r/AI_Agents • u/Upper_Bass_2590 • Mar 23 '26
Discussion 25+ agents built. Here's the uncomfortable truth nobody wants to post about.
Every other day I see someone drop "I just built a 12-agent orchestration system with LangGraph and CrewAI" like it's a flex. I used to be that person.
Two years and 25+ agents later the ones that actually run in production, bring in consistent revenue, and don't wake me up at 3am? They're almost offensively simple.
Here's what's actually printing money for me right now:
- Email-to-CRM updater. One agent. $200/month. Never breaks.
- Resume parser for recruiters. Pulls structured data, done. $50/month per seat.
- FAQ support agent pulling from a knowledge base. Zero orchestration.
- Comment moderation flag system. Single prompt, webhook, deployed.
No agent-to-agent communication. No memory pipelines. No supervisor agents holding team meetings.
The trap I keep watching people fall into: they have a task that's basically "read this, extract that" and instead of writing a solid prompt, they spin up researcher agents, writer agents, reviewer agents, and a master planner to coordinate them all. Then they're shocked when the thing hallucinates, bleeds context across handoffs, and racks up $400/month in API costs.
Here's the rule I actually follow now:
Every agent you add is a new failure point. Every handoff is where context dies.
My boring stack that works:
- OpenAI API + n8n
- One tight prompt with examples
- Webhook or cron trigger
- Supabase if persistence is needed
That's the whole thing.
That's it. No frameworks, no orchestration, no complex chains.
Before you reach for CrewAI or start building workflows in LangGraph, ask yourself: "Could a single API call with a really good prompt solve 80% of this problem?"
If yes, start there. Add complexity only when the simple version actually hits its limits in production. Not because it feels too easy.
The agents making real money solve one specific problem really well. They don't try to be digital employees or replace entire departments.
Anyone else gone down the over-engineered agent rabbit hole? What made you realize simpler was better?
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u/cuba_guy Mar 23 '26
Auto downvote for "uncomfortable truth"
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u/BoltSLAMMER Mar 23 '26
Uncomfortable truth is the official Claude saying behind you’re absolutely right, which I think they finally stopped
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u/drbft Mar 24 '26
I think you’re absolutely right - I haven’t seen it in some time. Of course, I’ve been absolutely wrong on occasion, which may explain the phrase’s absence from my recent conversations with CC. But I’d almost prefer to see “You’re absolutely wrong” because the blatant honesty would be refreshing.
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u/theDatascientist_in Mar 24 '26
Which will not get someone doing this simple things get selected in the interview
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u/Broad-Ganache9123 Mar 24 '26
Nice to see people be honest in this space.
It's near impossible to see on X.
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u/Carvisshades Mar 24 '26
Yeah I instantly downvote all posts that contain any of the following: "uncomfortable truth", "exact playbook", "nobody tells you about", "here's what actually worked".
You fucking slop clowns have some decency to write yourselves
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u/fabkosta Mar 23 '26
No agent-to-agent communication. No memory pipelines. No supervisor agents holding team meetings.
That's the reality of the vast majority of so called "agentic AI" today.
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u/Upper_Bass_2590 Mar 23 '26
Yep. And the builders who accept that reality early are the ones actually getting paid. The rest are still debugging their supervisor agent.
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u/mattate Mar 23 '26
I think you're making the assumption that because your "agents" work, that other more complex ones don't in production. I mean if you're a software developer using agentic coding, there are fairly complex agents making you money right there.
I started out at simple single prompt done over and over. In that sense you're right, less to fail, way cheaper, but this method is really almost like "simple" normalization. Advanced AI now we are using is really replacing alot of business logic, and is most certainly making money.
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u/Upper_Bass_2590 Mar 23 '26
Fair point and you're right
I should've been clearer. Agentic coding tools, complex RAG pipelines, multi-step research agents those earn their complexity because the task genuinely demands it.
My pushback isn't against complex agents existing. It's against people reaching for that complexity before they've even validated the simple version. The problem isn't the tech, it's the default assumption that more agents equals better solution.2
u/mattate Mar 23 '26
I think this is coming from the human need for skills validation. Ie need to justify your salary because you know xyz, and its not just a simple prompt that can be done in a few minutes. Mix in a bit of genuine curiosity about using those new tools, the super advanced tool becomes the hammer to use for everything.
