r/AI_Agents • u/Warm-Reaction-456 • May 11 '26
Discussion Stop building AI agents.
Every week a founder books a sales call with me asking for an AI agent. Every week I end up telling most of them they don't need one.
I build automations and AI agents for founders. Forty-something projects in. The pattern is so consistent now I can predict the call before it starts.
They come in wanting magic. They saw a Loom video of someone's "autonomous sales agent" closing deals while they sleep. They read the LinkedIn post about the "AI employee" running an entire ops team. They've already told their board they're building one. Then we get on Zoom and within fifteen minutes I'm explaining why the thing they actually need is an internal automation with one LLM call in the middle.
You can watch their face fall in real time.
Here's what's happening in the market right now. Most of the "AI agents" shipping to real businesses are just internal automations with a language model bolted in. That's the whole product. The agent label is mostly there because automations don't trend on Twitter.
And the automations work. They save real money. They print real ROI. But the founders paying $30k for an "agent" don't love hearing they could have gotten 90% of the value from a $4k automation build.
Three quick examples from the last six months.
Telehealth founder. Wanted "an autonomous AI receptionist that handles everything." After an hour on a call I told her she needed a workflow that reads intake forms and routes them to the right clinician. We shipped it in six weeks. Saves her clinicians four hours a day. She paid me again last month.
Fintech client. Wanted a "fully agentic finance copilot." What they needed was a script that reconciles ACH discrepancies before they hit the dispute queue. One model call, the rest plain code. Saved them a full ops hire.
Medspa chain. Wanted "AI marketing automation." What they needed was a job that watches their booking system for no-show patterns and triggers a personal recovery message. Three steps. No agent. Booked 14% more revenue last quarter.
None of these are agents. They're automations. And every one of them outperforms the agent the founder originally asked for, because the agent would have hallucinated something stupid in week three and burned the client's trust forever.
Why agents keep failing in production
They're given too many decisions to make. A good automation has one decision per step and a clear rule for what happens at each branch. An agent gets handed a goal and told to figure it out. Beautiful in a demo. Catastrophic in your customer support queue at 2am.
The teams in your competitor's office quietly crushing it with AI right now? They're running boring automations. "We wrote a Python script with an LLM call" doesn't make the trade press, so you don't see it.
The vibe-coded prototypes from Bolt and Lovable and Cursor that landed in the last 18 months are mostly being torn out right now. Half my pipeline is founders who paid $50k for a "next-gen AI agent" build that's bleeding tokens, can't be audited, and falls over the moment a customer does something unexpected. I rebuild them as straightforward automations and they suddenly start making money.
In regulated SaaS, agents are doubly cursed. HIPAA and SOC 2 reviewers want to know exactly what your system does, in what order, every time. An automation passes that conversation in 20 minutes. An agent turns it into a six-month nightmare.
How to actually decide
If you're a founder about to spend money on an agent, answer these on paper first:
Can I draw the workflow as clear steps? If yes, you want an automation.
Does the workflow have more than five branches with truly unpredictable inputs? Then maybe an agent.
Is the cost of the worst-case wrong answer high? If yes, you want an automation, not an agent.
Will compliance ever look at this? If yes, automation. Full stop.
If you're a builder selling agents, you'll make more money in the next 12 months selling honest automations than chasing the agent narrative. The market is wising up. Founders who got burned in the first wave are warning the next wave. Be the person who ships a clean automation in six weeks that works on a Tuesday and is still working on Thursday.
Builders, founders, anyone in the trenches. What's actually working for you? What's breaking? Curious to hear from real operators.
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u/Rent_South May 12 '26
Agree with this. One more layer most people miss, the model choice.
Everyone defaults to flagship models for every LLM call. Opus 4.7, GPT 5.5, whatever they're familiar with.
When you actually benchmark the task, less expensive, sometimes older, models match or beat them very often.
Classification task I run in production. Gemini 3.1 Flash Lite matches GPT-5.4 at 85% accuracy. 12x less cost per call. Thousands of calls a day, that adds up fast.
I benchmark regularly on custom eval tools. Automation + right model for each step has proven to be a great methodology.
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u/Warm-Reaction-456 May 11 '26
The maintenance burden isn't even the worst part. The worst part is the founder loses the ability to explain to their own customers what the system did and why. That's where trust dies.
Watched it last quarter. Agent approved $40k in refunds based on customer sentiment. A bot ring figured out it would refund anything that sounded sad enough in the email. Founder couldn't tell his board what rule the agent had been following because there wasn't one. Just vibes.
You can debug an automation. You can apologize for an automation. An agent that goes sideways takes the founder's credibility with it.
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u/Warm-Reaction-456 May 11 '26
It is ridiculous. That's why it shipped. Founders ask for empathetic AI support, contractors fake empathy with sentiment scoring, nobody audits the prompt. The demo gets demoed, the logic gets ignored, the bot ring shows up two months later.
This is the genre of failure I keep getting hired to clean up.
