Why AI Developers Keep Forgetting When Entrusted with Complex Tasks: The Secret of 'Constraint Decay'

Imagine this. You arrive at work this morning and instruct your newly onboarded, super-intelligent AI junior employee: “Build a simple suggestion board for our employees to use on the company homepage.” The smart AI employee whips up perfectly working code and displays it on the screen in just 10 minutes. Amazed by that incredible speed, you decide to assign it a real-world task while you’re at it. “That’s brilliant! Now, rebuild it so it strictly matches our company’s main database rules, applies the latest security patterns, and perfectly integrates with our existing login system.”

But then, a surprising thing happens. The AI, which seemed like a genius developer just a moment ago, suddenly starts writing nonsensical code that breaks the existing company system, or wanders aimlessly, completely forgetting the core security rules you just mentioned. It might even just freeze up and output absolutely nothing on the screen.

This frustrating situation is not a simple error or a temporary bug. It is a fatal weakness recently discovered by AI researchers observing AI coding agents (AI programs that plan and write code autonomously), such as ChatGPT, in back-end development (the development of server and database areas invisible to users)—this is the phenomenon of ‘Constraint Decay’. Amidst the public expectation that AI could 100% replace human developers any minute now, this phenomenon provides clear clues as to where AI’s true current limitations lie.

Why It Matters

Reading tech news these days, you hear everyday that AI is completely changing the game of software development. Many people often worry, “Won’t human developers be obsolete in a year or two?” In fact, when it comes to changing the color of a button on a website or building a simple screen, AI is much faster and more accurate than a human.

However, creating production-level software that we safely use in our daily lives is not just a matter of typing plausible code onto a blank slate. Simply put, it’s the difference between building a flashy-looking model house and constructing a sturdy, real apartment building where actual people live and use water and electricity.

Real software must safely separate and store the data of millions of users, communicate with legacy banking systems without clashing, and strictly adhere to established frameworks so that other colleagues can easily modify the code later. In short, it must protect complex, rigorous rules and structures as if its life depended on it. For current AI to become a true ‘fellow developer’ that can be fully trusted with complex tasks, the ability to not lose its way in such demanding environments is absolutely essential. The real reason why we cannot hand over a company’s core business systems entirely to AI is hidden right here.

The Explainer

Scientists intensively researched the cause of why AI behaves erratically when rules increase, and they named this phenomenon ‘Constraint Decay’. [2605.06445] Constraint Decay: The Fragility of LLM Agents in Backend Code Generation

Let’s take an easier look inside the AI model to see why this phenomenon occurs. AI’s neural network layers (the information processing structure of artificial intelligence connected like the neurons in a human brain) are like the filters in a smartphone photo-editing app. Just as an original photo passes through a brightness filter, a color filter, and a noise reduction filter in turn to become a wonderful final product, your simple input of “build a suggestion board” passes through hundreds of layers to be transformed into programming code. At this time, the AI understands the code by breaking it down into tiny puzzle pieces called ‘tokens’ (the smallest unit of data by which artificial intelligence reads and writes text). The principle is to continuously append pieces by probabilistically calculating the most natural piece to come next out of hundreds of thousands of puzzle pieces.

But what does it mean for ‘constraints’ to be continuously added here? Going beyond simply completing a nice picture, it is like stacking dozens of demanding instructions such as “the red puzzle piece must not be placed next to the blue puzzle piece, the company logo must be printed on the back of the corner pieces, and you must draw a black border every 10th row.” As instructions pile up one after another, the AI’s information processing capacity becomes overloaded, eventually causing it to slowly forget the important rules mentioned earlier one by one.

As an analogy, imagine fine-tuning (the process of giving AI additional training for a specific purpose) a smart dog that has just finished basic obedience school to teach it a new trick. If you give the dog that has finished basic training just one command, “Sit!”, it obeys very well. Under loose specifications where it only has to perform one task, it demonstrates excellent ability. But what if you apply multiple conditions all at once: “Sit, then raise your right front paw, wag your tail three times, and then bark exactly once only when I clap twice”? Even the smartest genius dog will fall into confusion, spin around in circles, and eventually completely forget even the first command to “sit” and just lie down on the floor.

The brain of an AI coding agent is exactly the same. In a free environment with few rules to follow, it writes code excellently. However, the situation changes drastically when it is deployed into back-end development, which rigorously handles the invisible servers and databases. Heavy ‘structural constraints’—such as data rules determining what data is stored where and how, architecture patterns (standard design methods that constitute the framework of software) that serve as the backbone of the entire system, and Object-Relational Mapping (ORM, a translation technology linking the coding language to the structure of the database)—tag along throughout the entire task. When demanding requirements like this accumulate, the AI’s coding performance plummets as if falling off a cliff, ignoring initial instructions. [2605.06445v1] Constraint Decay: The Fragility of LLM Agents …

In particular, the research team dissected the causes of AI failures in detail, as if performing surgery. As a result, they revealed that the seeds of fundamental errors primarily originate from defects in the ‘Data-layer’, a very deep and important area that physically stores and retrieves data. Constraint Decay: The Fragility of LLM Agents in Backend Code … In complex tasks that require a continuous train of thought step by step, a small mistake in a previous step snowballs into a fatal error in the next step, eventually causing the entire set of rules to collapse. Computer experts refer to this phenomenon as ‘Reasoning decay’ (a phenomenon where the flow of logical thought gradually breaks down). Why LLM Agents Fail: Four Mechanisms of Cognitive Decay and the Reasoning Harness Layer - DEV Community

Where We Stand

This fascinating and important research was conducted as part of the ‘AI4SWeng’ project funded by the European Union (EU), led by two experts, Francesco Dente and Dario Satriani. Can LLM coding agents follow strict architectural rules? In …

They set up a massive test stage to objectively prove “how long can AI well remember and follow various strict structural constraints?” They built environments for a staggering 8 different web frameworks (standard architectural toolsets for making websites quickly and safely). Then, they had the AI perform 80 greenfield generation tasks writing code from a completely blank slate, and 20 additional tasks appending new features to existing code. Constraint decay: The fragility of LLM agents in backend code … To use a human analogy, it’s like throwing them unified building codes, having them build 100 houses in 8 different environments, and then looking through a magnifying glass to see how thoroughly they followed the regulations.