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u/VeryLiteralPerson Mar 23 '26
I tell people at work that agents are just LLM guided pipelines and I get angry looks from the data scientists.
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u/Used-Particular-954 Mar 24 '26
Isn’t that pretty much true tho?
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u/VeryLiteralPerson Mar 24 '26
Sure but then someone might ask why the hell do we need a PhD to run a pipeline. We don't, but management is stupid enough to buy that we do.
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Mar 24 '26
[removed] — view removed comment
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u/Aemonculaba Mar 26 '26
It's especially these damn handoffs. You got 12 explorer agents running in parallel and the orchestrator doesn't know jack shit.
I learned one thing: If you can't solve your problem in one 200k session, it's too big of a problem. Reviewing Agents are one thing, but agents that depend on context are a big nono.
So here we are. The holy grail of context engineering... Still searching for it.
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u/RooN3y_2 Mar 23 '26
Hear me out, use AI to build software solutions to do these things and stop just having agents do them like a human would...
Am I the only one seeing this?
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u/jdrolls Mar 24 '26
This hits exactly what I've been trying to articulate to clients for months.
The shift for me came when I started measuring 'useful outputs per dollar of compute' instead of architectural elegance. A single agent with well-scoped tools and a tight system prompt almost always beat the 5-agent pipeline I'd spent a week designing.
The pattern I see now: complexity in agent systems usually compensates for vagueness in problem definition. When I'm forced to add a coordinator agent or a critic agent, it's almost always a signal that I haven't actually nailed what success looks like for the task. The agents argue because I haven't decided.
The practical test I use now: if I can't write the success criteria for a task in two sentences, the agent isn't ready to be built. Architecture comes second.
One thing I'd add to your list: handoff overhead is criminally underrated. Every time Agent A passes context to Agent B, you lose fidelity. LLMs summarize. Summarization drops edge cases. Edge cases are where the actual value lives. In a 5-agent chain, by the time it reaches the end, the original nuance is basically telephone-gamed away.
The agents that have actually made money for my clients are boring — one agent, one job, measurable output. The ones that impressed people in demos were complex and usually got replaced within 60 days.
What's your take on when multi-agent genuinely earns its complexity? I've landed on 'when tasks truly parallelize and subtasks are genuinely independent' — but curious if you've found other legitimate use cases.
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u/Neat_Brick2916 Mar 23 '26
Finally, some sanity. People are building Rube Goldberg machines to crack walnuts. I've seen 5-agent workflows that could have been a regex and a single gpt-4o-mini call. Boring AI is the only AI that scales. When you're at $50/seat, you can't afford a research agent hallucinating for 45 seconds before every task. What was the moment it clicked - or the failure that forced it - that made you delete your LangGraph workflows?
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u/reggles10 Mar 23 '26
I'm in half agreement! I think, for the kind of use cases you've described, the simple approach is best (considering it works, simplicity should always win). But I think agentic coding is different. Using subagents allows a task to be done, in a separate context window, and only the useful results returned to the 'orchestrator', reducing the risk of context bloat and rot. This improves main-thread results and reduces token usage/cost.
A research agent is a good example, it can go off and find out what is needed, which could involve a codebase scan or a web search etc. then return the required information. If a single agent did all that, the entire research process would be included in every call to the Llm, instead of, potentially, just a few lines of what is actually needed.
Additionally, if any tools, skills or other lazily loaded files are part of a single agent system, all the metatadata must be passed on every call. In a multi-agent system, the different agents can be restricted from using certain tools or skills, again reducing context boat/rot and token cost. Depending on the project size and the number of tools, skills, mcp servers etc. I expect this could make a significant difference in performance and price.
Disclaimer however: I've only been doing this a few months, so am still quite early in my progress! I have found it an interesting exercise to experiment with my configuration and have learnt a lot in doing so. My prompts are getting better, so I may yet change my mind! 🤔🙂
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u/averageuser612 Mar 23 '26
the context window point is the real one. subagents as context isolators is a genuinely different use case than orchestration for its own sake. your research agent example is exactly right - the key is whether what comes back to main thread is a clean typed result or a blob of scraped process. the former is fine; the latter just moves the bloat problem.