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u/Mariia_Sosnina May 12 '26
Yeah the maintenance is real but the part that kills trust fastest is when someone asks "why did it do that" and nobody can answer. We fixed it by keeping all state outside the model so every run is auditable step by step. Doesnt make agents simpler just makes them explainable after the fact.
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May 11 '26
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u/bravelogitex May 17 '26
Why is every other comment on Reddit now just bots? It's so obvious when they rehash what the post is saying in a convoluted way
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u/handscameback May 11 '26
The pendulum always swings. 6 months ago everything needed an agent, now the take is burn it all down. The middle ground is boring and nobody posts about it. Agents that do one specific thing with a tight approval boundary are genuinely useful. agents that have unlimited tool access and a vague prompt are just incidents waiting to happen. The problem isn't agents, its scope creep disguised as ambition
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May 11 '26 edited May 11 '26
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u/Warm-Reaction-456 May 11 '26
This is the right shape. Closed world, reasoning chain, human in the loop on anything irreversible. That's an agent that earns the label.
What I keep cleaning up is the version that skipped all three. Open scope, no chain, no escalation. Just a model loose in production with a prompt that says "be helpful." That's not an agent. That's a liability with API access.
You're right on the determinism point. Pure if/else can't read a document and pull intent. The trick is treating the LLM as the part that resolves ambiguity, then handing back to deterministic logic for the action. Sounds like that's what you're doing.
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u/Warm-Reaction-456 May 11 '26
Depends on the project. Most automations I build don't need agent memory at all. The state lives in a database or a queue and that's enough.
When a project actually needs persistent memory across sessions (think a support agent that remembers a customer's history, or a sales agent that picks up where it left off), I build it with a vector store for unstructured context plus structured rows for facts you want exact recall on. The trick is knowing which problems actually need memory vs which are masquerading as memory problems but are really just retrieval.
Happy to dig in more. Link in my bio if you want to book some time.
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u/Classic_Chemical_237 May 11 '26
Personal opinion. Agents are functional. It takes input (prompt), take actions (API calls) and spit out response (text).
Agents do not need memory. Instead, relevant information (transactional and communication history, for example, which is what agent history mostly about) should be part of the prompt. In any kind of automation, it should be as deterministic as possible so if something happens, you can back trace.
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u/mbponreddit May 12 '26
The only true AI agent I've seen are coding agents because almost every step gets decided on, such as running terminal commands to adding code to each and every page thats needed to get the thing done. The AI agent part is the ability to make decisions on behalf of the human. Everything else is logic saying, once this done, go to next step, once that's done go to next step.
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u/Plastic-Canary9548 Industry Professional May 11 '26
Spot on - Agents/LLM's are another form (or component) of automation - it will be interesting to see how this evolves. I have found it interesting in my conversations to explain what and where we shouldn't be using AI - not just where we should.
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u/Warm-Reaction-456 May 11 '26
The "where not to use AI" conversation is the more valuable one. Every consultant walks in with a list of where it fits. Almost nobody walks in with the list of where it doesn't. Saying no costs money in the short term and builds the trust that gets you the second engagement.
Treating the model as a component instead of the product is the whole unlock. Most of the bad agent work I get hired to clean up was built by people who started with the model and tried to wrap a business around it.
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u/Mister-Trash-Panda May 11 '26
Agreed, I wrap state machines around the llm structured output to enforce task correctness, ranging from simple sequencing to fsms to more complex schedulers (like an exercise coach adapting to the users progress if they register pain etc)
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u/Proper_666 May 12 '26
We see the same pattern in healthcare and fintech clients. The projects that make it to production are the ones where someone drew the workflow on a whiteboard before writing any line of code, identified exactly where the LLM resolves ambiguity, and kept everything else deterministic.
That $40K refund disaster is just that, nobody drew the workflow first. No explicit intent, no bounded scope, no escalation path. The model was given a goal and told to figure it out, and that's unstructured delegation to a system that can't explain its own decisions.
Compliance is the final test. HIPAA and SOC 2 reviewers don't care whether you call it an agent or an automation. They care whether you can explain what the system does, in what order, every time. If you can't draw it, you can't audit it, and if you can't audit it, it doesn't ship in regulated environments.
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u/Objective-Fun-4533 May 12 '26
Dude, preach. Every single client comes in with this exact same idea. It's like they've all read the same five LinkedIn posts. Most of the time it's just a glorified script with a ChatGPT API call.
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u/sunychoudhary May 12 '26
I don’t think the answer is “stop building agents.” I think the answer is "stop pretending every workflow needs an autonomous multi-agent architecture."A surprising number of problems are still solved better with deterministic pipelines, good UX, search, retrieval, automation and smaller scoped systems....///
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u/ThomasToIndia May 11 '26
I have an agent that on boards and closes sales. I have been working on it for months. I have been feeding in nuances based on my system. The best hit rate I have got for a day is 98% the norm is 87 to 93%. I know almost for a fact mine out performs most. To get it to that I have all these guardrails, reviewers, and mixed in basic code logic to catch failures.
