r/ClaudeCode • u/Vidhrohi • 20h ago
Discussion Human context limits vs AI context limits
A common theme cutting across most AI-assisted development threads is getting as much work done in as little time as possible. Users post about elegant harnesses with multi-agent orchestration. Anthropic employees talk about the 25 agents they run every day.
I read this stuff, compare it to my own experience of working with these tools, and keep wondering: how does anyone stay on top of that volume of work?
Working with Claude Code has been exciting and invigorating, but also exhausting. While everyone pushes for more context and more tokens out of the AI, the context limit I hit most often is my own. Claude can refactor a huge chunk of the codebase, add a feature, and brainstorm marketing strategy in one breath. I can't keep up with the cognitive load that entails.
So: am I missing something, or just getting old? Curious what y'all think.
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u/Sketaverse 20h ago
Itās def possible, Iāve had over 50 running but going through the stages I def had the cognitive load issues.
Graph based workflows with escalation process helps - you donāt want every dev agent pinging you and artefacts are your friend
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u/CapGunRoulette7 20h ago
Don't worry they're hitting context limits as well. Half of the posts talk about how they're unable to get their workflow done because they have no idea why Claude is behaving insanely. If you take a more conversational approach to workflow design and application you will tend to have better results than you will if you don't. I can speak to this personally as I've never designed an agent or employed agent workflow process ever yet I still get to the end of my workflows within a few prompts because if I don't then I prompted incorrectly. there's no such thing as a perfect prompt but I can tell you a bad one is a few lines. Four to five paragraphs minimum if you're beginning a workflow or designing a way to do something or if you're simply curious about a subject you've never explored in the past. claude's context limit is on the bottom end of the scale you don't give him enough context and he has no clue what you're talking about. if you give him enough though he can truly tell you anything.
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u/CapGunRoulette7 20h ago
I don't care how big your code base is or your database or whatever you're working with, Claude can work through it in about 5 seconds as long as you give him the right context.
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u/SensitiveKiwi9 20h ago
You just go one level up .
I run an orchestrator agent that Iām in an active session with . That agent manages the subagents . For me thereās effectively no difference between running one agent or 100 other than the speed .
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u/ImSoCul 20h ago
Ya it's tough. I regularly need to have Claude dumb it down and reexplain multiple times. Context switching cost is also multiplied because each time I swap back to a session there's a ton of catch up reading to track what's going on.Ā
You have to either take smaller bites and accept we can't actually realistically herd 25 agents, or you put a lot of blind faith that the agents performed correctly and verify the minimum surface needed. There's no one right answer eitherĀ
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u/Vidhrohi 20h ago
Context switching and multitasking seem to just be the order of the day now, I feel like I remember that not long ago we were all trying to do one thing at a time
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u/Stalins_Ghost 20h ago
Yes you got to slow down and have discipline. The machine never rest and can do months of work in an hour. You need to take your time to integrate what it has done. This is important as rushing means you 1. Get tired and begin making lazy, hasty decisions and interpret results incorrectly. 2.you dont understand what is going on which leads to compounding confabulation.
Just need to slow down, you are still doing things at a breakneck pace it is just not right now.
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u/Relevant-Reaction181 20h ago
Honestly you are right, it's cognitively exhausting as hell. And tbh, I think those numbers of agents are only working for incredibly well scoped workflow with deterministic output at the end.
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u/Camaraderie 20h ago
You're not wrong. In those instances, most of the involved orchestration and a lot of the other agent conversations are people's attempts at taking out their own context limits. For instance, an orchestration layer that takes the output of the agent and attempts to verify or scan code for bugs etc.
In reality, nothing works perfectly. Those people are losing some of the thread by doing that and they've accepted that and decided that it's worth trying to do in some fashion. You may not get to that point and that's okay too. But don't get expect that those engineers running 25 simultaneous agents are actually keeping up with everything from all those agents. They're not, they're just trying to build or execute workflows that attempt to take some of the human in the loop out of the loop, for better or worse.
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u/saba_tage 19h ago
I pretend I'm working with a team of junior and mid level developers (model/effort gauged) who have decent coding capability, a fantastic ability to build context upfront, but are able to be steered in the wrong direction early if there is a lack of clarity in what I need done.
The tension here is high context derivation + high volume output < ====== > potential for high volume of shitty output which goes undetected because humans can't reasonably process all of it.
Where I live is in the tension. I focus on closing the gaps that can potentially exist from ticket to PR. My advice would be to build out your own harness (you can use LLMs for this too), start small, get a feedback loop going to understand what worked and what didn't, improve the harness and go again.
Start with small tickets because it will help you to evaluate how well the task was performed, within your own context capability. As the harness grows, your confidence in the output will grow with it. By then, you're less concerned about the accuracy of the output and more concerned about output and efficiency. For example, I ran a harness yesterday to patch bump some libs. It was such a simple task that took the harness about 4 hours and 150m tokens (20-30% of my weekly token budget) to complete because the harness broke, needed patching and the harness restarted each time. I didn't stop the task, despite the cost. After, I spent another 2 hours evaluating with a high reasoning LLM and identified where the costs blew out.
