Ever since the start of 2025, Agentic AI coding tools like Cursor, WindSurf, and Trae have been sweeping through the development world. Yet, much like earlier generations of GenAI technology, these new Agentic AI tools often dazzle when creating small demos but stumble when put to the test in real-world, production-scale scenarios. Generating prototypes of a thousand lines of code is effortless, and the self-iteration, automated debugging, and rapid delivery all appear smooth and impressive. Once you move into actual software engineering—say, once your codebase exceeds five thousand lines—the magic fades. The AI seems to be groping around in the dark, unable to retain a cohesive sense of the overall architecture or the logical decisions made so far, ultimately leading to odd bugs that only get resolved through frequent human intervention.
This naturally raises a question: Is Agentic AI truly the game-changing revolution it’s touted to be, or just another overhyped bubble? This article delves into two fundamental questions:
- Why does Agentic AI appear to have a project-size limitation?
- What technical strategies could possibly break through that size restriction, enabling AI to truly perform in large-scale engineering contexts?
A Look at Three Major Failure Patterns: Spatial Mismatch, Temporal Forgetfulness, and Reinventing the Wheel
To understand why these issues arise, we first need to identify the concrete failure patterns Agentic AI exhibits in large-scale projects. Generally, there are three.
The first pattern emerges in the spatial dimension of software development, where modifying one file ends up breaking another. In small demos of just a few hundred lines, Cursor can generate a workable version in a single pass. Everything seems flexible and robust. But once a project expands to several thousand or tens of thousands of lines, the AI often makes elementary mistakes. For example, it may fail to notice that a function already exists in another file and just rewrite a duplicate. Or it modifies module A during one iteration without realizing that module B depends on A’s interface, so A and B become incompatible. Yes, Agentic Workflow can catch the resulting error messages, but the AI seems to have lost its edge, requiring multiple rounds of iteration to track down and fix the problem.
This limitation is partly due to the size constraints of the context window. Tools like Cursor build their context windows automatically following certain rules. When we ask the AI to write code, if an existing file with relevant features isn’t included in that window for some reason, the AI simply can’t factor that knowledge into its implementation. The result is this kind of mismatch.
The second pattern is a time-based cycle of repetition: fix, then backtrack, then fix again. If you’ve used Agentic AI for relatively complex projects spanning days of development, you may have noticed the AI sometimes cancels its own corrections. For instance, it might introduce fix X to address error A. Then, after a while, as it makes other modifications, it casually reverts X, causing error A to recur. By the later stages of a project, the AI seems to grow less efficient, taking longer and longer to debug. This back-and-forth cycle not only wastes time and energy, but can bring an entire project to a halt.
The core issue is, again, the limited context window. Most Agentic AI tools rely on the context window as their memory of prior code and decisions. In the early stages, error A and fix X are still in the window, so the AI remembers to maintain X. But once the conversation drags on, or a new session is started, that memory drops out of scope. The AI no longer remembers why X was important, causing it to revert the fix and recreate the original error.
The third pattern becomes glaring when these tools interface with an existing codebase they didn’t write, particularly when the total code has grown large. Without a global understanding of the high-level design, the AI struggles to pinpoint which file needs updating for a given requirement. And it lacks any sense of the code’s development history. It often manifests as a bad habit: instead of reusing or understanding existing code, it just writes new functionality from scratch. Sometimes it even duplicates functionality that it itself wrote earlier, leading to multiple, conflicting versions of the same feature.
In short, once the codebase surpasses a few thousand lines, the AI’s limitations in long-term memory begin to drag down productivity. The more the project grows, the more changes accumulate, and the more the AI feels like it’s wandering through a fog—undoing its own work, forgetting what it tried last time. Next, we’ll explore the underlying reasons for this “context amnesia.”
Core Constraint: Reliance on Short-Term Memory in the Context Window
To understand why Agentic AI underperforms in large projects, we need to look at its memory mechanism: in most current tools, memory of previously written code or decisions is limited to what can fit into a context window. Whether the tool uses retrieval-augmented generation (RAG) or an Agentic method that automatically reads files, if some vital piece of information isn’t included in the window, the AI forgets it. It then repeats past mistakes all over again.
As projects get bigger, you might think of a solution like constant refactoring—splitting code into smaller pieces so the AI can handle it in bite-sized chunks for each output. While that can help in specific cases, it doesn’t address the fundamental problem of “lack of global design understanding.” Even with neatly arranged code, the AI still relies on short-term context. Once the context window is full, it loses track of previous logic. In other words, refactoring only reduces how much the AI must handle at any one time. It doesn’t provide a true, callable memory that persists beyond the window.