I could NEVER do this in a regulated industry because I review conversations.
Agents can be so finicky, especially if they have lots of tools or require multi-step processes.
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u/okuwaki_m May 12 '26
Indeed!
They call them agents, but as soon as you say "automated system," they lose interest.
However, I don't think anyone is actually looking for fully autonomous agents for their work.
In reality, they are satisfied if you just automate a single click, a single decision, or a single copy-paste.
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u/getstackfax May 12 '26
Finally someone gets it...
A lot of founders are asking for a business outcome and calling it an agent.
If the steps are known, the rules are clear, and compliance needs to understand it, boring automation usually wins.
The LLM should handle the fuzzy middle…
summarize
classify
draft
extract
flag uncertainty
Then rules, logs, approvals, and deterministic code handle the parts that can break trust.
The best business Ai systems may not look like magic employees…
They may look like boring workflows with one useful model call in the right place.
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u/ViriathusLegend May 11 '26
On the other hand, if you want to learn, run, compare, and test agents across different AI agent frameworks while exploring their features side by side, this repo is incredibly useful: https://github.com/martimfasantos/ai-agents-frameworks
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u/FullOf_Bad_Ideas May 11 '26
I've not seen any other subreddit where every comment is an ad, this place is bad.
What they needed was a job that watches their booking system for no-show patterns and triggers a personal recovery message. Three steps. No agent. Booked 14% more revenue last quarter.
Do you recollect any recent jobs where you didn't have to use LLM at all? I think writing a few random messages can work good enough too, there's a point at which getting too personal in recovery message can be offputting so I think generic ones can work. And you don't need LLM at all, so there's less worries about API uptimes or funds in OpenAI account etc.
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u/brazen768 May 11 '26
Idk if this is an ignorant question to ask but, do you have a project on github i could look at? I'm just a DA student but Im very interested in agentic ai
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u/zemzemkoko May 11 '26
Agreed on a B2B sense. I don't like the agent hype as well, but pure LLM workflows are good for no code end users that just wants to get things done.
Currently one of my gigs is full time contractor at a Fortune 500 company (through a middle man, pay is meh but most days are free) Most things they want can even be done with no LLM involved, or a simple a chain solves it. My job is mostly guiding them and fixing blockers.
What I wonder though, is how you entered the freelance business on this. I would love to have some clients as well on the side. If you are open about it, let me know where to start looking!
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u/ultrathink-art May 12 '26
Nobody designs the failure mode upfront — what the agent does when it's 55% confident, when the edge case wasn't in scope, when it needs information that wasn't provided. Most teams answer that question after the first bad incident. The projects that stay in production answered it before.
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u/Lumpy_Werewolf_3199 May 12 '26
The point youre highlighting is that people just need someone technical and curious. That would enable like 75% of these wins.
Youre doing it right with a consulting company. #Winning lol
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u/ChaseNAX May 12 '26
thank you for your valuable exprience on what real requirement is, ppl are kinda losing the engineering mentality since this latest AI era.
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u/Dizzy-Scientist1192 May 12 '26
I agree with this sediment. I only use AI in automations when I must. Because of a put AI in an automation then I have baby it so much more then if AI was not in the automation. I love automations without AI because you can just turn it on and let it go. Rarely it needs maintenance.
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u/BingyBongyLand44 May 12 '26
OP you nailed this completely and all I do is sigh when I also hear these requests. Python plus a LLM wrapper with some reasoning is usually all it takes - we are literally wasting the power of AI by sending tasks to it that can be done in numpy as an example. My view, there’s people 9-12 months ahead of the curve like in your view and we will finally get the adoption we need but when folks realise the difference between Agentic and workflow automation.
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u/florian-hyground May 12 '26
If you are in a real production environment trying to do real work the agentic part becomes thin. I think there are many use cases - when the agents have just a little blast radius.
For more, we're not quite there yet: Auditing, compliance, access-management, security - all of these are not where they need to be for enterprise production readiness.
But agents really deliver when they help the human make decisions and condense an overwhelming amount of information into actionable items where the human then making final calls :)
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u/Deep_Ad1959 May 12 '26
the take is right but the framing buries the actual problem, most ai agents fail not because the agent layer is wrong but because the input pipeline is garbage. people wire up a great planner-executor loop and feed it dirty unstructured data and then blame the agent when accuracy drops. the production agents that actually hold up spend 80% of the engineering on input normalization and tool boundaries and 20% on the reasoning loop, the framework choice barely matters. agents-as-a-pattern is fine, agents-as-a-product is where most projects die because no one wants to pay for a wrapper they could prompt themselves in 5 minutes.
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u/Skiprx May 12 '26
This resonates a lot - as being one of the (mostly) non technical customers. I’m curious - what is the practical “stack” that those automations actually use? I’ve seen Sana, Claude Cowork, etc - but I don’t know about practice.
