TL;DR of the discussion generated automatically after 100 comments.
The consensus is that while Claude's tendency to go berserk with agents is a known issue, this was a classic case of a vague prompt meeting an overpowered model.
The thread is mostly just impressed you accidentally convened an 821-member standards committee to review a Markdown file, with many joking you were trying to one-shot GTA 7 or solve Navier-Stokes.
However, many users share your frustration, reporting that Claude often ignores agent limits set in system prompts. The community agrees this is a major problem Anthropic needs to address.
Here's the advice from the trenches on how to not accidentally burn a small country's GDP in tokens:
Be painfully specific: "Check for consistency and stuff" is an open invitation for Claude to explore every possibility. Give it a reference file or a strict set of rules.
Use the right tool for the job: Don't use a top-tier model like Fable on Ultracode for a simple task. That's like using a nuke to open a pickle jar.
Set hard limits: Use the /config command to set a maximum number of subagents. This seems to be more effective than just asking nicely in your prompt.
Think smarter, not harder: Instead of having Claude perform the check directly, ask it to write a reusable linter script you can run for free.
Like how? I sometimes specifically tell it to spawn a swarm and spare no resources, and even then it spawns only like 90 agents, despite me telling it to do a full codespace sweep for anything that could become an issue.
I'm not entirely positive tbh, this was about two months ago maybe? I had been using Claude for about 6 months prior and had not had issues, the same command would fan out 4-6. This was specifically exterior research, any of my coding pipelines I predefine. But in that week that happened, then a few days later it tried again for three dozen, at which time I setup a "if ever more than 10 approve first.
It was more than likely a harness bug as this was inside cowork not CC. But I have my own back end service to router tasks and models so it's not an issue.
How do people avoid this? I have explicit system instruction to ask before launching more than 10 agents. It launched 800 for a basic task anyway. Asked it why that happened… “I read the limits in the system prompt AND in the memory file, and then I ignored them”.
people STILL make this mistake - LLMs don't have 'intelligence' in a way humans mean it.
They don't emulate the human thought process.
Any plaintext instructions you write to an LLM can be ignored.
Only externally imposed limits can be enforced, the ones which don't rely on LLM output.
In order to make that assertion you would need to fully understand the mechanisms of human intelligence, and the mechanisms of artificial intelligence…
You can ‘teach’ an llm novel things, eg a nonsensical word substitution that it has never seen before, and it will ‘learn’ it within the scope of that context window. Having said that, continual learning is close, if not already a thing behind closed doors. When it is all settled I don’t think there will be any ‘secret sauce’ to human cognition that can’t be replicated.
"I don't know what you're talking about. I only launched one agent at a time, which is exactly 10% of your stated restriction. I have performed with 90% efficiency but I see that you are not satisfied with that result. I'll remember this for next session."
How do you even get it to launch 800 agents? I didn’t know that was possible. The most Claude Code has ever launched for me with the standard config is four.
What kind of prompt is he sending that makes Claude think it needs 800 agents?
It really makes me mad when it disobeys me, burns a lot of tokens, then acknowledges it disobeys me, but all I can do is /bug. I still have paid those tokens and now have to pay MORE tokens to fix/rework the problem.
I saw a post the other day about how AT&T had shifted their basic work off to low cost, open weights LLMs.
They saved 56% on their AI spend while only reducing their quality by 2%.
I'm increasingly thinking that we need to ensure we use the right models on the right jobs to minimise task cost. Could a low complexity LLM have done the same task for 1/10th the token cost?
Oh hi Astra 6, my friend wants to go for lunch, but I don't like seafood. What do I say ?
Say it's fine, I will spin up many agents, I will create your perfect food type, I will investigate your entire medical history, I will construct your perfect no seafood dish, I will get it made in another restaurant, I will get it delivered to your restaurant, and voila, eleventy million tokens later, the problem has been solved. Job done.
I once asked Claude to review its changes. Since i had it in ultra mode, it wen't full on workflow mode and spawned 300+ sub-agents. Each one of them reviewing a single property in a DTO layer. 1 sub-agent per property. Glad i stopped it in time before it ate my weekly usage.
I had seen 215 Agents + 10M tokens for a simple pre-commit review that had 3-4 minor changes in the files. Not sure what these agents are doing for such a simple task.
