{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Benjamin Nweke",
  "title_listed": "Most AI Agents Fail in Production Because They're Built Backwards",
  "keywords": "",
  "domain": "大模型算法",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/most-ai-agents-fail-in-production-because-theyre-built-backwards/",
  "publish_date": "2026-05-27",
  "corpus_stage": "",
  "notes": "",
  "id": "e1feacdc0a57",
  "collected_at": "2026-10-02T07:38:54",
  "title": "Most AI Agents Fail in Production Because They’re Built Backwards | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Agentic AI](https://towardsdatascience.com/category/artificial-intelligence/agentic-ai/)\n# Most AI Agents Fail in Production Because They’re Built Backwards\nGood models don't save bad architecture, and most teams learn that the hard way.\n[Benjamin Nweke](https://towardsdatascience.com/author/benjaminnweke/)\nMay 27, 20268 min read\n![](../images/000.jpg)\nImage by author (Generated with ChatGPT)\nThe first time I saw a multi-agent system seriously fail in production, it wasn’t dramatic. There was no crash. No error message. The system just kept running and producing outputs that looked reasonable until someone actually read them carefully enough to notice something was off.\nWhen we decided to look into it, it took us two days' worth of debugging to figure out what was going on. Funny enough, the model wasn't hallucinating, and the input-output tools were delivering the correct results.\nThe problem, when we finally found it, was architectural. The model and the tools were set up correctly, but the idea was that reasoning would tie the whole thing together, which, as you would guess, obviously failed.\nTurns out reasoning does not do that sort of thing.\nThat experience is what I keep coming back to when I think about why so many AI agents that work in demos don't really survive real-world use.\nIt's not a capability problem. \nIt's an architectural one.\nAnd if you've read my previous piece here on TDS, [Why AI Engineers Are Moving Beyond LangChain to Native Agent Architectures](https://towardsdatascience.com/why-ai-engineers-are-moving-beyond-langchain-to-native-agent-architectures/), the pattern should sound familiar: systems built top-down, from goal to tools to model, with the quiet assumption that intelligent behavior fills in the gaps.\nThat assumption is what \"built backwards\" means. And it's more common than most teams realize until something breaks.\nLearn this step by step with the interactive [AI Agents](https://roadmap.sh/ai-agents) roadmap.\n## Agents Aren't Entities. They're Systems.\nA production AI agent isn't a single intelligent thing.\nRather, there is a set of interacting pieces with different responsibilities, failure modes, and levels of observability.\nThe LLM is one of those components, not the whole system. Just one piece of it.\nIt may sound obvious when you say it out loud. But the \"autonomous agent\" framing that dominated 2023 and most of 2024 kept pulling engineers toward a different mental model: one entity, one reasoning loop, everything handled by the model.\nAll you need is tools, a good system prompt, and a hope that everything will fall into place.\nIn contrast, engineers who have shipped real AI-based products rarely describe their systems that way. What they actually describe sounds a lot more like distributed systems architecture. \nNot because they read a book about design patterns, but because they got burned enough times that they started putting structure more seriously in their workflow.\nBuilding top-down, starting from \"what should this agent do\" and working backwards into tools and prompts, is quick to get started.\nIt's also how you end up with a system where the model is responsible for too much, and nothing is individually debuggable.\nThe architecture was decided by the goal, not by the engineering requirements. \nThat's the backwards part.\n···\n## So What Really Goes Into a Production System?\nThe abstract version is easy to nod along to. Here's what it actually looks like.\nEvery production AI system I have seen that works cleanly has something like a **decision layer** , whether the team named it like this or not. It's the part where the model lives and does its actual job.\nThe instinct is to push everything into this layer: parsing requests, managing memory, handling retries, resolving tool failures.\nThis is okay if you’re working in a Jupyter notebook. In production, under load, with real users, this becomes the part of your system where everything is everyone's fault, and most times, nothing can be debugged.\nThe decision layer should do one thing well, and that is deciding what to do next, given a certain context that is already prepared for it.\nThat's the whole job.\nWho prepares the context? Something else. Who acts on the decision? Also something else.\nThat \"something else\" is the orchestration layer, and in most well-built systems, it’s genuinely just code: conditionals, asynchronous runners, retry handling, queue routing, maybe even a state machine depending on how involved the workflow is.\n![](../images/001.png)\nInstead of expecting the model to do everything, treat it like just another component. Here, standard code does the heavy lifting with state and tools, so the LLM only has to worry about making the next decision. Image by author.