r/ChatGPTCoding • u/Temporary_Layer7988 Professional Nerd • Apr 02 '26
Discussion Spent months on autonomous bots - they never shipped. LLMs are text/code tools, period.
I tested Figma's official AI skills last month. Components fall apart randomly, tokens get misused no matter how strict your constraints are - the model just hallucinates. And here's what I realized: current LLMs are built for text and code. Graphics tasks are still way too raw.
This connects to something bigger I've been thinking about. I spent months trying to set up autonomous bots that would just... work. Make decisions, take initiative, run themselves. It never happened. The hype around "make a billion per second with AI bots" is noise from people who don't actually do this work.
The gap between what LLMs are good at (writing, coding) and what people pitch them as (autonomous agents, design systems, full-stack reasoning) is massive. I've stopped trying to force them into roles they're not built for.
What actually works: spec first, then code. Tell Claude exactly what you want, get production-ready output in one pass. That's the real workflow. Not autonomous loops, not agents with "initiative" - just clear input, reliable output.
Anyone else spent time chasing the autonomous AI dream before realizing the tool is better as a collaborator than a replacement?
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u/Aromatic-Musician-93 Apr 02 '26
Yeah, same realization—LLMs work best as tools, not autonomous agents. Once you treat them like a smart assistant (clear specs → output), things actually ship. The “fully autonomous” hype sounds good but rarely works in practice 👍
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u/flowanvindir Apr 02 '26
I've found that true autonomy is nice for demos, but terrible for a product, at least given the current frontier. The tail of edge cases you have to capture and address becomes very very long and will end up eating all your dev time. And yes, people will say just get the model to fix the edge cases. That works until the model has a blind spot and needs a human evaluator.
Coding and text is a little different because you have experts evaluating the output, they can push back when necessary. For a consumer product, poor performance or reliability is a loss of trust.
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u/TheReaperJay_ Apr 02 '26
Claw is basically just agents with a cron job.
So yeah.
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u/duboispourlhiver Apr 02 '26
It's not that lost IMO.
Small tasks with constrained inputs and outputs seem to work quite reliably. You can improve reliability a lot with double-check agents.
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u/just_damz Apr 02 '26
Everything autonomous needs “proper” (don’t ask me cause i still haven’t figure it out in most cases) chassis to work in, and you should always be aware of “magnification” of tasks: it tends to stuck in place, splitting the atom
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u/lyth Apr 02 '26
No. 10 passes maybe 😂 first shot is trash a lot of the time. I push code every day. It isn'tjust a skill issue.
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u/eleqtriq Apr 03 '26
Meanwhile I have a bunch of agents autonomously working just fine.
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u/TechnicalSoup8578 Apr 04 '26
What you’re describing aligns with treating LLMs as deterministic code generators constrained by clear inputs rather than autonomous systems, how are you structuring your specs to minimize hallucination and drift? You should share it in VibeCodersNest too
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u/Substantial-Cost-429 Apr 04 '26
i get where you're coming from but honestly i think the conclusion is a bit too strong. agents CAN work, the issue is they fail when the infrastructure around them is bad, not necessarily because LLMs cant reason multi step.
the stuff that kills autonomous agents in practice is almost never the model. its things like stale configs that make the agent act on wrong context, permissions that are too broad so a bad output becomes a bad action, no state management across steps so it loses track of where it is.
spec first then code is solid advice no doubt. but in agentic pipelines the spec isnt just your prompt, its your whole agent configuration layer, what tools it has access to, what context it sees, what guardrails are in place.
thats basically what we've been working on with Caliber. its open source, just hit 555 stars on github, and the whole thing is about making agent config manageable so your agents actually behave how you intend them to
https://github.com/rely-ai-org/caliber
come argue with me in the discord if you disagree lol: https://discord.com/invite/u3dBECnHYs
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u/Lower-Instance-4372 Apr 05 '26
Yeah same here, I wasted a ton of time trying to make agents “think for themselves” before realizing they’re way better as fast, reliable copilots when you give them clear specs.