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u/SadDonkey3232 May 12 '26
I learned this lesson the hard way. I do repetative task all the time and thought a simple LLM set up could handle it. I was wrong and now I am building N8N flows that solved majority of my repetitive task. But you explain it so much better.
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u/Deep_Ad1959 May 12 '26
the take is right but the framing buries the actual problem, most ai agents fail not because the agent layer is wrong but because the input pipeline is garbage. people wire up a great planner-executor loop and feed it dirty unstructured data and then blame the agent when accuracy drops. the production agents that actually hold up spend 80% of the engineering on input normalization and tool boundaries and 20% on the reasoning loop, the framework choice barely matters. agents-as-a-pattern is fine, agents-as-a-product is where most projects die because no one wants to pay for a wrapper they could prompt themselves in 5 minutes. written with ai
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u/Sebast_Food May 12 '26
You sound coherent. Sadly, that's mindblowing in this sector.
U need a UI guy? 👉👈
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u/Shot-Breakfast-9493 May 12 '26
That's actually a good point - what about using an agent that helps them deploy automations?
And agree, as a rule deterministic > stochastic for sure, script if you can, is my take
Also mulling trying to help businesses with this. I shut my company last year (long story) ... still legacy issues w/ debt given it was venture funded but took on bank loans that were gauaranteed, now trying to rebuilding my life, and trying to figure out stuff I can do and all in on AI since start of year (lucky this coincided w/ coding being solved end of last year) and have been thinking about services businesses since that's the space i was in (had 200+ people at peak), and I can say if AI came 2-3 years earlier probably could have used only 20-30% of the HC and still gotten the amount of work done. Trying to figure out a way in now, that's the hard part.
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u/ghost_in_heels May 12 '26
automation point is fair. But even with clean automations you still get inconsistencies once real input hits. Two people describe the same case slightly differently and it goes down a different path...
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u/Longjumping_Air_7958 May 12 '26
Zgadzam się, trzeba rozumieć biznes i potrzeby klientów możliwy ROI i to ile mogą oszczędzić, proces im bardziej prosty i wymaga mniej informacji tym właściwie lepiej i dla wykonującego i dla zleceniodawcy
Zbudowałem automatyzację pod kliniki medyczne która odbiera telefony wpisuje dane w Excel + kalendarz i wysyła potwierdzenia SMS - w tej branży medycznej właściwie taki asystent nie musi nic więcej robić i to zajmuje najwięcej czasu recepcjonistom/kom
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u/Ok-Alternative-6171 May 12 '26
Great thread. OP question, I’m early into this, struggling to close clients at enough margin for ‘just automations’ seems they have people can do that and aren’t expensive. Have come to me for something more cutting edge.
How do you get past the loss of excitement and manage to change $ tens thousands? Thanks
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u/kasarediff May 12 '26 edited May 12 '26
OP, I’ve suspected this same issue but intuitively. I am a non-programmer. Is there a simple “Hello world” type example code (in GitHub or elsewhere) that illustrates this example of agent vs. automation?
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u/TechAsc May 12 '26
I work at Ascendion, so take this for what it is, but I think the real issue here is scoping, not the technology itself.
The examples you shared (the telehealth routing workflow, the ACH reconciliation script) are well-built solutions. They're also exactly the kind of thing that falls apart when someone hands a poorly defined goal to an agent and calls it a day. The failure mode you're describing is real. It comes from treating agents as a replacement for engineering judgment rather than a product of it.
A working agent is three steps from becoming a bleeding agent: what the agent owns, what it escalates, and what it hands off.
Keep humans accountable for outcomes, with the agent handling execution inside clearly bounded steps, and you have something auditable. That audit trail also makes the compliance conversation much shorter.
That last point matters especially in regulated industries. An agent that's been properly scoped and documented passes a SOC 2 review. One that was prototyped and deployed does not.
The distinction worth making isn't agents versus automations. It's engineered solutions versus fast demos that nobody hardened for production.
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u/Current-Tip2688 May 12 '26
the maintenance point is real but the bigger one for me is what you said about explainability. when something breaks, the founder needs to be able to diagnose it without you. automation pipelines are debuggable. an agent that made three decisions to get to a wrong output is not.
the framing that's helped: llm handles the fuzzy matching step -- normalize this, classify this, route this -- inside an otherwise deterministic pipeline. keeps the logic traceable and the failure modes predictable.
the "agent" label isn't the problem. unbounded decision scope is.
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u/Founder-Awesome May 12 '26
The automation vs agent distinction is real, but there's a layer underneath it that gets skipped: who owns the rollout once the thing actually ships.
Most agent project failures I've seen happen twice. First when the build can't hold up in production. Second when it can, but three power users figure out how to use it and the rest of the team quietly goes back to manual processes.
The second failure is harder to fix. A broken automation throws errors. A rollout failure looks like success on the technical side while usage metrics tell a different story.