Thus, today’s GenAI, which works primarily via context windows, behaves like a fish with a seven-second memory, forgetting any design decisions once they swim beyond the current context. Within that limited scope, it writes solid code, but with no long-term memory, an Agentic AI faces functional collapse once a project surpasses a few thousand lines. The core challenge is how to move beyond total dependence on short-term context and offer a different mechanism for persistent memory.
As with many technical puzzles, we can draw inspiration from our own experiences. Humans also have very limited short-term memory, yet we handle large-scale projects by using external documentation—our equivalent of “putting it in writing.” Documents store overall design decisions, historical choices, and tribal knowledge, enabling us to avoid repeated mistakes and maintain a broader perspective. This suggests a similar approach for Agentic AI: giving it a form of long-term memory outside the fleeting context window.
Potential Ways Forward: Building Long-Term Memory into Agentic AI
How to actually give AI a long-term memory is still an open question, without any one-size-fits-all solution. But we can outline a few possible starting points:
One idea is to adopt Document-Driven Development. This follows the principle that “even a rough record is better than a good memory,” mirroring how we humans rely on external documentation. We can use prompt engineering, for example, to inform the AI that delivering a project document is as crucial as delivering code. The AI should be expected not just to write code, but also to maintain an accompanying document at all times. This documentation would define external interfaces, product decisions, technical frameworks, and high-level designs, while also preserving historical context—previous attempts, their outcomes, and so on. When the AI then writes or modifies code, it can more effectively leverage the context window by referencing the document for an overview of the system. It no longer has to repeatedly stuff every code file into that window. Likewise, the AI can adopt a workflow of first updating the document, then revising the code to match, keeping them in sync. This long-term memory doesn’t have to be purely natural language; it could be UML diagrams, protocol overviews, or even a JSON-based structure that both humans and machines can interpret.
A second approach sees this documentation as valuable for multi-agent setups as well. In a multi-agent architecture, different Agents benefit from their own separate context windows. The tricky part is establishing efficient, precise communication among them. A shared long-term memory can serve as this communication channel, providing each Agent with high-level knowledge of what the others have done without bogging them down in one another’s internal details. It thus becomes a single source of truth that spans all Agents. Of course, this also means thinking carefully about concurrency and consistency—using locking mechanisms or other methods to ensure Agents working in parallel don’t create conflicting states. It’s reminiscent of multi-user collaboration, except now with AIs. Automatic merging, historical tracking, and diff analyses all become intriguing directions to explore.
A third perspective is the role that humans should play in this workflow. In the near term, we likely shouldn’t expect an Agentic AI to run an entire system autonomously. Even skilled human developers need ongoing guidance and coaching from team leads. Our attitude toward AI shouldn’t be “fire and forget,” giving it a task and walking away. Instead, we should guide, correct, and nurture the AI over time, letting it accumulate experience. The advent of long-term memory opens up new possibilities for this relationship. Rather than relying solely on conversation windows (i.e., chat interactions), we could treat the long-term memory as a shared medium of communication. If the AI’s behavior diverges from our expectations, we don’t have to correct it line-by-line in a chat. We can simply revise the design document and instruct the AI to refactor any code that no longer matches the updated doc. Or if it repeatedly makes the same mistake, we can encode the relevant lesson in its long-term memory. That fix then becomes immediately available to all other AIs working on the project. In other words, this isn’t just about “the AI writing some docs and consulting them.” It’s also about integrating long-term memory into human–AI collaboration. Currently, tools like Cursor are oriented around the AI as the driving force, with humans providing only a simple prompt. For larger projects, though, a more collaborative balance might be preferable, where humans design the overall documentation structure and key summaries, and the AI fills in details. When a major overhaul is needed, the AI drafts an updated document, then humans refine it and confirm. Only then does the AI proceed to code changes. This way, we’re not restricted to the AI’s short-term memory, and we don’t get bogged down in every tiny detail.
Conclusion
Agentic AI is still in its early stages. It’s sparked tremendous excitement and delivered productivity gains, but it also introduces new complications. This article doesn’t claim to offer a complete, out-of-the-box solution—if anything, it raises more questions than it answers.
Defining how to implement true long-term memory will likely be a gradual process. To pinpoint the bottlenecks and possible paths forward, our priority might be to incorporate more transparency, interpretability, and debuggability into existing tools. For instance, we could expose the context windows in Cursor or Trae, so that any failures are easier to diagnose. We could also invite community involvement, giving advanced users the ability to adjust or fine-tune these windows, or even experiment with new ideas for long-term memory. Of course, for the companies developing these Agentic AI tools, such openness might present commercial risks. But openness and collective effort could be a practical way to accelerate the community’s understanding of these systems and their best practices in the short term.
In sum, Agentic AI tools indeed face a perilous gap once they exceed about five thousand lines of code, and the root cause is their reliance on short-term context windows. To truly rise above this barrier, document-driven development, openness, and community collaboration are promising avenues to explore.
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