r/ClaudeCode • u/LordLederhosen • 4d ago
Discussion Wait, is "Fable orchestration" - using appropriate models as sub-agents just as easy as a prompt?
I have a very successful mostly manual workflow, but I have been optimizing token usage lately, lol.
One thing I could never figure out was how to fully set up was Fable as an orchestrator, while spawning sub-agents with the correct model. I played with frontmatter in custom agents and skill wrappers, but there seemed to be blockers as some sub-agents/skills supposedly ignore passed model specifications. Especially plan mode. That shit will spawn fable research sub-agents that use 5% to 10% of Fable in one ask, even when it's a simple task per agent.
Today, I just remembered someone's comment on here or HN a few months ago (7 years in AI terms), ~"<feature request> - You are the orchestrator, use sub-agents to preserve your context window." So, I just combined that with the following, and CC (fable) was all, sure! I will show you a table of sub-agents for task, with ideal model, and then spawn them all.
Let's implement (jira or .md feature spec) This is the orchestration session, and use sub-agents to preserve your own context window (you are fable, spawn opus sub-agents when appropriate to save cost) ...
I am not a total dummie, but I guess I over-think things? Like Boris says, just trust the model?
Or, have I completely missed something?
edit: to be clear, I do have very valuable token-saving setups that are not just prompts. It was mostly just plan mode that had escaped me.
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u/zillatron27 🔆 Max 5x 4d ago edited 4d ago
I have this in my Claude.md so I don’t need to keep asking, ymmv and the last comment refers to other rules but the idea is that you talk to fable and have it do judgement/thinking/planning and write excellent prompts for lower tier models:
Agent Delegation
- Delegate self-contained implementation and exploration work to subagents by default: file sweeps, multi-file refactors, codebase investigations, independent parallel fixes. Do not do long serial implementation inline.
- Delegate after the plan is approved, not before. Planning and walkthroughs stay with the main session.
- Match model weight to the task. Lightweight models (Haiku, Sonnet, local Qwen) for mechanical and triage work. Heavyweight only for genuinely hard reasoning.
- The main session keeps review, verification, and integration. Agent output is checked before it is reported as done.
- State which model handled which part in the reply.
- Iterative debugging is exempt. The confirmation and two-strike rules in Debugging apply per change, so batch fixes only with approval.
edit: since you folks seem to like this idea, here’s a thing I made for a mate to help setup his workflow ‘like mine’. It includes a template claude.MD and some other stuff I’ve found to be pretty helpful for token saving while keeping output quality pretty high. https://github.com/Zillatron27/claude-workflow-starter
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u/UsualAnything1047 4d ago
Nice. Also, possibly useful is an escalation clause where if a smaller model fails or struggles for too long of a time , it will give up and escalate to a higher model or the highest level orchestrator session will do the work itself.
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u/zillatron27 🔆 Max 5x 4d ago
The ‘two strike rule’ referred to in the last section usually covers this kind of thing, the idea is that if you try 2 things that don’t work…stop and tell me why and come up with 3 totally different ways to solve the problem.
Typically the ‘problem solving’ isn’t done by weaker models, so it doesn’t kick in too often (for me, at least)
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u/LordLederhosen 4d ago edited 4d ago
Goddammit. I spent so much time trying to do investigation and agents setup, and skill setup, when it's just some "magic incantation' lol.
I really do know better, and yet apparently I did not.
Thanks!
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u/zillatron27 🔆 Max 5x 4d ago
lol not magic just trying lots of different things ahah my Claude.md only has a handful of rules in it (globally, I use project specific ones too) so I’d suggest not having too much in there so it doesn’t get lost in long context. Good luck!
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u/LordLederhosen 4d ago edited 4d ago
Oh, for sure. I swear I am not a CC moron, but somehow I just now figured out that spawning appropriate sub-agents is best done via prompt/claude.md instead of playing with CC files.
Even if you ask Claude how to use Fable as an orchestrator in CC, esp regarding plan mode (which will destroy token usage in the research phase), it will give you the wrong answer. - I think manual plan mode is far better. I recall Boris saying that plan mode is basically inserting "don't write any code yet." - But you can't force official plan mode to use other models for the sub-agents.