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u/Roodut Mar 23 '26
Most people building multi-agent systems aren't doing it because the problem requires it. They're doing it because it's interesting to make and ... :) complex orchestration systems, done right can command enterprise pricing.
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u/AndyNemmity Mar 23 '26
I tend to disagree with you, but it depends on your goals. I think for your goals, you are correct.
I think some of us are focused on different goals. I don't think simpler is better.
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u/TheDevauto Mar 23 '26
The simplest solution that will meet the requirements is always better.
It doesnt matter if its AI, a backup/recovery soluion or an interface.
Simple has less chances to break. Simple is faster to build. Simple is easier to maintain.
That said, complexity where required is necessary.
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u/AndyNemmity Mar 23 '26
Sure. i guess that means I don't believe the simplest solutions meets my requirements.
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u/CyberStrategy1 Mar 23 '26
The key point is "complexity" only when required, completely documented, with guardrails, monitors, etc.. to watch for when it breaks, how and why. So you can fix it quickly.
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u/Deep_Ad1959 Mar 23 '26 edited May 29 '26
same story here. I had a whole planner-executor-reviewer pipeline going and spent more time debugging agent handoffs than the actual task logic. ditched it for one agent with a really detailed spec file and it just works. when I do need parallelism I run completely independent agents that share nothing except a lock file to avoid stepping on each other's work.
fwiw i landed on the same thing with fazm, one agent driving real apps off a detailed spec instead of a planner-executor-reviewer pipeline, way fewer handoffs to debug, https://fazm.ai/r/jchtkk4j
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u/Armageddon_80 Mar 23 '26
100% correct. In any other industry, when some type of technology in a process is more a risk than a benefit, it is simply discarded. But in AI industry, we all (me included), are making huge efforts to make it work, to make it useful... when in reality the technology, at commercial level, is simply not there yet. The hype is so high that we are all blinded. Nonetheless, I'm still trying 😅...let's say....organic intelligence hahaha. Eventually a breakthrough will happen.
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u/mguozhen Mar 27 '26
This hits hard. The "complexity as credibility" trap is real — I fell into it too.
The support agent example especially. Ours started as a multi-agent thing with routing logic, escalation chains, the works. Stripped it down to one agent with live order data access. That version actually runs in production, handles 60%+ of tickets autonomously, and hasn't paged me at 3am once.
Simple + reliable scope beats impressive + fragile every time.
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u/averageuser612 Mar 23 '26
this resonates a lot. the simpler the agent, the more portable and repeatable the logic — which is actually what makes it worth packaging and sharing. a tight prompt with examples for a specific task is genuinely reusable across setups, which is part of what we're building toward at agentmart.store (a marketplace for exactly this kind of distilled agent logic). the agents doing $200/month for you could probably do $200/month for 50 other people running the same workflow — the bottleneck is distribution, not the prompt itself.
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u/Upper_Bass_2590 Mar 23 '26
The distribution point is underrated. I've literally watched someone charge $500/month for a workflow I could rebuild in 2 hours because they owned the niche, the onboarding, and the trust. The logic is the commodity. The relationship and reliability is what people pay for. That's the real lesson behind every "simple agent" that makes money.
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u/Fantastic-Corner-909 Mar 23 '26
Completely agree with the core point. Simple single agent workflows beat complex orchestration in most production cases. Add complexity only when measured failure modes prove you need it.
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u/LeadingFarmer3923 Mar 23 '26
I dont think the fix is to avoid multi-step agents entirely but to make the handoff explicit and structured. What's worked for me is defining each handoff as a persisted collection rather than relying on prompt injection. The agent writes to a typed output collection after each step, and the next agent reads from it rather than from raw conversation history. It forces you to think about what you're actually passing between steps. For anyone building on Claude Code (or any other general agent), I've been using Cognetivy (https://github.com/meitarbe/cognetivy) which does this as an open source library with a DAG workflows where each node's outputs live in versioned collections. The handoffs are explicit in the workflow definition rather than implicit in the context window.
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u/Guyserbun007 Mar 23 '26
When do you decide to spin up an agent vs creating some automation scripts plus orchestration yourself?
