AI coding agents from companies like OpenAI, Anthropic, and Google are now capable of developing complete software applications, from writing code to running tests and fixing bugs. While these tools offer powerful new capabilities for developers, they operate on complex principles that are important to understand to use them effectively and avoid potential pitfalls.
These systems are not a single, magical AI but rather sophisticated programs that coordinate multiple large language models (LLMs) to tackle complex software projects. Understanding their internal mechanics, especially their limitations around memory and context, is key to leveraging their power without introducing new problems into a project.
Key Takeaways
- AI coding agents are complex systems that use a "supervising" AI to manage multiple specialized AI models working in parallel.
- A primary limitation is the "context window," or short-term memory, which agents overcome using techniques like context compression and external notes.
- Human planning and oversight are critical, as agents can produce flawed or inefficient code without proper guidance.
- Recent studies suggest these tools may not always increase productivity for experienced developers working on familiar codebases.
The Architecture of an AI Coder
At the heart of every AI coding agent lies a large language model, a neural network trained on immense datasets of text and programming code. This model is essentially a highly advanced pattern-matching engine. When given a prompt, it generates a statistically probable continuation, which in this case is functional code.
However, a single LLM isn't enough to build an application. Modern coding agents are structured as a program wrapper around multiple LLMs. A primary "supervising" agent interprets the human developer's instructions. It then breaks down the main goal into smaller, manageable subtasks.
These subtasks are assigned to several parallel LLMs, or "workers," which can use software tools to execute their instructions. For example, a worker might be tasked with listing files in a directory, fetching data from a website, or writing a specific function. The supervising agent monitors their progress, evaluates the results, and adjusts the plan as needed.
Sandboxed Environments for Safety
When operating, these agents need to interact with a computer's file system and network. To prevent accidental or malicious actions, they typically run in a sandboxed environment. This is an isolated virtual space, often a cloud container, preloaded with the project's code. Within this sandbox, the agent can safely read and write files, run commands, and execute code without affecting the user's main system.
The Challenge of 'Forgetting'
One of the biggest technical hurdles for AI agents is their limited short-term memory, known as the "context window." An LLM can only process a finite amount of information at once. The entire conversation history, all generated code, and the model's own internal reasoning must fit within this window for every single interaction.
As a project grows, the context can become overloaded, leading to a phenomenon researchers call "context rot." The model's ability to accurately recall details from earlier in the process diminishes, causing it to forget key architectural decisions or previously fixed bugs.
The Cost of Context
Processing information in the context window is computationally expensive. According to engineering documentation from Anthropic, a multi-agent system can use approximately 15 times more processing tokens than a standard chatbot interaction for the same task, making their use a significant financial consideration.
Tricks to Improve AI Memory
To work around these memory limitations, developers of AI agents have engineered several clever solutions:
- Tool Use: Instead of loading a massive database into context, an agent is trained to write a specific query to extract only the necessary information. It might also use standard command-line tools like `head` or `tail` to peek at large files without reading them entirely.
- Context Compression: When the context window nears its limit, the system can automatically summarize the history. This process preserves critical information like architectural plans and unresolved issues while discarding less important details, such as redundant command outputs.
- External Notes: Developers can create special files, such as `AGENTS.md`, within the project directory. These files act as a persistent set of instructions or notes that the agent can refer back to after its context has been compressed, helping it re-orient itself.
The Human Element Remains Crucial
Despite their advanced capabilities, AI coding agents are tools that require skilled human oversight. The practice of generating code without understanding how it works, sometimes called "vibe coding," is considered risky for professional software development. Shipping code you haven't written or vetted yourself can introduce security vulnerabilities and long-term maintenance issues.
Independent AI researcher Simon Willison has argued that the developer's responsibility is shifting.
"Almost anyone can prompt an LLM to generate a thousand-line patch and submit it for code review. That’s no longer valuable. What’s valuable is contributing code that is proven to work."
Best practices now recommend a structured approach. Before asking an agent to write any code, a developer should first instruct it to read the relevant files and formulate a detailed plan. Without this initial planning phase, agents tend to jump to the quickest solution, which may not be the most stable or scalable one.
Are AI Agents Always Faster?
While the promise of AI coding assistance is accelerated development, the reality can be more nuanced. A randomized controlled trial published by the research organization METR in July 2025 found that highly experienced open-source developers took 19 percent longer to complete tasks when using AI tools.
The study noted several important factors: the developers were experts with deep familiarity with their codebases, and the AI models used have since been updated. However, the findings suggest that for seasoned programmers working in familiar territory, the overhead of managing and verifying AI-generated code can sometimes outweigh the benefits.
For now, the ideal use for coding agents may be in building prototypes, proof-of-concept demos, and internal tools where the stakes are lower. As these systems have no true agency or accountability, the ultimate responsibility for the final product remains firmly with the human developer guiding the process.