\nMany teams reach for frameworks here because bare orchestration code feels too simple, like surely there's supposed to be more infrastructure.\nThere usually isn't. \nThe less magic this layer contains, the faster you'll find bugs when they appear. And they will appear.\nFrom experience, I learned this the hard way on a project where the orchestration lived inside a framework's execution model. Something was retrying tool calls in a way that was corrupting state downstream. \nWe spent two days finding the issue. Two days for a bug that could have been resolved in no time at all if the retry logic had been three lines of Python I wrote myself.\nThis leads us to the tools and execution layer, where all communication happens.\nNow, the **tools and execution layer** is where things talk to the outside world. This layer usually has just one job, and that is to take a well-defined input and then produce a predictable output.\nBut the failure I kept seeing, and kept repeating, honestly, was tools that tried to be helpful by doing more than one thing. A single function that calls an API, updates a cache, and does other things. \nIn a setup like that, when it breaks, you don't know where. Even when you try to replace the API, you're untangling logic that shouldn't have been tangled in the first place.\n**Memory and state** is where I'd push hardest, because it's where most teams are most underprepared. \nMost teams think about memory as \"what the model knows.\" The more important question is what the _system_ knows, and whether that knowledge is current.\nI remember one day when it took me an afternoon to debug what seemed to be a simple \"model hallucination.\" The model had kept referring to user preferences, which, however, had been updated twenty minutes ago.\nThat's not a model problem. \nThat's a systems problem. \nAnd it's surprisingly common.\nIn multi-agent systems, specifically, shared state is where subtle failures breed. One agent updates something. The others don't know.\nEveryone proceeds confidently in slightly different directions. The output looks almost right, which is [almost worse than looking wrong](https://towardsdatascience.com/the-multi-agent-trap/).\nAnd then there's **evaluation and observability** , which almost everyone always puts off until something goes wrong. I've been guilty of this, too.\nThe difference I keep in mind is that logging tells you what happened. Observability tells you [_whether what happened was correct_](https://www.datadoghq.com/state-of-ai-engineering/). In a deterministic system, those are close to the same thing.\nIn an AI system, it’s not. You have to be able to follow the specific request from start to finish, including what information the model had to consider, what decision it made, what external API call it invoked, and how it acted upon its response.\n···\n## Building It the Right Way Around\nIt starts with the top-down approach: I want an agent to do X, so I'll give it the tools, a nice system prompt, and if the model is smart enough, it will be fine.\nAnd this is exactly what people use to make prototypes, and why wouldn't they? They are not wrong.\nBut here's the thing: the problem is that it treats the architecture as a consequence of the goal rather than as something you design deliberately.\nThen the system grows. You know, more tools, more workflows, more edge cases, more users, and suddenly there’s no real foundation underneath any of it.\nBottom-up is more time-consuming, but it’s far more comfortable.\nYou start with the basic building blocks and make sure they actually work. Then you figure out what each part should communicate, what data it owns, and what it’s responsible for. \nEventually, the system takes shape naturally from the interaction of its parts.\nThis isn't a \"real engineers build everything from scratch\" argument. It's not even about tooling at all, actually. It's about the mental model you're building with.\nI've seen engineers use sophisticated frameworks and build clean systems because they understood what each layer needed to do.\nI have also seen engineers write vanilla Python and build an undebuggable mess because they were still thinking in terms of \"the agent decides everything.\" The tools follow from the model in your head, not the other way around.\nThe most robust multi-agent system I have had the opportunity to work with closely had almost no AI-specific infrastructure. When I first saw the repo, I honestly assumed I was looking at the wrong codebase. \nA message queue, worker processes with distinct scopes, shared state storage with explicit read/write contracts, and a coordinator making routing decisions. \nThe language model queries were performed by the workers themselves, each receiving a set of context created upstream by a different process.\nAll in all, the whole thing was about a thousand lines of Python. I've seen demo agents with more code than that. Every part was traceable.\nWhen something behaved unexpectedly, we'd usually find the problem in under an hour because there was no magic to look through. Just code with a clear path through it.