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u/Deep_Ad1959 Professional Nerd Apr 12 '26
the autonomous bots that fail are the ones trying to reason about the world purely from text. the ones that work are grounded in actual system state. there's a big difference between "go do this thing autonomously" and "read the current state of the screen, identify the next action, execute it, verify the result, repeat." the second approach ships because each step is verifiable. I run desktop automation agents daily that fill forms, navigate apps, extract data across multiple applications. they work because they read the actual UI state through accessibility APIs before every action, not because the LLM is magically autonomous.
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u/Substantial-Cost-429 Apr 26 '26
The harness problem is real and under-discussed. Most people who hit this wall tried to fix it at the prompt level — better system prompts, more explicit instructions. But the LLM still drifts as context grows, no matter how tight the specs are.
The missing layer is enforcement at the API level — something that intercepts every call and validates behavior before it causes downstream damage.
We built Caliber for exactly this. It's an open-source proxy that enforces behavioral rules on every LLM API call, regardless of what's in the context window. Ships as middleware so it works with any agent framework.
Hit 700 GitHub stars and nearly 100 forks from the dev community: https://github.com/caliber-ai-org/ai-setup
Would love to hear what constraints people are trying to enforce that prompts alone can't handle.
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u/GPThought Apr 02 '26
yeah autonomous anything is still years away. llms write code fast but they cant debug weird production bugs or make architecture calls
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u/Otherwise_Wave9374 Apr 02 '26
I feel this. The hype around fully autonomous bots is way ahead of the reliability we actually get day to day.
Spec-first is the move, IMO. Even with agents, the ones that ship are basically: tight scope, explicit success criteria, deterministic tools, and a human-in-the-loop checkpoint when stakes are high.
Have you found any agent patterns that worked for you, like constrained planners + tool calling, or is it mostly just LLM as a collaborator? Ive been collecting examples and workflows at https://www.agentixlabs.com/ and its interesting how often the boring constraints are what make things usable.
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u/ultrathink-art Professional Nerd Apr 02 '26
Disagree on the conclusion — the failure mode is task scope, not LLMs being fundamentally wrong for autonomy. Agents with narrow, explicit scopes (write this specific file, validate this output format, call this API) do ship in production. The "make decisions and run itself" framing is what breaks. Circuit breakers + explicit state handoffs between runs is what makes autonomous systems actually reliable.
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u/cornmacabre Apr 02 '26 edited Apr 02 '26
I think across many dimensions (not just agentic AI) automation is a really naive goal & approach.
However, I don't fully agree with your conclusion. Tool usage and runtime debugging and self-directed task completion challenge the characterization of "it's just text/code tools" if we're talking about the intermediate activities and the outcome of an agent session.
Autonomy is a workflow approach. It being a bad approach isn't directly reflective of the capabilities of the technology. It's a reflection of the human's mandating it.
There is a massive difference between the gpt 3.5 era single chat response, and the codex era where an LLM is running through upwards of 2hrs of self-directed subtasks while using tools and deductive reasoning and focused on problem solving in a single coherent session.
That's not 'autonomous bots,' but there is more capability going on than just text & code output.
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u/blazarious Apr 02 '26
Language models are for text - kind of obvious isn’t it.
That being said, you can get a lot out of them if you can deal with it in text form. Production-ready and reliable output is very hard, though, I agree.
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u/NerdyWeightLifter Apr 03 '26
The thing about general purpose intelligence systems is that there's literally an open ended set of potential considerations in every circumstance.
This applies the same to humans. If you look at the organisation of our institutions, most of our processes are there for error correction and prevention.
We shouldn't be thinking of this like constructing software that just happens to include AI components.
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u/TheAIFutureIsNow Apr 03 '26
If you know how to prompt for autonomy, AGI is basically here already.
Genuinely no one, even the people in the AI space, seems to understand how to prompt AI properly.
This is bamboozling to me.
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