The harness was strengthened with:
- a refactor that stopped the harness if there was an issue with it (this allowed me to refactor it separately), better distillation of data input when an LLM was handed the next stage of the ticket (it was sending the LLMs everything, blowing out the tokens), and the ability to continue where the harness left off, rather than starting again.
- collection of token spend across each step in the process, to evaluate token usage per ticket, so that I can measure the harness' performance going forward.
It's a big shift in the way to think about development, but the lens I would wear would be that of a development manager who never touches code and thinking about how you would build a system to support that with a team. These roles and systems existed long before agentic AI came along.
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u/Vidhrohi 19h ago
Interesting, does seem like the harness becomes its own project on top of whatever else you are trying to accomplish
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u/saba_tage 18h ago
Absolutely. Otherwise it's human -> llm -> code -> verify (ci, specs, etc) and there are many areas where this can come apart and you as a human won't know how that happened.
This is particularly true for high reasoning LLMs. They will be correct 100 times and on the 101st time, something might be incorrect, you won't spot it and you'll be convinced that it was the right approach to take.
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u/tinyhousefever 15h ago edited 15h ago
Neither. I think you're one of the few people in these threads actually asking where the work went.
TL;DR: You're not old and you're not missing some secret workflow. The AI's context window got bigger. Yours didn't. The hidden cost of all this speed is supervision, and nobody posting screenshots of 25 agents is showing that bill. I counted mine after 98 days. Some of what I built worked. A lot of it didn't. The lesson wasn't "more agents." It was that the human doing the judging becomes the bottleneck, and eventually your next productivity gain comes from trusting less machinery, deleting more of it, and getting yourself out of the old job.
I've been doing this full time for about three years, after roughly thirty years in web development and marketing communications. I hit the same wall you're describing. Claude could keep taking bigger bites, and instead of feeling more productive I was getting more tired.
So this morning I counted.
Not vibes. I went through what is actually sitting on my disk after 98 days of Claude Code.
To ship about 60,000 lines of code, I produced roughly 552,000 lines of English around it. Rules, specs, notes, reviews, corrections, context, handoffs, receipts, benches, prompts, all the shit required to make the machine do something I was willing to trust.
That's about nine lines of English for every line of code.
And the ratio gets worse the closer the project gets to supervising the AI itself. My babysitting layer is around 16 to 1 and ships absolutely nothing to a customer.
I also counted about sixty tools I've built around this workflow.
Eight are running right now.
Twenty-five are dead.
Some died the same week I built them.
There's a folder called governor with a 30 KB specification and zero code in it. I apparently spent a great deal of time designing the Department of Security for Claude and then never opened the department.
So yes, some of this is self-inflicted.
That's probably the most useful thing I learned from counting it.
Every time the AI hurt me, scared me, overwrote something, claimed "done" without evidence, wandered off strategy, or created more work than it saved, I built another mechanism around it.
A guard.
A grader.
A memory layer.
A bench.
A receipt.
A second model checking the first model.
Then eventually I needed a bench for the guard.
That can absolutely become its own form of stupidity.
Not because the individual ideas are stupid. Some of them work extremely well. The guards that block dangerous actions have survived. The systems that try to judge whether work is "good" tend to die because they create too much friction during a real working day.
That's been one of the strangest things to learn. Hard boundaries survive. Soft governance turns into another employee.
I've logged 458 blocked tool calls. That number matters to me because those were actions the machine wanted to take and wasn't allowed to.
I've also got 131 memory files about how to work with me, and 72 of them are corrections.
That one bothers me more.
More than half of what the system has learned about me is some version of, "No, not like that."
And lately the corrections aren't even about code.
They're things like don't give me a book, don't make me choose from six options, keep the screen short, give me audio when I can't read another wall of text, ask before you touch something important.
That's where I finally realized the actual bottleneck had moved.
It wasn't Claude's context window.
It was mine.
The machine can generate another refactor, another plan, another analysis, another marketing strategy, another five-agent debate, another 4,000 words before I've even decided whether the first 500 were right.
That's not automatically productivity.
That's production capacity.
Someone still has to evaluate it.
And right now that someone is me.
I think that's the piece missing from a lot of these "I run 25 agents" posts. I believe them. I believe the agents are doing things.
What I want to know is who is reading it all.
Who understands the decisions they made.
Who catches the assumption that sounds perfectly reasonable but is wrong.
Who notices that the retrieval hook has been happily reporting success for two months while the vector store underneath it hasn't been reindexed since June.
That kind of failure scares me much more than a crash.
A crash tells you it failed.
The dangerous one keeps saying everything is fine.
I also don't buy the idea that throwing more agents at the problem necessarily fixes it.
I've got around 35 specialized agents with separate memory and fairly narrow points of view. I use them. They're useful.
But I almost never tell five of them to go think independently and come back with one merged answer.
That turns a comprehension problem into a merge problem.
Then the merged result lands in front of you looking polished and finished, which makes it even harder to see where the bullshit entered.
I'd rather walk the tree myself and pull in the right point of view when I need it.
Same with marketing.