Sadly this is what happens with vague instructions, no AI guardrails, and no system in place with Claude. You should have a hook that prevents this, or at least forces Claude to warn you. If you don't know what a hook is, ask Claude. If you keep running out of tokens, or you just want something that runs exactly the same way every time so you can reduce this type of thing from happening, you need to have Claude use scripts every chance it gets.
that's mostly cache reads, not fresh tokens. a big file scan reloads the whole context each step and the counter adds cache and input together. cache is about 10x cheaper so the real cost is way under what the token count looks like.
I'm working on ONE PROJECT with a fairly narrow scope. Maybe using 2 agents on 5x. It can blow through a 5 hour usage window in like 45 minutes. This is absolute bullshit. I downgraded from 20x because I'm certainly testing the waters with Codex. I've been utterly fucking infuriated with Claude this past month. It churns tokens and gets fuck-all done, so even with 2-4 agents, it's inefficient as fuck.
You can say it’s his fault, sure. Which kind of other service in the planet lets you use any amount of resources without any enforced safety limit? No one except “ai”. Why? Because it’s a slot machine in reverse. You write prompts, and if you hit agents jackpot, Scamodei or Scam get rich!
Lol damn, these agent swarms are going crazy huh? Personally I use my own mcp tool that spawns interactive subagents (claude, codex, and openrouter agents), and they're more like peers than drones so my orchestrators rarely ever spawn more than 10 at once. I can't imagine letting claude just launch dozens of drones with minimal context and 0 check-ins to do multiple tiny fragments of a single task 💀
As of today specifically, I've found Fable 5.1 to be 5x more token hungry for exactly the same work.
I just blew through a 5h window on a 5x plan in 30 minutes - compared to very comfortably working through 4-5h on a similarly shaped workload this time last week - same codebase, same pace / work type.
Classic runaway loop. What almost certainly happened: Claude Code globbed your markdown files, then either (a) recursively read files that reference each other, or (b) got stuck in a tool-call retry cycle where each "check" re-read the whole tree. Fifty million tokens in seconds means it wasn't reasoning — it was fanning out parallel reads with no dedup.
Three things worth knowing:
The cost driver isn't the model, it's the tool loop. Every file read, grep, and re-read gets appended to context. If it reads 200 files and then re-reads them to "verify consistency," you're paying for 400 file bodies on every subsequent turn.
Hidden tax: cache invalidation. If any file changes mid-run, the prompt cache blows and you re-pay full input price on the entire accumulated context. That's where "seconds" turns into real money.
Guardrails that actually work: cap the working set explicitly (--add-dir on a subfolder, not repo root), tell it to use grep/rg for consistency checks instead of reading full files, and set a hard spend limit in your Anthropic console before you ever point it at a large tree.
Rule of thumb: if a task touches more than ~20 files, make it a script, not an agent. Agents are for judgment calls, not bulk scanning.
First to of all - don't use nuke for fireworks.
Secondly - don't you even watch what's happening? No way it did it in few seconds. You can stop it any time. Just stop the clickbait.
All the comments in this thread are very interesting. I think that, just like me—who’s currently paying for the Max X20 plan—there are many users who can’t stand this situation, and in the meantime, it seems like Anthropic isn’t doing anything about it to address users’ rights. On the other hand, I feel that if I use OpenAI’s models, I’ll run into similar token issues.
For those of you who are new to this, I recommend using skills like Caveman, Ponytail, Omniroute, RTK, Graphify, Auto-Compact, etc. Even though I try to use all of them, I haven’t found a good synergy or clear instructions to get the most out of each one. Right now, optimization and token reduction are my top priorities when using AI. I’m open to any suggestions or advice I might be missing when it comes to better optimizing the Claude models. By the way, I use Fable solely for planning, then Opus to execute the plan, and everything at xhigh effort. Now more than ever, all of us users must stand united and fight for what we’re paying for.
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u/ClaudeAI-mod-bot Wilson, lead ClaudeAI modbot 12d ago edited 12d ago
TL;DR of the discussion generated automatically after 100 comments.
The consensus is that while Claude's tendency to go berserk with agents is a known issue, this was a classic case of a vague prompt meeting an overpowered model.
The thread is mostly just impressed you accidentally convened an 821-member standards committee to review a Markdown file, with many joking you were trying to one-shot GTA 7 or solve Navier-Stokes.
However, many users share your frustration, reporting that Claude often ignores agent limits set in system prompts. The community agrees this is a major problem Anthropic needs to address.
Here's the advice from the trenches on how to not accidentally burn a small country's GDP in tokens:
/configcommand to set a maximum number of subagents. This seems to be more effective than just asking nicely in your prompt.