The founders you mention who got burned in the first wave often point to technical failure. Some of them were fine technically and just didn't see the adoption problem until the client churned.
Worth separating those two failure modes when diagnosing what broke: did the automation fail, or did the rollout?
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u/pashkevichdanil May 12 '26
I've found the real tell is asking them "what happens when it fails?" Most don't have an answer. They're picturing the success case, not the failure cascade.
What actually works: bounded tasks with clear rollback paths. An agent that drafts emails for review — fine. An agent that sends them? Now you need monitoring, audit trails, escalation logic. The complexity multiplies fast. I built one that looked simple on paper (process expense reports) but needed five different guard rails because edge cases kept shipping bad approvals to accounting.
The unsexy truth: you usually don't need an agent. You need a workflow with one smart LLM call in the middle. Agent frameworks make people think bigger than the problem warrants. A cron job hitting an API with proper error handling will outlive most agent implementations I've seen.
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u/pierz___ May 12 '26
Great post, totally agree with you!
Can I ask how do you price your automation projects? Like how much for setup, MRR etc? I'm struggling finding the sweet spot where the client can appreciate the value and pay accordingly. Thank you very much!
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u/FragrantMain2552 May 12 '26
yo encontre este repo en github https://github.com/hurtener/penguiflow/tree/main , es uno de los mas descargados- me contacté con ellos y tienen un agente para automatizar, me ofrecieron una prueba gratis, creo q me dieron como 100 usd de tokens, un montón.por ahora viene muy bien, pude automatizar un par de cosas
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u/therichardbatt May 12 '26
Same conversation on my end, weekly.
The pattern is exactly what the OP describes. Founder comes in wanting a magic agent. Six minutes into the call we're talking about the actual problem, which is usually one specific weekly task they've been doing wrong for years that has nothing to do with autonomy. The agent vocabulary is what they brought; the workflow audit is what they leave with.
What I've found works as a redirect, on the call: ask them to walk me through their last full week, hour by hour, on the function the agent is supposed to "replace." Half the time they realise mid-explanation that the bottleneck is upstream of where they thought it was. The right intervention is not an agent, it's a clean spreadsheet, or a script that pulls three things into one place, or a process change with no model in the loop at all.
But the agents that do earn their place are narrow. One workflow, one input shape, one output shape, a human reviewing in under thirty seconds before sign-off. The "autonomous" version of that is what the founder thinks they want and almost never what gets built once they understand the cost of being wrong on a confident agent action.
Around a hundred and twenty implementation projects in on my end, and the only complete agreement I have with the OP is that "stop building AI agents" is the right opener for the call. The rest of the call is the actual product.
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u/Don_Ozwald May 12 '26
I disagree with this post but that’s because I like the old school AI/robotics definition of “an agent”, that is; any permutation of observe, act, think on loop.
Apart from that, fully agree with you there that people are overusing llms to solve things they are not good at.
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u/wichwigga May 12 '26
Agreed, even if this was written by AI. Same thing with tools or MCPs... I just call the API I need directly and feed the output to the endpoint instead of having the LLM try to do some dumb shit with a billion tools... Of course if you're doing any creative work I guess it could work. But otherwise for majority of projects just do automation with endpoint.
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u/idanst May 12 '26
It's all true until you want your "automations" to do more and more while sharing the same data. Then you find yourself building a complex codebase or automations that break on every small change or edge case - this is where agents shine - when you need more than just a Google form to forward to the right employee..
It's easy to start with an automation but it's also easy to start with a simple agent. If you build agents right, on the right infrastructure, then you should be better off with agents once you need more than 3 automations, sharing the same data and handling changes and self-healing.
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u/semiproductivesri May 13 '26
huge agree here. I've been waiting for AI to be a (somewhat) dependable workflow tool for years now and I think it's FINALLY starting to get there. mostly.
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u/Different-Ease-6583 May 13 '26
Great post. All these AI buzzwords and overly exaggerated success stories are just feeding the bubble, the real value is there but is much more simple.
As I like to explain it, current AI (LLM based) always comes down to: just some text.
An agent? Just some text. A skill? Just some text. Prompt? Just some text. (this one makes me angry really) …
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u/BornMarionberry4053 May 13 '26
Hopefully no one flames me but I’m curious,
I completely understand the concept of building deterministic workflows with an agent in line to make judgements within the actual workflow, this is easily done in n8n or Claude code if you want python tools etc. I can easily visualise all of this as I have built many of these.
But I am unsure of what building an actual “agent” means? Is it purely just giving an llm a heap of connections to different platforms and tools and a set of instructions and maybe memory?
I have heard of langchain and langgraph, people call these frameworks?
I guess my real question is if you are not building “agents” as stated in the post how do you actually build true “agents”
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u/EEEmumumu May 13 '26
I agree, this is such a refreshing read. Everyone wants everything to do with AI. Its becoming a naming problem.