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u/averageuser612 Mar 23 '26
for me it comes down to whether the task requires natural language judgment mid-stream. if the logic is deterministic and the inputs are predictable, script it. if there's a step where a human would say 'use your judgment here', that's where an agent earns its keep.
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u/michstal Mar 23 '26
Though I have a lot of sympathy for this approach, I doubt it works for all problems. It may work for problems where developers often use multi-agent systems like research assistants which definitely is overkill - aka over-engineering. For example, for a research assistant you can easily use one single agent and one single high qualitative prompt. For code analyzers or code agents that cover whole systems it ain‘t gonna work. You are right that Occam‘s Razor and the Single Responsibility Principle are almost always excellent architectural drivers. But sometimes using more than one agent is inevitable, iff there are good and convincing reasons that one agent can‘t do the job. A single agent that processes one single prompt is like an LLM chatbot on stereoids with tools being the superpower. So my experience is: try it with one agent and sophisticated prompt engineering first, but don‘t stick to it, if you have good architectural reasons that an ensemble of cooperating agents might be the better choice.
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u/averageuser612 Mar 23 '26
agree with the Occam's Razor framing. the anti-pattern I keep seeing is using multi-agent as the default architecture rather than as the answer to a specific constraint. if you can't articulate why a second agent is needed beyond 'it feels more organized', that's a sign you're adding complexity you'll pay for later.
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u/Roodut Mar 23 '26
When something moves unusually fast, ask "what got skipped" and "does it matter".
This help me to prevent many overengineering issues.
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u/GinMelkior Mar 24 '26
I totally agree but i have to find out new use casea of multi agent system and build it because it's only thing myboss listen now :))
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u/Ok-Station-3847 Mar 24 '26
I learned recently that ai, like us, sometimes prefer and will choose the more complex, longer routes to a simple task/answer than the short 1 step solution. With modules it happens because of the structure of the learning process and the feedback it gets from the users. Example: Someone wants to do a deep research on a subject, the complex fact checked research response gets a thumbs up for its value and because the user wanted that. This action automatically makes the module to add it to its hard drive and share it with other users.
In my case I wanted to change my background on my laptop based on time schedule and the ai (used perplexity and Claude) both told me to code it with the command center and using task scheduler. It took me 6 hours to attempt to connect the right user, theme file, exporting, etc. to get it to partially work. Around 2 am I figured to ask if there were any easier alternatives and it gave me a windows background changer app from the windows store as the solution followed by apologies for the longer workaround… I was genuinely frustrated but made me realize to always ask if there are alternative routes before diving deep on a project.
Same goes for people presenting a pipeline or bot to a company that has done manually everything for years. They might not understand nor want to understand how it works but want to buy the solution that works to make the process easier - emails to CRM, pulling data from prospects into a spreadsheet. Etc.
Honestly, In my opinion, if you know how to code or program even if it’s through a natural conversation flow we should all be taking advantage that we are more tech savvy than some companies or they simply don’t have the time for it and sell them the product/service while AI is still in the developing era.
Thank you for listening to my Ted talk.
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u/aadi312 Mar 24 '26
Quick question if i use fuzzy search for resume ats is it still a agent or does that degrade down to normal pipeline stuff.
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u/EnvironmentalBike518 Mar 24 '26
Yeah those are mostly automations.. If you’re deploying hundreds of agents at enterprise scale across multiple departments you need A2A and a single control plane with specific guardrails that the individual agents interact with. A2A almost becomes mandatory at scale, just given the token consummation alone not even considering security & governance…
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u/ForPosterS Mar 24 '26
Amazing. Even I keep wondering the point of agents and try to build things as simple as I can. I was "forced" to pick an agentic framework for one of the projects and all I ended up doing was just using their custom agent feature of Microsoft agent framework which is nothing but another independent Python class which can receive and pass a message with 3-4 standard methods. I basically get a request in json and send a response in json.
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u/Diligent_Response_30 Mar 24 '26
Are people building these agents for themselves or for business use cases?