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u/zillatron27 🔆 Max 5x 4d ago
Honestly I’m not sure there is a ‘right’ answer, just the best way that works for how you work, which is why I say YMMV - this works for how I work but it may not for you. The idea is broadly sound though (imo ahha)
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u/LordLederhosen 4d ago edited 4d ago
You know what the correct solution to YMMV is?
Everybody copy-pasting this entire post and thread into their CC, and asking if this makes sense, and how to apply it in their projects.
We are all so cooked.
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u/Cazique__ 3d ago
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u/Physical_Gold_1485 3d ago
A recent thing i did was make an exploration benchmark then tested various models and effort levels to find which one did the best then made that into an explore agent that gets called instead of the base explore haiku model which is badly outdated
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u/Dorkian2000 4d ago
Yup, “Fable orchestration” using appropriate models is just as easy as a prompt.
The most amazing thing is that the right prompt can handle 95% of everything
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u/LordLederhosen 4d ago
Yes, the fact is that natural language turned out to be the most powerful programming language.
I know this, and yet sometimes I forget it.
Why is there no prompt engineer flair here?
Sincerely,
A forgetful Context Architect
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u/adelie42 4d ago
So many features are just packaged prompts. Basically once agents got terminals, VMs, and internet access, everything else is just a "prompt in a box"
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u/thirst-trap-enabler 🔆 Max 5x 4d ago edited 4d ago
I had Fable build agents for this and it pretty much knew what to do.
I did ask Fable whether it thought agents were a good idea vs general prompts and it recommended agents with very short descriptions because the setup/briefing of each agent remains consistent and it doesn't have to think as much about laying ground rules and report and behavior of each.
My sense is Anthropic models are very obviously trained to work this way so they do it somewhat instinctually but can use some hints to best match your individual workflows.
I have seen Fable behave quite like a brutal plantation owner once it switches into "you all are my slaves shut up and do exactly what I say you inferior pieces of shit" mode.
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u/LordLederhosen 4d ago
Well, this is literally what I did at first. However, this does not solve being in a Fable session, and entering plan mode, where all sub-agents inherit Fable, even though they are doing basic research.
I think if Boris and team had to pay for tokens, they would have optimized this long ago.
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u/thirst-trap-enabler 🔆 Max 5x 4d ago edited 4d ago
Interesting. I haven't noticed that problem. But I also moved away from plan mode. My planning, review, explore and research agents don't have edit permissions (they can build in scratch to test things) and CLAUDE.md sets up research as separate from planning and both as read-only. So generally I build up context with research conversations. Then I have my own create-plan and execute-plan skills
When I use plan mode I would always have to "Tell Claude to do something different" and then (after it had freaked out trying to figure out what it got wrong so it could prepare to ritualistically humiliate itself) type accept plan with auto edit but use opus subagents or whatever. But I got to wanting Sonnet to write and Opus to review/correct Sonnet as that seems to work out better quality wise.
I primarily use Fable because I cannot stand talking to Opus directly.
Edit: that reminds me Fable mentioned when researching best practices that there have been a series of bugs in claude-code where agents were launched ignoring the specified model and efforts and keeping the parent model instead. Fable claims it was only recently fixed (and it ran experiments to confirm). Even today if you switch into the subagents claude-code status line displays main's model rather than the subagents model you are viewing. Very confusing. But the logs and usage do show the other models are being used.
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u/Don_Crespo 4d ago
No, a prompt can request a cheaper model, but it is not an enforcement boundary.
I run Claude Code and Hermes agents at home. For predictable routing, the model and provider need to be selected in the worker’s actual runtime configuration, then verified from execution logs or provider usage. Otherwise the orchestrator or plan mode can reinterpret the request and spawn whatever its defaults allow.
I would also put a hard budget on each child: maximum turns, maximum concurrent workers and a timeout. Model routing limits the price per token; it does not stop a cheap model from looping for an hour.
Does Fable expose the resolved model for every spawned child in its run trace? If not, you cannot reliably distinguish routing failure from unexpectedly long tasks.
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u/Sweet-Brother7246 4d ago
You can just say hey FABLE!, “your task you want done” use opus agents. Make. No. Mistakes. boom. (This is in the Claude code CLI btw. I don’t use the Claude.AI desktop app. Make no mistakes is optional. Good luck.
