Smart agent to orchestrate cheaper agents for labour.
Dumber agents to be very explicit about asking smarter agents for requests that require deep information.
This is a thing - the SOTA models really light up the more information you can provide them, and humans are too lazy / incapable of doing that in every prompt.
Entirely depends on what you are trying to do.
Edit: you can also use a smart agent to ask another smart agent, obviously, which is good for personal workflows.
But there are definitely cases.
GPT Pro is an absolutely spectacular model, that almost cannot be used without 'prompt help'. Or rather, it can only be effectively leveraged by very intricate prompting, which requires the help of another agent.
I typically use Claude Code to write prompts for GPT Pro , and cut and paste the prompt in, and let GPT Pro work for 20 minutes, then take the response back to Claude Code and work with Opus to consider the results.
Opus is 'dumber' (nominally) then GPT Pro.
What's the best method to have a 'smart' agent just.. ask itslef?
You may use https://github.com/pingdotgg/t3code as a wrapper above claide/codex/pi/... harnesses, or a proxy below (forgot which one, as agent what theo uses for balancing multiple claude subs)
Asking Claude to make prompts is like a Math teacher stuck working at a cafe about Math problem, the just light up.
They are very good at at it.
* Orchestrator: Opus 5.5
* Feature lead: GPT 6.1 Sol
* Worker: Muse Spark 1.3 Contributor
GPT 6.1 Sol is surprisingly efficient too, I've been impressed with it.
My orchestration skill setup is https://github.com/beefsack/processed-beef/
Does anyone have any evidence that having the most powerful model being the orchestrator, and other sub agents being progressively less powerful, is optimal? Is this just common sense or it has been measured somehow?
For me it could be that this scheme may make the code itself poorly written, verbose or faulty
In my setup, the orchestrator also drives a lot of the design and planning, and the less capable models get inputs from the more capable models by responding back up the chain. The more capable models also review everything that is produced by the cheaper models.
I've also had systems that invert this and allow cheaper models to call out to more capable models when they need to, but in my observation I found them quite bad at knowing even when to call out and I find wrapping the cheap models a bit more reliable in that problems tend to get caught before they bubble up to the user.
I'd be interested to know what the difference in cost is in this setup on building a complex feature vs using Opus directly.
At this point if I could do anything with zero effort or time, I would get Opus 5.5 to make a grand scheme, handoff to the local model, and give it ability to reach out to Opus again.
That way you can do old school intent matching and rule-based bots that work super well, and only shell out for deeper work.
Claude will write some scripts to call the LLM API and use it like any other tool.
I haven't found a pressing need for anything more complex than that. My multi-agent setups tend to be a pile of bespoke scripts per project anyway, an MCP server is only gonna get in the way. But I suppose this is because I self-host the models, if you're using OpenRouter or whatever it may need more complexity than a curl call to an unauthenticated port
So with the gdrive app for my OS it just looks like a local file path. Had the local agent learn to parse it.
I still prompt in AI studio direct as needed; local agents see the log change but cannot push back to Gemini their result.
Few reasons:
AI Studio is available where ever via browser, no VS Code or my usual vim setup to miss. I can switch between projects there as an idea hits and the agent switches up the local repo path and session history.
AI studio can search web, etc, etc already so my agent is less complicated with addons in general
Also using AI Studio between local agent turns has been my "human in the middle" approach; to have an explicit gap in the process and check each turn.
I only have a 24GB 3090 most models also take longer locally and I never hit the free tier limits with Gemini :D
Though the latest Meta model is twice as fast at 30B for me as other similar size models. 1-2 hour code gen and QA of build errors is now 30-50 minutes to parse latest changes from Gemini and get a successful build. The Meta model is pretty great at "fill in the middle" from a well structured plan.
Overall this loop feels like a normal workday pace. I freelance so I have a lot more say over that.
Ps: as you won't use your proxy to distill or alter claudecode requests to anthropic you should be compliant with anthropic rules.
Both are, I think, valid options. Depends on the workflow you're looking for.
https://opencode.ai/docs/agents/
There are a lot of ways to do this if you stop using Big Ai harnesses that make it difficult to use models besides theirs.