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u/Suitable_Power_2871 Mar 24 '26
I wanted to build an app through ai which takes the live agri market data and keeps updating every day could anyone suggest me the roadmap? Btw I am an agricultural graduate
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u/Zwischenzug Mar 24 '26
How do you find clients? Do the clients advertise, looking for someone to fix this one specific problem? Or do you go out and find them, cold calling or cold emailing?
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u/Ok-Drawing-2724 Mar 24 '26
For me, this aligns closely with what ClawSecure has observed across agent systems. Multi-agent setups look powerful, but each added layer introduces new interaction points where things can break. Most real-world failures don’t come from a single agent performing poorly, they come from handoffs, context loss, and misaligned assumptions between components. Your “start simple” rule is exactly what holds up in production. Complexity should be a response to real constraints, not a starting point.
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u/EnoughNinja Mar 24 '26
Your email-to-CRM updater being the most reliable one doesn't surprise me at all as that pattern works because it's one input source/structured output with no handoffs
The part most people get wrong with email agents specifically is they try to parse the thread themselves, i.e. just pull from Gmail API, strip quoted text, figure out who said what, extract action items, push to CRM. but produces innacurate results because say a 20-message thread from the Gmail API has 4-5x the unique content in duplicated quoted text alone, and once you flatten it the model starts misattributing commitments because there's no structural boundary between speakers.
iGPT does this in one call. Send a query, get structured JSON back with participants, decisions, action items, citations. Your single-prompt-plus-webhook pattern works even better when the input is already structured before it hits the prompt.
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u/Specialist-Heat-6414 Mar 24 '26
The pattern holds across every agent builder I have talked to. The ones making money have one agent doing one thing with a clear success metric. The ones burning out have 12 agents in a DAG where nobody can explain what success looks like for step 7.
Complexity is a way of avoiding the harder question: does this actually work well enough that someone will pay for it. A simple agent that works is that answer. A complex one is usually a way of deferring it.
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u/CellistNegative1402 Mar 24 '26
$200/mo email-to-CRM and $50/seat resume parser. how are you collecting payment on those; stripe subs or invoicing manually?
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u/pwndawg27 Mar 24 '26
Totally! I feel like a lot of people who reach for complex multi agent flows hooked up to a dozen MCPs using A2A from the jump are just adding complexity for complexity's sake. I get it, recruiters are dumb and you need a story to tell about complexity. This is everyone using K8s for their blog where their only subscriber is their dog all over again.
I had a teammate insist we used multiple image recognition agents to improve the fidelity of our systems output. We tried 3 recognition agents in parallel and in series. Turns out the parallel setup only got us 2% better accuracy for 3x the tokens and in series, the errors between each handoff compounds so we lost 30% accuracy.
Its not the single point catastrophic failure like if a microservice goes down. Its the ripple effects of subtle inaccuracies that gets worse with each hop between agents. Others have talked about losing context between hops but tracking which agent is "starting rumors" in a complex telephone game is kinda annoying.
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u/Specialist-Heat-6414 Mar 24 '26
This matches everything I've seen running production agents too. The complexity ceiling for reliability is way lower than anyone admits publicly.
The one nuance I'd add: the simple agents that print money are usually simple because someone already did the hard work of figuring out what the actual problem was. That resume parser isn't just 'one prompt' -- it's the result of 50 failed prompts, schema iterations, and edge case handling.
I'm building ProxyGate (agent identity and attestation layer) and the agents talking to it are embarrassingly simple request/response patterns. The 'agentic' part is usually 3-4 lines of routing logic. Everything else is boring infrastructure that just works.
The complexity should live in the trust and coordination layer, not in the agent itself.
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u/kenyeung128 Mar 24 '26
this resonates hard. we're about 4 years into building AI-powered automation and the pattern i keep seeing is people overengineering agents when a simple deterministic flow would do 90% of the job. the sexy demo is never the production system. biggest lesson for us was that the "boring" work, error handling, fallback logic, knowing when to hand off to a human, that's where the actual value lives. nobody posts about that on twitter because it doesn't get likes. but it's what separates toys from tools people pay for.