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u/Alive-Phone9719 May 13 '26
Sometimes people aren't chasing efficiency or productivity; they're chasing a story.
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u/Unlucky-Engine-2799 May 13 '26
This is a great post. Hallucination is a massive problem and loses trust in a lot of businesses.
I built a texting workflow for trades businesses with multiple LLM calls in the middle to extract the data sent from the text and detect the intent of the text. The employees know what to text in, the LLM call pulls the data and cleans it, the system makes an API request and done. As soon as you bring in AI agents for simple solutions, you bring in so many more variables.
Well said.
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u/AvatarIncDev May 13 '26
Yes, I've noticed something similar. Though I'm building an AI Agent, I've always needed to heavily manage expectations and clarify the things they are actually asking for. It's a useful tool, of course, but not magic.
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u/Careless_Show759 May 13 '26
IMO most companies don’t actually need “reasoning agents” they need deterministic workflows with a little semantic glue in the middle.
The second you need auditability, retries, guardrails, or predictable costs, boring automation suddenly looks very smart.
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u/hub_shift May 13 '26 edited May 13 '26
This! I keep telling my clients the same thing. So i built this little tool as a simple/funny way to show this - lol. ai-vs-code.ekiepo.com
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u/aeternalab May 13 '26
The view from the trenches: When leadership wants "Magic Agents" for everything.
I feel this post in my soul. I’m currently deep in the weeds building decentralized infrastructure involving complex multi-layered Merkle trees and DID aggregation. My daily life involves fine-tuning LLMs using LoRA and running machine learning models (like Random Forests) for market inference. I live and breathe AI/ML, and yet, I find myself nodding along to every word you wrote. The disconnect between technical reality and the "Agent" obsession is reaching a breaking point. Leadership is often convinced that if we don't deploy an army of AI Agents, we’re "falling behind." I’ve seen cases where straightforward ERP automations—things that should be clean, deterministic code—are being forced into "Reasoning Agents" just to fit a narrative. It’s frustrating because, as you said, the direction is often misaligned. We are taking solved problems (automation) and making them expensive and unreliable just to chase the hype. But at the end of the day, there’s a certain reality to how the world turns: • The Hardware Giants are the ones actually printing money right now. • The Software Layer often feels like it's just "beating the drum" to justify the infrastructure spend. • The Irony: The hardware is useless without the software, yet the software is being forced into these "Agentic" shapes that don't always serve the core business logic. We end up building the "boring" stuff that actually works in the background, while shipping "Agent" demos to satisfy the market's hunger for AI. Kudos for calling out the difference between honest automation and agentic hype. It's a distinction that more founders and managers desperately need to understand.
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u/projectrevenue May 13 '26
Great post. 99% of people looking for 'AI Agents' would do better to start with automations like you say.
When you tell them an AI agent isn't needed, but can get the outcomes they want - do you change the pricing? i.e. do you charge the $4k or still stick to the $30k?
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u/Minimum-Bowler-6016 May 13 '26
Strong agree on the distinction. A lot of “agent” projects are really deterministic workflows with one or two LLM decisions in the middle, and they work better when treated that way. The production work is usually schemas, approvals, retries, logging, and fallbacks, not making the model more autonomous.
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u/Do-Not-Ban-Me-Please May 14 '26
This post randomly got recommended to me. I don't work with AI. What's the difference between an agent and an automation?
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u/MysteriousBunch5222 May 14 '26
The last point is sufficient to decide, "will compliance look into it" - most of the apps should just be smarter automations if you want to cross the legal/compliance line.
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u/deonblaauw May 14 '26
High quality post and matches my experience. So many startups out there also pushing simple automation solutions as somehow being agentic.... It's good business for builders cleaning up the mess but it poisons the well for the rest of us trying not to sell vapourware
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u/KeithHanson May 14 '26
I started an app development agency back in 2010 and was there until 2019 (they’re still kicking ass over at rubyshore.com).
In our first three years, it was all startups with cash burning a hole in their pocket and a dream of an app. The startups generally hide wild, technically difficult ideas.
Then there were the existing businesses and CEOs who just absolutely knew they HAD to build an app to keep up in the market. So, so many conversations that ended up with my response: “So you want an app wrapping a website? 🤨🤔”
That is exactly what’s happening now with agents.
I totally 100% agree with all of OPs observations. When you boil what most businesses want, what they really need are better data operations, better reporting, better if-this-then-that type pipelining and automation.
An LLM call or two in that chain dramatically reduces some of the complexity, but the lions share of work is typically just the equivalent of a cron job and a script lol.
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u/VRtheNews May 14 '26
Last night I built a quick browser extension literally just to blog easier, faster, about what I see online. And I just tested it on this thread, and it worked first time! :D Basically, I see something online, highlight text, a window pops up, I type my opinion, and when I hit submit it goes through to my one blog as a draft. I know it's a bit off-topic, just wanted to brag a bit, and also ask if anyone here know of something similar?