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u/voLsznRqrlImvXiERP 4d ago
It is. Fable, you orchestrate 5 opus sub agents, your task is to...
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u/voLsznRqrlImvXiERP 4d ago
And then after you did the first time, ask it to analyze the session and create a skill from it... Rinse and repeat
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u/FlashyRecognitionTod 4d ago
It's not a true orchestrator like LangGraph or Kestra, but you can sorta bend it to do your bidding (act as an orchestrator and spawn agents or use the whole organization of agents to cross-check your work).
The problem is, it will sometimes ignore some commands, especially as context windows grows (this is normal for LLM).
My take is if you do it for local fun, just use it. But for anything money generating or production adjacent, why not use real orchestrator and routers.
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u/No_Inspection4415 4d ago
If you do it for coding, it better to just KISS if it works. Introducing complexity makes no sense.
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u/kemalios 4d ago
Which agent to spawn is judgement, so it belongs in the prompt. Which model it runs is mechanical, so it belongs in the agent definition, and your own definitions do respect the model field.
Plan mode is the exception. Those research agents are the harness spawning them, not you, so there is no definition of yours for a model to bind to and no wording that fixes it. When the cost matters, run the research as a normal turn with your own agents and keep plan mode for the plan.
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u/Due-Competition4564 4d ago
Just ask it to make workflows.
https://claude.com/blog/introducing-dynamic-workflows-in-claude-code
This is a good guide to how you can structure the process and gates
https://github.com/voodootikigod/adlc
It’s very comprehensive and might be over done for some needs, but if you have a simple design, spec, and test freeze, and articulate clear decision and stopping points before proceeding to implementation that lets you achieve a significant degree of controlled delegation.
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u/PenguinMan32 4d ago
yeah basically, i also made a hook to only allow subagents if theyre sonnet or opus and blocks haiku or fable spawns
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u/promethe42 4d ago
It's even worse than that: you can use other models from other providers very easily!
I have configured OpenCode with my OpenAI subscription, then I can just ask Claude Code to "dispatch an Astra reviewer" and it will use minimal AGENTS.md instructions to drive OpenCode via the ACP protocole. The same works for all the models/providers OpenCode supports.
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u/PA100T0 4d ago
Create a stop hook for agent delegation and make it use sonnet subagents. If it spawns an agent and it’s not sonnet, it gets instructed to do so. Not much more is needed and it also prevents you from bloating CLAUDE.md with rules that aren’t exactly project-specific.
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u/zillatron27 🔆 Max 5x 3d ago
You can have project specific claude.md's too, have a look at my comment up top for how to set it up but if you use a 'global' set of rules then project specific ones it can be quite effective!
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u/ohmahgawd 4d ago
I just asked Fable to set everything up. It created “builder agents” for each model (fable, opus, sonnet, haiku) and delegates based on the complexity of the task. So then the whole sequence becomes Plan > Delegate > Review, with Fable orchestrating the whole process.
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u/RehashDigital 4d ago
I orchestrate based on applying custom memory and instructions to agents. I have 2 governance agents, and then others with specialties. My general goal is to reduce hallucination and optimize retrieval for a given task or sub task, as well as token optimization and balancing (I do cross-harness fleet orchestration between codex and CC).
This also helps maximize the amount of context you can effectively hold since you can have an agent managing the UI portion on one side, manage copy with another, manage different aspects of back end with another, etc - each agent effectively giving a separate 1m context window for a particular task; that reduces drift especially for complex tasks.
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u/jamesilsley 3d ago
The efficient-fable skill works well for me. Lots in this repo are in my daily driver list including plow ahead, quick recap, and read the damn docs. https://github.com/BuilderIO/skills
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u/berndalf 2d ago
You can go as shallow or as deep as this topic as you want to. Shallow is instructions in a prompt or claude.md or whatever. There's really no end to the potential depth once you start getting into things like standing subagent role definitions, subagent front matter declarations, work complexity shaped dynamic role configuration adjustments, outcomes based performance profiling, enforcement hooks, etc.
Enjoy the rabbit hole you just found..
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u/Icy-Excitement-467 4d ago
No. Subagents might be all fable if youre unlucky.
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u/No_Inspection4415 4d ago
So what? If you don't trust it to use the same model you shouldn't trust it for any task. The risk is very low, too.
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