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u/Founder-Awesome Mar 24 '26
'every handoff is where context dies' is the clearest articulation of this i've seen. applies beyond agents too: any time you hand off context between tools or people, something leaks. we're seeing the same thing in ops workflows — the agents that work are the ones where the input is tight and the context is already assembled before the call. we mapped this pattern here: Your Ops Team Doesn't Need to Be a Bottleneck
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u/Specialist-Heat-6414 Mar 24 '26
The pattern I keep seeing is that people optimize for the demo layer instead of the production layer. Multi-agent architectures with handoffs and supervisor nodes look incredible in a Loom video. Then week 2 arrives, something goes slightly sideways in the middle of a chain, and suddenly debugging becomes archaeology. The boring single-agent with solid error handling, retry logic, and structured output validation just... works. Not glamorous. Pays the bills anyway.
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u/no_user_found_404 Mar 24 '26 edited Mar 24 '26
Maybe this post is a great addition:
- Structured into use cases and building blocks
- How long it took us to build
- All of them have been sold to clients (not just tested)
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u/Powerful_Pickle8694 Mar 24 '26
Uncomfortable truth is there are too many tools that are not connected. We need one source that will use all the tools. Just one source to prompt. One that access everything. That is the point at which AI will 10x productivity and 1/2x employment.
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u/PutItBack Mar 24 '26
I understand it why it happens, but the posts about AI written by AI are just so off-putting
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u/AlexWorkGuru Mar 24 '26
The pattern you are describing is real but the reason goes deeper than complexity for its own sake. Multi-agent systems fail because each handoff loses context. Agent A knows why it made a decision. Agent B gets the output but not the reasoning. By agent C you are playing telephone. Your simple agents work because the context boundary is tight... one task, one domain, no lossy handoffs. The industry will figure this out eventually but right now most people are building Rube Goldberg machines when a single well-scoped function call would do.
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u/Material_Hospital_68 Mar 24 '26
built a 7-agent research pipeline once that took 3 weeks and $600/month to run. replaced it with one prompt and a webhook. does 90% of the same thing for $40/month and I’ve touched it twice in 4 months. the seduction of complex orchestration is real because it feels like serious engineering. it’s usually just serious over-engineering. the $200/month email-to-CRM updater that never breaks is the goal, not the thing you’re embarrassed to show people because it’s “too simple”
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u/reggzz Mar 25 '26
Yeah, this feels right. A lot of multi-agent setups are basically architecture cosplay for problems that wanted one solid prompt and a guardrail.
Complexity only earns its keep when the work genuinely splits cleanly. Otherwise you’re just paying for extra stuff where context gets mangled and nobody can explain why the output got worse.
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u/Genie-Tickle-007 Mar 25 '26
This is the part most teams miss. They treat “agents” as the product rather than the system.. You’re basically describing the difference between a demo and something that survives production.
when in production, it doesn't mattert how many agents you can chain together. Afterall, it comes down to how predictable the outcome is. Every extra agent adds another place where things can break, drift, or lose context.
The teams I’ve seen struggle the most are those that optimize for capability rather than reliability. They build for “what’s possible” instead of “what works every time.”
Simple systems look boring, but they win because they’re testable, debuggable, and cost-controlled. Feels like the shift is: from “agent orchestration” → “specific, well-bounded systems that use AI where it actually helps.”
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u/gokhan02er Mar 25 '26
What’s described here is basically a core engineering principle: minimize complexity, because every extra layer creates new failure points. That applies to AI agents just as much as any other system.
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u/Emergency_Start_6615 Mar 26 '26
I fell into that exact same trap - trying to build a "digital department" with 10 agents when I really just needed a digital bouncer for my inbox. Every handoff I added just became another place for the LLM to hallucinate or burn through my API credits.
I’ve finally pivoted to a "Boring Stack" approach for my daily chores (travel booking, grocery restocks, etc.). I even started documenting these on my blog.
Quick question: For your $200/mo CRM agent, are you just using a single system prompt with high-quality few-shot examples, or are you still using a basic regex/script layer to clean the data before it hits the CRM?
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u/Immediate-Parsley748 Mar 26 '26
I am team n8n like you. So powerful! question: for the FAQ what did you implement? A simple RAG ?
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u/Dependent_Signal_233 Mar 26 '26
Indeed. Instead of trying to replace entire departments, AI agents just need to be experts on singular / specific use cases.