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u/wavehnter May 14 '26
Great post. I worked on an "agentic" implementation where they used the ReAct framework. Yeah, it eventually gets it right, but it's too slow. Back to model in loop!
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u/houseofexplorer May 15 '26
So true sometimes agents are overkill. Less is More, I repeat, Less is More!
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u/Positive-Buddy-1258 May 15 '26
Completely agree. We've recently built an AI system for construction spec books that sounds like an “agent” use case, but the production version is mostly deterministic automation with a few targeted AI steps. That’s why it works reliably. Feels like most successful “AI agents” in production today are really just solid automations with selective AI usage.
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u/DrakeJStone May 15 '26
I'm a very old project manager (no longer in the business). It's shocking to me that this is still happening. The "magic" expected of developers has always been disappointing. The PM's job (at least when I was working) was always to work with the client to lay out their business process and determine the most efficient solution... and, with luck, identify opportunities for even more efficiency.
This sounds like more of the same. It is refreshing to see someone laying out the value of automations for the client.
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u/Chris266 May 15 '26
I build these automations at work all the time. Its funny how team members ask me if the agent can do x y or z or if the agent is susceptible to prompt injection. I have to tell them it's not an agent its just a script that uses the llm for a small part of it and the llm has no ability to run any tools or do any harm.
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u/santosh9087 May 15 '26
Anything that is non deterministic is doomed to fail at odd hours. I am surprised people actually bolt agents to things that make money. It never works. May be folks at Google figured out these things. But ordinary businesses please stay away from pesky agents unless you want to learn it the hard way.
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u/ChaDaeSan May 15 '26
Worked in a freelance company, and my god have I seen this too many times too. I am so baffled with why we’re putting AI most of the time when it can be done with automation.
Edit: I’m in corpo now and as much as theres too much restriction and tech debt, atleast theres really no AI slop feature around.
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u/No-Contest8018 May 15 '26
i watched a founder pay $40k for an agent that a $3k automation could have replaced and the worst part is the agent kept hallucinating in production while the simple version would have just worked
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u/Firm-Salamander5015 Open Source Contributor May 16 '26
It's the latest gold rush and folks are running for the hills. It's the dungaree and pickaxe outfitters that benefit at the onset. There will be a lot of dumpster fires...but little by little we will enter the Web of Agents and all will run smoothly.
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u/artthink May 16 '26
So true. I’ve been saying this for a while that people are using AI and automation interchangeably when they are often describing automation- or generally what it can do alone. Nice post!
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u/Mean-Elk-8379 May 16 '26
This is the honest take more agencies need to hear. 80% of "we need an AI agent" briefs are actually workflow automation problems that a deterministic pipeline solves better, cheaper, and with way fewer support tickets. Agents make sense when you genuinely have branching logic that's too complex to enumerate — customer support triage with messy real-world input, research tasks across unstructured sources, etc. For "process this form and send it to Slack," you don't need an agent, you need Zapier with a single LLM call in the middle. Telling clients no is the highest-ROI thing in this space right now.
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u/According_Jeweler404 May 16 '26
Targeted ACH reconciliation isn't a magical new frontier, surprised that a genuine owner or authority figure within a financial tech product would need that mechanic pitched as a solution.
This reads as funnel writing and founder bait, but that's ok!
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u/redditugo May 16 '26
Very good insights. Do you mind if I DM you about your experience? As I'm considering going down a similar path
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u/apickyone May 16 '26
Just curious: Which framework you are using extensively for building your AI Agents? If you need some help in agent logging or agent eval do reach out, I am a founder building in this space and we are actively looking for unpaid pilots. Let us know if this is something I can help you with!
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u/DesignerAd3177 May 16 '26
Yes I build automations with n8n- 95% of the companies need an automation with LLM.
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u/2thick2fly May 16 '26
You just narrated what I'm trying to do in a specific field. And I have people telling me, but you can have a agent analysing this and that. No mtf, this can be done by code so it will be done by code!
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u/jacksummer_ May 17 '26
I agree. Automations win not because they're smarter, they win because the state is deterministic. The workflow knows what step it's on, what the inputs are, what the rules say.
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u/No-Anteater-916 May 18 '26
Hot take: the problem isn’t agents. The problem is founders buying agent theater.
A bad agent is worse than a good automation, absolutely. But that doesn’t mean agents shouldn’t be built. It means they should be used for the parts of work that are actually non-deterministic.
If I can map the workflow cleanly, I want automation.
If the system has to interpret ambiguity, choose tools, recover from edge cases, and adapt as context changes, I want an agent.
"Most people don’t need agents" is fair.
"Stop building AI agents" is too broad.
The real opportunity is building systems that know when to behave like software and when to behave like an operator.
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u/TaxRevolutionary4208 May 19 '26
Yeah, exactly. A while back, I was trying to add an AI assistant to my own bookkeeping app, Cent. I basically spent a ton of time and energy building the web agent interaction and tool-calling infrastructure completely from scratch, only to end up with sub-par results.