\nThat system was built bottom-up. The goal was defined, but the architecture wasn't derived from it. Components were designed first, evaluated on their own, and then composed in order to implement the desired functionality. The latter is the most important aspect, not the former.\n···\n## Where I Think This Is Going\nAs far as I can tell, the direction we’re heading in is slowly shifting away from “agent frameworks” and toward proper infrastructure, with systems for evaluation, model routing, fallbacks, and state management.\nAt least some of it already exists out there. The majority is yet to come as people solve hard production problems in this space.\nThe thing I see over and over again is that people building the most reliable systems rarely even use the best models. What they have instead is a clear understanding of everything that happens inside their systems.\nThe model used by such a system can be GPT-4, but it may as well be a small local model. It matters little when everything else works properly.\nWe’re moving from treating the model as the product to treating the system as the product. The model matters, but it’s only one component among many.\nMost agents don't fail because the model wasn't good enough. They fail because the system around the model was designed backwards, starting from what the agent should do and assuming the architecture would sort itself out. \nIt doesn't. \nBuilding it the right way around, components first, behavior second, is what separates the systems that hold up from the ones that look impressive until they don't.\n···\n## Before you go!\nI write more about the real engineering decisions behind AI systems, where abstractions help, where they hurt, and what it takes to build reliably. \nYou can [**subscribe to my newsletter**](https://thatcsguy.substack.com/?r=79lldp&utm_campaign=pub-share-checklist) if you'd like more of that.\n**Connect With Me**\n  * [LinkedIn](http://www.linkedin.com/in/nwekebenjamin)\n  * [Substack](https://thatcsguy.substack.com/)\n\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Agentic AI](https://towardsdatascience.com/category/artificial-intelligence/agentic-ai/)\n# Most AI Agents Fail in Production Because They’re Built Backwards\nGood models don't save bad architecture, and most teams learn that the hard way.\n[Benjamin Nweke](https://towardsdatascience.com/author/benjaminnweke/)\nMay 27, 20268 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/05/ChatGPT-Image-May-23-2026-05_34_02-PM.jpg)\nImage by author (Generated with ChatGPT)\nThe first time I saw a multi-agent system seriously fail in production, it wasn’t dramatic. There was no crash. No error message. The system just kept running and producing outputs that looked reasonable until someone actually read them carefully enough to notice something was off.\nWhen we decided to look into it, it took us two days' worth of debugging to figure out what was going on. Funny enough, the model wasn't hallucinating, and the input-output tools were delivering the correct results.\nThe problem, when we finally found it, was architectural. The model and the tools were set up correctly, but the idea was that reasoning would tie the whole thing together, which, as you would guess, obviously failed.\nTurns out reasoning does not do that sort of thing.\nThat experience is what I keep coming back to when I think about why so many AI agents that work in demos don't really survive real-world use.\nIt's not a capability problem. \nIt's an architectural one.\nAnd if you've read my previous piece here on TDS, [Why AI Engineers Are Moving Beyond LangChain to Native Agent Architectures](https://towardsdatascience.com/why-ai-engineers-are-moving-beyond-langchain-to-native-agent-architectures/), the pattern should sound familiar: systems built top-down, from goal to tools to model, with the quiet assumption that intelligent behavior fills in the gaps.\nThat assumption is what \"built backwards\" means. And it's more common than most teams realize until something breaks.\nLearn this step by step with the interactive [AI Agents](https://roadmap.sh/ai-agents) roadmap.\n## Agents Aren't Entities. They're Systems.\nA production AI agent isn't a single intelligent thing.\nRather, there is a set of interacting pieces with different responsibilities, failure modes, and levels of observability.\nThe LLM is one of those components, not the whole system. Just one piece of it.\nIt may sound obvious when you say it out loud. But the \"autonomous agent\" framing that dominated 2023 and most of 2024 kept pulling engineers toward a different mental model: one entity, one reasoning loop, everything handled by the model.\nAll you need is tools, a good system prompt, and a hope that everything will fall into place.\nIn contrast, engineers who have shipped real AI-based products rarely describe their systems that way. What they actually describe sounds a lot more like distributed systems architecture. \nNot because they read a book about design patterns, but because they got burned enough times that they started putting structure more seriously in their workflow.\nBuilding top-down, starting from \"what should this agent do\" and working backwards into tools and prompts, is quick to get started.\nIt's also how you end up with a system where the model is responsible for too much, and nothing is individually debuggable.