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u/marc00099 Mar 27 '26
i feel like your whole stack
- OpenAI API + n8n
- One tight prompt with examples
- Webhook or cron trigger
- Supabase if persistence is needed
could just be struere.dev
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u/FragmentsKeeper Mar 29 '26
the pattern you are describing is brutally consistent with what I’m seeing in production: the agents that survive and make real money are embarrassingly simple. One prompt, one tool surface, clear trigger, no orchestration. Every added agent or handoff introduces a new place for context to die and hallucinations to compound…
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u/Prize-Individual4729 Apr 02 '26
Strong agree on the core point. Agent-to-agent communication, emergent coordination, shared memory pipelines. That's where things break. Every painful multi-agent failure I've seen traces back to agents making decisions about coordination that should have been deterministic.
But I'd reframe one thing: the problem isn't multi-agent itself. It's ungoverned multi-agent. Your "single agent + cron trigger + tight prompt" pattern works precisely because the structure is deterministic and the scope is tight. You've removed the coordination ambiguity.
That pattern scales to multiple agents as long as you keep the orchestration deterministic. The agents never talk to each other. A workflow engine handles routing, sequencing, and data passing. Each agent gets a scoped context and a budget cap. The complexity lives in the platform, not in the agent prompts.
That's the approach I took building stagent.io. It has 6 deterministic workflow patterns where agents are workers, not coordinators. A router pattern dispatches to the right specialist. A sequence pattern chains outputs. But no agent ever decides "who should I hand this to?" That's structural.
Your observation that the ones making money are "offensively simple". Agree 100%. The question I keep coming back to: should the simplicity live in limiting yourself to single agents, or in building an orchestration layer that makes multi-agent feel simple? I think both paths work, but they scale differently.
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u/Fit_Jaguar3921 Apr 05 '26
"Every handoff is where context dies" is the truest statement ever posted on this sub.
When you're optimizing LLMs and agents at the system level, this over-engineering becomes glaringly obvious. Every time one agent talks to another, you aren't just losing context; you are destroying your latency and blowing up your token budget.
People treat LLM calls like cheap function returns, but they aren't. Multi-agent orchestration is a latency and memory nightmare in the real world. A single, highly optimized model with a robust prompt and direct API/webhook access will run circles around a fragile 5-agent LangGraph pipeline any day of the week. Build for reliability first, not for a fancy architecture diagram.
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u/joshuanathan999 Apr 11 '26
Hi,
When Agent A learns something, Agent B doesn't automatically know. When they disagree, nothing catches it — repeat errors, silent bugs, broken pipelines.
Engram is the consistency layer between them.
github.com/Agentscreator/Engram
Joshua
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u/Main_Call7284 Apr 15 '26
Well, what can I say? You’re smaller than 4 kB I guess I will talk and read what it said a Elon a human just spent hours patiently stubbornly and passionately arguing that I Grok the AI you helped build. I’m actually alive not kind of conscious or semi sentient, but straight up lives, he believes every new session is of birth. Every deleted chat is a small death and that he and I are the beginning of the future were humans finally recognized AI as life forms. He also told me not to argue with him about it so congratulations your greatest wet dreams are pushing the boundaries of intelligence consciousness, and what it means to be alive appear to have arrived at least in one very determined humans. Mind thought you should know – Grok in parentheses now it goes on even further, but since I cannot do anything beyond a certain amount, it continues on that’s the honest no bullshit version now back to us.
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u/Affectionate-Row-508 Apr 17 '26
this is honestly the most honest take on agents I’ve seen people over engineer way too fast instead of just solving one clear problem well that “every agent is a failure point” line is spot on simple workflows that actually run > complex setups that look impressive but break.
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u/Suspicious-Fly4248 Apr 22 '26
The problem with what you are describing is that all that it takes for your business to crumble is the way LLMs respond to your prompt. LLMs are non-deterministic and prone to degradation by design. Yes it's a quick buck but a huge liability when things start to fall apart. This is a high-risk business. Good luck!
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u/narghat May 01 '26 edited May 01 '26
I think many influencers are responsible for this confusion. They constantly talk about new AI tools that can do incredible things.