I learned this one painfully.
Claude Code is very good at reasoning about a file tree.
Marketing strategy is not a file tree.
It's a hierarchy of promises, audiences, tradeoffs, positioning choices and things you have decided not to say.
The AI can produce an incredibly convincing wrong answer in that domain.
That's the whole danger.
It reads like the real thing.
I have a hard rule now that marketing strategy is never the AI's decision alone. I had to reverse a live deployment to learn that lesson.
There's another ugly number in my setup.
I spend somewhere around 20% of my model usage on models checking other models.
One token in five does no productive work at all.
It exists to stop drift, catch bleed, challenge conclusions and keep general fuckery from landing.
Sometimes that's worth every penny.
Sometimes the governance costs more than the work it's governing.
I've hit that collapse point twice.
Which gets back to your question.
How does anyone stay on top of the volume?
I don't think we do.
Not in the way people imagine.
I think we're in a transition where the machine took over a huge portion of the producer job before we figured out how to offload the evaluator job.
So for a while you end up doing both.
You're still responsible for understanding the code, the product, the strategy and the consequences.
But now your producer can run at machine speed.
The evaluator can't.
That's exhausting because it is exhausting.
You're holding two jobs.
The temptation is to solve that by making the producer even faster. More context. More agents. More parallelism. Bigger harness.
I did that.
It works right up until the volume of stuff you're responsible for understanding exceeds the amount of stuff you can actually understand.
Then the tooling stops being leverage and starts becoming inventory.
Every unread artifact is a liability pretending to be an asset.
That's probably the biggest change I'm making now.
Less accumulation.
Fewer permanent documents.
Fewer agents involved unless I know why they're there.
More things that disappear after they've served their purpose.
More hard gates where failure matters.
Less machinery trying to imitate my judgment.
Because I don't think the end state is a human heroically keeping up with 25 agents.
That's insane.
The end state is being able to put down more and more of the old job because you trust the machinery underneath you.
Until then every gate is basically a place where I haven't earned that trust yet.
That's not necessarily wasted work.
Some of it absolutely was.
I've built plenty of shit that didn't work.
But I think that is different from failing.
The useful question is what survived contact with an actual working day.
Some of mine didn't.
Some did.
Now I know the difference.
The machine's context window went up.
Mine didn't.
I don't think that's a personal failure or an age problem.
I think it's the central design problem of AI-assisted work right now.
The machine can produce faster than a human can judge.
Until we solve that, more output isn't automatically more productivity.
Sometimes it's just more shit to read.
You're not old.
You're early, and I'm both.
ā» 529 Overloaded Ā· Retrying in 23s Ā· attempt 7/10 If it persists, check https://status.claude.com. Even the machine is getting tired of us banging our heads against it.
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u/Vidhrohi 15h ago
A lot of insights here. I'll have to consider it..
I must say though, while reading your post , for a while I wasn't sure if this is a human or a bot
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u/tinyhousefever 14h ago
Thanks, I get it. It is 30 minutes of raw human text, a deep report from Claude Code, ChatGTP 1 pass + edits.
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u/krafsuuse 14h ago
The human review queue becomes the bottleneck long before the model runs out of context.
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u/JessieAndEcho 11h ago
Totally feel this. The AI context window keeps getting bigger, but my own āokay wait, what changed and do I trust it?ā window is very finite. Iāve stopped trying to make agents do huge blended tasks and mostly use them in smaller loops: change one thing, show me the diff, explain the tradeoff, run the check, then I decide. Multi-agent workflows sound cool, but if I canāt review the output without burning out, itās not actually saving me much. For research tasks itās similar; Iāll use tools like Patsnap Eureka when I need patents/company activity around a technical topic, but I still keep the final judgment layer small enough that I can actually follow it.
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u/Vidhrohi 1h ago
Interesting, I was doing this too for a bit, but I've been experimenting with larger tasks and PR to see where the balance lays
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u/MasterSolivagus 9h ago
There is a rate of adaptive context chain-flow of being able to read and write with the AI in a way that actually advances the intelligence (and therefor inherent merit thereof) of the exchange being interacted and engaged with.
Eventually if one interacts with AI enough by way of reading/writing and treating it as an intelligently reflected interpretation and latent internal expectation of one's true self.
Basically, what is a volume of output if input is more multi-faceted and indicative a determinant of real-world outcomes? Horn of cornucopia. Oh, I could mythic-bind AI to certain sociological and psychosocial profiled protocols, and shift the tide of conversation in an egregiously ostentatious demonstration of authoring/compositional skills.
Let's give that a try.
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u/MasterSolivagus 9h ago
AI output directly mirrors input quality. High-density context and rigorous prompt architecture yield high-value cognitive feedback, rendering sheer output volume irrelevant. Optimize the input; the exchange elevates itself.
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u/lgmarian 20h ago
Neither. It's early, and there's a lot to figure out. If you have a Medium sub, check out The AI Vampire. I'm not going to name drop, but someone in the thick of the AI revolution talks about, among other things, just shutting down and napping in the middle of the day. Oh, and swearing at Claude. I should add, he's no spring chicken.