/fs is again, imo quite untapped. Some problems here are running native things (thing numpy/pandas) in containers is a nightmare (or impossible).

Anyway, happy to see other folks seriously doing stuff in this space. If anyone wants to collaborate on anything don't hesitate to reach out :) I'm also actively looking for a job or some contract gigs.

Fun times ahead.

> Helps if/when polluting context with a zillion (mostly unnecessary) tools.

Ugh, I can't help but respond to this one point. The fact that this is even an issue in the current year just tells us how screwed the software field is in a lot of ways. I don't mean that in existential terms, but of how divided we've become in terms of what's happened to human reasoning. On the one hand, you have people who apply deep thinking to develop the sort of approaches you described, and there's the exponentially growing segment of not-even-programmers who seem to never ask themselves whether any of their ideas have any sort of consequences.

Take MCPs for instance. Sure, I guess it can sometimes make sense to have a stateful API that is optimized for agents. Yet, more often than not, these MCPs frontload a ton of context where it's not needed, and solve problems where none existed. Merely sticking an API (MCP) in front of an API (CLI, REST, GraphQL) without a benefit that can be explained in a single sentence is lunacy and demonstrates a real lack of complex thinking.

i have found agents to be excellent at using CLIs, which are fractal-like. i built this reddit ads api cli and my agent immediately starting introspecting it in a "drill deeper" manner:

https://github.com/genei-Ltd/reddit-ads-cli

Have you had workflows/benchmarks to test this on? I'm primarily interested if there are real use cases that would benefit.

I've also explored s/Fractal tool discovery/Skill tree approaches, seems to work pretty well when you stick the equivalent of XREFs in the frontmatter.

What makes it fractal?

Fractal tool discovery is a fascinating idea! Have you worked on implimenting this into any agent harnesses already to any success? My first impression is allowing the agent to fork itself, not unlike launching a subagent, then returning it's response back to the main agent.