\nThe architecture was decided by the goal, not by the engineering requirements. \nThat's the backwards part.\n···\n## So What Really Goes Into a Production System?\nThe abstract version is easy to nod along to. Here's what it actually looks like.\nEvery production AI system I have seen that works cleanly has something like a **decision layer** , whether the team named it like this or not. It's the part where the model lives and does its actual job.\nThe instinct is to push everything into this layer: parsing requests, managing memory, handling retries, resolving tool failures.\nThis is okay if you’re working in a Jupyter notebook. In production, under load, with real users, this becomes the part of your system where everything is everyone's fault, and most times, nothing can be debugged.\nThe decision layer should do one thing well, and that is deciding what to do next, given a certain context that is already prepared for it.\nThat's the whole job.\nWho prepares the context? Something else. Who acts on the decision? Also something else.\nThat \"something else\" is the orchestration layer, and in most well-built systems, it’s genuinely just code: conditionals, asynchronous runners, retry handling, queue routing, maybe even a state machine depending on how involved the workflow is.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/05/mermaid-diagram-2026-05-23-171820-1024x490.png)\nInstead of expecting the model to do everything, treat it like just another component. Here, standard code does the heavy lifting with state and tools, so the LLM only has to worry about making the next decision. Image by author.\nMany teams reach for frameworks here because bare orchestration code feels too simple, like surely there's supposed to be more infrastructure.\nThere usually isn't. \nThe less magic this layer contains, the faster you'll find bugs when they appear. And they will appear.\nFrom experience, I learned this the hard way on a project where the orchestration lived inside a framework's execution model. Something was retrying tool calls in a way that was corrupting state downstream. \nWe spent two days finding the issue. Two days for a bug that could have been resolved in no time at all if the retry logic had been three lines of Python I wrote myself.\nThis leads us to the tools and execution layer, where all communication happens.\nNow, the **tools and execution layer** is where things talk to the outside world. This layer usually has just one job, and that is to take a well-defined input and then produce a predictable output.\nBut the failure I kept seeing, and kept repeating, honestly, was tools that tried to be helpful by doing more than one thing. A single function that calls an API, updates a cache, and does other things. \nIn a setup like that, when it breaks, you don't know where. Even when you try to replace the API, you're untangling logic that shouldn't have been tangled in the first place.\n**Memory and state** is where I'd push hardest, because it's where most teams are most underprepared. \nMost teams think about memory as \"what the model knows.\" The more important question is what the _system_ knows, and whether that knowledge is current.\nI remember one day when it took me an afternoon to debug what seemed to be a simple \"model hallucination.\" The model had kept referring to user preferences, which, however, had been updated twenty minutes ago.\nThat's not a model problem. \nThat's a systems problem. \nAnd it's surprisingly common.\nIn multi-agent systems, specifically, shared state is where subtle failures breed. One agent updates something. The others don't know.\nEveryone proceeds confidently in slightly different directions. The output looks almost right, which is [almost worse than looking wrong](https://towardsdatascience.com/the-multi-agent-trap/).\nAnd then there's **evaluation and observability** , which almost everyone always puts off until something goes wrong. I've been guilty of this, too.\nThe difference I keep in mind is that logging tells you what happened. Observability tells you [_whether what happened was correct_](https://www.datadoghq.com/state-of-ai-engineering/). In a deterministic system, those are close to the same thing.\nIn an AI system, it’s not. You have to be able to follow the specific request from start to finish, including what information the model had to consider, what decision it made, what external API call it invoked, and how it acted upon its response.\n···\n## Building It the Right Way Around\nIt starts with the top-down approach: I want an agent to do X, so I'll give it the tools, a nice system prompt, and if the model is smart enough, it will be fine.\nAnd this is exactly what people use to make prototypes, and why wouldn't they? They are not wrong.\nBut here's the thing: the problem is that it treats the architecture as a consequence of the goal rather than as something you design deliberately.\nThen the system grows. You know, more tools, more workflows, more edge cases, more users, and suddenly there’s no real foundation underneath any of it.\nBottom-up is more time-consuming, but it’s far more comfortable.