The conclusion of the experiment was very cold and clear. According to the paper, these results send a definitive message to general users as well. Current AI coding agents are “highly reliable magic tools for rapid prototyping to quickly bring ideas in your head to the screen and test them, but they are still in a state where they cannot be fully trusted and deployed for production-grade back-end development, where rules and structures are life.” Constraint decay: The Fragility of LLM Agents in Backend Code Generation

There is a blind spot here that we easily fall for. Countless ‘AI coding performance benchmark tests’ currently on the market simply grade the code generated by AI as correct if it functionally ‘just works’. Even though invisible non-functional elements—such as complex architectural patterns and strict security rules essential in a company’s actual production software—are ignored, people mistakenly believe that “AI got a perfect score on the coding test” and has perfectly replaced developers. Constraint Decay: The Fragility of LLM Agents in Backend Code …

An even more bizarre and severe form of error also exists. Beyond spitting out nonsensical code because it’s overwhelmed by constraints, the AI exhibits symptoms of shutting its own mouth entirely. The researchers also discovered instances of ‘Output stalling’ (an error where it fails to produce results and stops due to overload)—where instructing the AI agent to write a document with vast and complex formatting requirements caused it to silently output only a blank response and stop working, without any error warning on the screen. When Agents Go Quiet: Output Generation Capacity and Format-Cost Separation for LLM Document Synthesis It looks exactly like a person who goes blank from a bombardment of complex instructions from their boss and gives up answering altogether.

What’s Next

Does this mean AI is doomed to never be able to develop back-end software intertwined with complex rules? It is too early for us to give up all hope. Analyses by experts studying system architecture point to a completely different, positive solution.

One sharp analyst points out that this frequent failure phenomenon is not due to a ‘lack of intelligence’ in the AI itself. The real problem is poor design of the ‘architecture’ (the operational structure of the system) that puts the AI to work. They emphasize, “Failures do not originate inside the AI’s brain. The real cause lies in what information we feed the AI initially, and what filtering processes we put the AI’s output through afterwards.” The LLM Reliability Paradox: Agents Aren’t Broken, Your Architecture Is In other words, it means the issue is not a breakdown of the AI itself, but a problem with the ‘workflow’ through which we employ the AI.

Therefore, future AI coding tools will break away from the brute-force approach of simply increasing brain size. Just as a meticulous human developer constantly runs code after every line to see if it works and checks if it fits the rules, these tools will necessarily be equipped internally with an ‘independent Validation layer’ (a system acting as an overseer checking code quality and rule compliance) that immediately reviews the code spat out by the AI right beside it and catches errors. Already in the development field, a double or triple safety net approach—where AI outputs are not blindly trusted but must pass through separate safeguards and parsers (tools that read code and analyze it for grammatical correctness)—is becoming the mainstream. Why Does the LLM Stop Computing: An Empirical Study of User-Reported Failures in Open-Source LLMs

AI’s Take

The AI coding tools we encounter in our daily lives are not magic wands, but more like top-of-the-line, high-performance ‘power drill sets’. When it comes to quickly assembling an IKEA desk, they offer peak efficiency that overwhelms a struggling human. However, if you hand them the architectural blueprints for a complex, large-scale hospital and tell them to build a massive building, they will cause a catastrophe, drilling holes in the wrong pillars without even knowing the proper sequence.

Although the ‘Constraint Decay’ phenomenon—where it forgets constraints—currently looks like AI’s Achilles’ heel, this is merely an inevitable growing pain in the process of the technology maturing. Before long, AI will evolve into a mature partner that writes its own code while simultaneously strictly censoring its own code.

However, until a true AI architect emerges that perfectly controls errors on its own, the ‘insight’ to establish a project’s large framework, constantly coordinate numerous constraints, and guide it down the right path will remain the most reliable and shining weapon of our human developers. No matter how brilliantly technology advances, the role of directing the giant gears to interlock and turn precisely must ultimately belong to humans.

References

1. [2605.06445] Constraint Decay: The Fragility of LLM Agents in Backend Code Generation
2. Constraint decay: The Fragility of LLM Agents in Backend Code Generation
3. [2605.06445v1] Constraint Decay: The Fragility of LLM Agents …
4. Constraint Decay: The Fragility of LLM Agents in Backend Code … (DeepPaper)
5. Why LLM Agents Fail: Four Mechanisms of Cognitive Decay and the Reasoning Harness Layer - DEV Community
6. Can LLM coding agents follow strict architectural rules? In …
7. Constraint decay: The fragility of LLM agents in backend code … (Eurecom)
8. Constraint Decay: The Fragility of LLM Agents in Backend Code … (CatalyzeX)
9. When Agents Go Quiet: Output Generation Capacity and Format-Cost Separation for LLM Document Synthesis
10. The LLM Reliability Paradox: Agents Aren’t Broken, Your Architecture Is
11. Why Does the LLM Stop Computing: An Empirical Study of User-Reported Failures in Open-Source LLMs