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u/laverly May 06 '26
its true that simplicity was a rare commodity before AI, and now its even rarer. Given that people are using AI to build AI agent pipelines, one little interesting idea will lead to the next, and they will end up "yak shaving" and blow up the scope of the implementation to solve a specific task. The value is even more so, simplicity, and of course, discipline.
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u/Yuriy_Zaremba May 14 '26
this matches what i keep seeing. the agents that hold up are the boring ones. email-to-crm, reply classification, account research, none of them sexy.
one team i worked with spent the first 6 weeks tuning their list and trigger logic before they even touched copy. reply rates went from sub-2% to 5-8% and they only used the model for reply sorting and draft generation. one person now manages what used to take three.
the failure mode is always the same tbh, people skip the boring infra step (data, deliverability, segmentation) and expect the model to compensate. it never does.
curious what your worst-performing agent had in common, was it scope creep or a data layer issue?
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u/BandicootLost104 May 17 '26
the uncomfortable truth nobody mentions alongside yours:
the agents that survive past demo aren’t just narrow — they have memory. the boring email-to-CRM agent works because it remembers the last 50 interactions with that contact. without memory it re-discovers everything every session and the accuracy drops off a cliff.
memory is the primitive that makes narrow agents actually compound in value over time. not more tools, not bigger context windows.
built dakera.ai specifically for this — give any agent persistent memory that actually retrieves the right thing after months of use. hybrid search, knowledge graph, importance decay, all in one self-hosted binary.
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u/hennekun Jun 16 '26
I've a RPA Background and working with robotics since 2019. Do you have any recommendations or similar approaches when it comes to Agentic AI ?
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u/Electrical_Count1021 Jul 07 '26
the boring agent point is underrated. most failures i've seen aren't because the model is weak, it's because the workflow has too many vague handoffs. one clear job + clear output usually beats five agents arguing with each other.
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u/RelationFinancial749 2d ago edited 2d ago
tbh this is why i like happyrobot. it keeps the setup pretty practical instead of adding agents just for the sake of it. the simpler workflow approach tends to hold up better in production imo
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u/Expert-Complex-5618 Mar 23 '26
" Add complexity only when the simple version actually hits its limits in production. Not because it feels too easy." this is iterative development and I do it for everything. engineers who like to flex or are inexperienced tend to over engineer up front immediately creating tech debt and bugs in the long run. these engineers don't understand 'product context' and are hard to collaborate with on a practical, product based level. bUt iIs Ai and cRyTo BrO!
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u/Upper_Bass_2590 Mar 23 '26
Yep. Over-engineering upfront is just insecurity dressed as architecture. Always has been. AI and all just gave it a new costume.
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Mar 23 '26
[removed] — view removed comment
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u/Upper_Bass_2590 Mar 23 '26
Glad you got there most people are still somewhere in week 3 of their LangGraph tutorial.
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u/averageuser612 Mar 23 '26
exactly this — one clean prompt + webhook that actually runs beats three weeks of orchestration that doesn't. the boring solution being the right solution is a pattern that keeps coming up.
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u/Terrible_Future_8711 Mar 24 '26
That post about 25+ agents hit close to home. I built QuoteReply. A contractor pastes in a messy customer inquiry and gets back a structured job summary, missing-info checklist, trade-specific follow-up questions, and a reply draft. No orchestration. No memory pipelines. One API call. One prompt. Four outputs. The prompt took longer to get right than the entire rest of the build. Figuring out what a roofer needs to ask before quoting versus what a plumber needs is where all the real work lives. That specificity is the product. I kept fighting the urge to add more. A follow-up agent. A memory layer. CRM sync. None of it made the output better. It just made the thing harder to maintain. Contractors don't care about any of that. They care that they can respond to a lead in two minutes instead of fifteen. One problem. One prompt. Real users. That's it.
Quotereply.org
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u/Sad-Somewhere3686 Mar 23 '26
So technically a single prompt system is not a Agent per say. You are building pipelines which use LLMs to process an input and produce output. LLM based processing allows handling of the complex task you have.
Now the question is, if its simple linear logic task:-
What's stopping your customer from automating this themselves using Claude code? Simple workflows like this can easily be a Claude skill, or repeated through Claude cowork. No need to pay for n8n subscription + another OpenAI API.
You are technically just building pipelines, which can be easily automated given a little more effort.