\nYou start with the basic building blocks and make sure they actually work. Then you figure out what each part should communicate, what data it owns, and what it’s responsible for. \nEventually, the system takes shape naturally from the interaction of its parts.\nThis isn't a \"real engineers build everything from scratch\" argument. It's not even about tooling at all, actually. It's about the mental model you're building with.\nI've seen engineers use sophisticated frameworks and build clean systems because they understood what each layer needed to do.\nI have also seen engineers write vanilla Python and build an undebuggable mess because they were still thinking in terms of \"the agent decides everything.\" The tools follow from the model in your head, not the other way around.\nThe most robust multi-agent system I have had the opportunity to work with closely had almost no AI-specific infrastructure. When I first saw the repo, I honestly assumed I was looking at the wrong codebase. \nA message queue, worker processes with distinct scopes, shared state storage with explicit read/write contracts, and a coordinator making routing decisions. \nThe language model queries were performed by the workers themselves, each receiving a set of context created upstream by a different process.\nAll in all, the whole thing was about a thousand lines of Python. I've seen demo agents with more code than that. Every part was traceable.\nWhen something behaved unexpectedly, we'd usually find the problem in under an hour because there was no magic to look through. Just code with a clear path through it.\nThat system was built bottom-up. The goal was defined, but the architecture wasn't derived from it. Components were designed first, evaluated on their own, and then composed in order to implement the desired functionality. The latter is the most important aspect, not the former.\n···\n## Where I Think This Is Going\nAs far as I can tell, the direction we’re heading in is slowly shifting away from “agent frameworks” and toward proper infrastructure, with systems for evaluation, model routing, fallbacks, and state management.\nAt least some of it already exists out there. The majority is yet to come as people solve hard production problems in this space.\nThe thing I see over and over again is that people building the most reliable systems rarely even use the best models. What they have instead is a clear understanding of everything that happens inside their systems.\nThe model used by such a system can be GPT-4, but it may as well be a small local model. It matters little when everything else works properly.\nWe’re moving from treating the model as the product to treating the system as the product. The model matters, but it’s only one component among many.\nMost agents don't fail because the model wasn't good enough. They fail because the system around the model was designed backwards, starting from what the agent should do and assuming the architecture would sort itself out. \nIt doesn't. \nBuilding it the right way around, components first, behavior second, is what separates the systems that hold up from the ones that look impressive until they don't.\n···\n## Before you go!\nI write more about the real engineering decisions behind AI systems, where abstractions help, where they hurt, and what it takes to build reliably. \nYou can [**subscribe to my newsletter**](https://thatcsguy.substack.com/?r=79lldp&utm_campaign=pub-share-checklist) if you'd like more of that.\n**Connect With Me**\n  * [LinkedIn](http://www.linkedin.com/in/nwekebenjamin)\n  * [Substack](https://thatcsguy.substack.com/)\n\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657271062_x78p7k.jpeg)\nWritten By\n## [Benjamin Nweke](https://towardsdatascience.com/author/benjaminnweke/)\nBenjamin Nweke is a Computer Science graduate and technical writer exploring AI, machine learning, and data science. He writes about building reliable AI systems, model evaluation, agentic architectures, and the practical challenges that appear when machine learning moves beyond experiments. His work focuses on making technically complex ideas clear, useful, and grounded in real implementation.\n[See all from Benjamin Nweke](https://towardsdatascience.com/author/benjaminnweke/)\n[AI Agents](https://towardsdatascience.com/tag/ai-agents-2/)[Artificial Intelligence](https://towardsdatascience.com/tag/artificial-intelligence/)[Data Science](https://towardsdatascience.com/tag/data-science/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Software Architecture](https://towardsdatascience.com/tag/software-architecture/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fmost-ai-agents-fail-in-production-because-theyre-built-backwards%2F&title=Most%20AI%20Agents%20Fail%20in%20Production%20Because%20They%E2%80%99re%20Built%20Backwards)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fmost-ai-agents-fail-in-production-because-theyre-built-backwards%2F&title=Most%20AI%20Agents%20Fail%20in%20Production%20Because%20They%E2%80%99re%20Built%20Backwards)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fmost-ai-agents-fail-in-production-because-theyre-built-backwards%2F&text=Most%20AI%20Agents%20Fail%20in%20Production%20Because%20They%E2%80%99re%20Built%20Backwards)\n\n\nTowards Data Science is a community publication. 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