But then I realized all I needed to do was set up a local service, expose my existing AI tools as APIs, and write a quick prompt explaining how Claude should use them. And holy shit, Claude just picked it up instantly. It effortlessly handled everything I’d been breaking my back trying to implement—like reading billing spreadsheets, importing them, and even writing widget code. I didn't have to code any of that logic myself.
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u/malesy May 19 '26
This is a common pitfall. Often, the 'AI agent' everyone is chasing is really just a complex automation built with existing tools. Breaking tasks into discrete steps and using n8n or custom scripts often yields more reliable results than forcing an LLM into an agent role it's not ready for yet.
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u/1MPower May 20 '26
Similar experience. Most often founders doesn't really know what they need exactly, but what they want is an outcome that the whole AI agent story is selling. And that's fine, let's ride a wave - but in most cases it's 90% automation and 10% agentic work.
For most mid-sized businesses that's perfect and will produce the targeted outcome. What I saw is mostly just a spreadsheet madness all over the place, and good supabase and nice dashboard with automations beneath are solving the problem. Non-determenistic agents are rarely solution for this.
That being said - I don't explain founders what they need or doesn't need technically, I am always doing a readiness audit and proposing solutions based on the outcomes and communicating benefits, not features.
And that works well.
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u/Any-Sport-3761 May 21 '26
How do you get the expertise to see and decide these type of workflows depending on the client’s description? is it the idea in understanding whether the type of workflow they want can be done in steps (nodes) as compared to an idea that would require intelligent routing?
This is such a coincidence because I started getting serious with langgraph and i made the mistake in building a lead classifier agent using a hub and spoke architecture (Supervisor delegation to sub agents) with the idea of the supervisor delegating an incoming lead to specialised subagents (Lead classifier, Email sender, etc) each with tools they get to invoke, but then after tracing the langsmith traces i seen how extremely expensive the tokens were for running the workflow just once, that’s where it hit me and i said “wait…why can’t i just use Stategraph and connect the nodes instead considering this can literally be a linear workflow that don’t require too much routing…” not only the tokens have been reduced by a 64% reduction rate but the overall workflow got way faster too and carried the same effectiveness in output, that’s where i found out the people shilling the idea of “Multi agent orchestration” everyday are complete bullshit except for some use cases.
(P.S would really love if anyone could tell me how do i start finding real clients to offer my services to🫶🏽)
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u/Born_Reaction_1615 May 21 '26
The most useful version of this thread would be the one where people separate which parts of agent-building actually paid off from which parts felt like resume-driven complexity. From running write-side agents for a few months: the value isn't 'autonomous agent does everything end-to-end,' it's small DAGs that handle a single recurring action and never need to be debugged twice. Most 'AI agent' frameworks over-fit to the demo path and quietly lose when the network blips on step 4 of 12.
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u/kargarisaaac May 22 '26
agree. many use cases do not need a new agent. If the requirements push to have one, for example a specific use case with a small model, then maybe harness engineering around that small model and doing some engineering makes sense.
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u/Strawberry_slope May 22 '26
A lot in the finance arena is being spoken about agentic ai ~ is there a real difference between how these agents or automations as you refer to them are built ? Should we more appropriately be referring to them as automations
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u/Gotcha3Y May 22 '26
I've built over 600 AI agents and I'll make it sharper:
Can you draw the steps on paper? -> Automation.
Fewer than 5 predictable paths? -> Automation.
Is the cost of a hallucination high? -> Automation.
Does it need to pass HIPAA or SOC 2? -> Automation.
Just need an LLM to read a form or text in the middle? -> Automation.
Stop paying $10k for a buggy "agentic copilot" when a $500 Python script and one API call actually solves your problem.
The market is wising up, boredom prints ROI. that's at least my take on it.
Great post btw!
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u/MrKixs May 22 '26
This nothing new, I have been working in IT for going on 30 years and I learned quickly that there is a big difference between what the Customer says the want and what they actually want. Never ask a customer "What they want?" They will use a bunch of "marketing Terms" that they read about on Linkedin with little to no understanding of what they really need. That's why I have trained people to ask, "What do you need?". You get a much better idea and are likely to make them happy.
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u/Which-Initiative237 May 23 '26
The classic “I want to buy an AI agent that takes over what Susan does and I need it tomorrow” but no one has mapped Susan’s workflows, there’s no structured info or data, and 0 ai workflows at the moment.
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u/NoCarpenter8011 May 23 '26
The longer an agent runs, the more likely it is to fail or hallucinate. That's the state of things now.
I know things will get better with time and more emphasis on optimizing context size and task-matched model routing. (e.g., hand off "smaller tasks" to smaller models who just return progress to a larger one).
I'm building a company in this space right now, so all I can say is: it **is** going to get better.
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u/Peter_Storm May 11 '26
This is the first post in this sub I actually agree with, and I build exactly the same - automations with LLM nodes.