Coding for AI Agents vs. Coding for Human Developers

As AI coding assistants and autonomous agents (often powered by large language models) become more involved in software development, best practices in coding must account for a new “audience.” Traditionally, code is written by and for human developers, emphasizing readability and maintainability for people. In contrast, code intended to be generated or maintained by AI agents may prioritize different qualities to align with an AI’s interpretive capabilities. This report compares the characteristics of good code optimized for AI agents versus code optimized for human developers, focusing on design patterns, code readability, and performance optimizations. We highlight how AI agents’ unique strengths and limitations shift best practices in structure, naming, documentation, and modularity.

Design Patterns and Architectural Style

Human-Oriented Design: Human developers frequently leverage well-known design patterns (e.g. Singleton, Factory, Observer) and layered architectures to manage complexity. These patterns serve as a shared language among developers and ensure qualities like modularity, flexibility, and reuse. A human-written codebase is likely to reflect deliberate separation of concerns and abstract interfaces that make the system extensible. Humans excel at big-picture architectural thinking – they can judge how to split responsibilities between modules and when to apply a pattern for future-proofing. For example, a human developer might implement a Strategy pattern to handle multiple algorithms interchangeably, anticipating the need for scalability.

AI-Agent-Oriented Design: AI code-generation agents, on the other hand, tend to produce code that is correct and idiomatic but not always aligned with a project’s specific design patterns or architecture unless explicitly guided. Current code LLMs often “fail to properly understand the existing design patterns and coding styles of a project”, yielding code that conflicts with the intended architecture. In practice, an AI agent may opt for straightforward implementations rather than abstracting a reusable pattern – for instance, directly instantiating and using classes in multiple places where a human might have created a Factory to manage them. This can lead to structural duplication: the AI might unknowingly write a duplicate of functionality that exists elsewhere, or choose an improper boundary for code reuse. Such design missteps occur because the agent lacks a persistent high-level view of the entire codebase. Humans typically catch these issues and refactor; when collaborating with an AI, a human often must prompt the agent to apply a better design (“A better design would be to…”).

Reusable Structures: In human-centric code, design patterns are intentionally applied to improve maintainability, even if it means introducing extra complexity (e.g. additional classes or layers) that experienced developers will recognize. AI-focused code tends to favor simplicity and explicitness over clever architectural abstractions. Since AI agents can generate boilerplate rapidly, they may duplicate code or logic rather than abstract it, unless guided to follow the DRY (Don’t Repeat Yourself) principle. The result is that agent-written code might be more linear and verbose, while human-written code might be more compact through abstraction. To bridge this, developers working with AI agents often need to explicitly instruct the AI to use a certain pattern or refactor for reuse once the basic functionality is in place. On the positive side, AI agents have encyclopedic knowledge of common frameworks and APIs, so they will usually follow widely-used patterns correctly if those patterns appeared frequently in training data. For example, an AI agent is very capable of producing a standard MVC structure for a web app or using a typical Observer pattern in a GUI, because these are common in code corpora. However, it might struggle with project-specific or novel patterns that deviate from what it has seen. In summary, human developers prioritize architecture and design coherence, whereas AI agents prioritize immediate functionality and rely on guidance to enforce architecture.

Code Readability, Naming, and Documentation

Readability for Humans: Code meant for human developers emphasizes clarity and ease of comprehension through established conventions. This includes descriptive naming, clean formatting, and judicious comments. Humans benefit from self-explanatory identifiers and structured code: for instance, well-chosen function and variable names (using full words or common abbreviations), and code organized into small, purposeful functions. Style guides (indentation, consistent braces/brackets placement, etc.) and practices like limiting function length help humans parse code quickly. Comments and documentation are written to explain non-obvious logic or provide context, but there’s an understanding that “the source code is the documentation” in many cases. In other words, human-written code often strives to be self-documenting; excessive comments can be seen as clutter if they simply restate the obvious. Documentation like design docs or README files exist, but inline documentation is usually kept minimal and must be manually updated, which developers often neglect over time. The result is that human-optimized code leans on readability through code structure itself, with comments only where necessary.

Readability for AI Agents: When the reader or maintainer is an AI agent, code readability takes on a different slant. AI models parse code as text and don’t get “confused” by long variable names or repetitive structures – in fact, explicitness and consistency help the AI. Therefore, code written for or by AI tends to include more redundant clarity that a human might find verbose. For example, an AI-guided approach might favor very descriptive names for everything, even at the cost of verbosity, to remove ambiguity (e.g., naming a variable annualInterestRate instead of rate or r). AI tooling can assist in this regard: there are agents that suggest context-aware variable names, ensuring they are clear, consistent, and meaningful (for instance, recommending clientCount instead of a generic x in a customer-data function). The result is extremely consistent naming across the codebase, which benefits the AI’s pattern recognition and also any human who later reads the code.

Furthermore, AI-focused code often comes with extensive inline documentation and comments compared to typical human-written code. Since an AI lacks long-term memory of the whole project, developers prompt AI agents to include summaries of a module’s purpose and requirements at the top of each file or function docstring. It’s not unusual in an AI-collaborative project to see each file begin with a comment block describing what the module does and any important context. Such thorough documentation is “relatively rare in human-written code”, because humans find it hard to maintain and keep in sync with code changes. An AI agent, however, can be instructed to update documentation every time it updates the code, overcoming the usual problem of stale comments. This means agent-optimized code can maintain up-to-date documentation at a granular level with minimal human effort – a significant shift in best practices. In large AI-generated codebases, this documentation is critical to compensate for the agent’s lack of holistic understanding on each invocation.

Structure and Modularity: Both humans and AI benefit from modular, well-structured code, but the reasons differ. Humans find large, monolithic functions hard to follow, and similarly an AI agent with limited context window can’t effectively work with an extremely long function or file. Good code for AI agents therefore tends to be highly modular: many small, single-responsibility functions or classes that the AI can tackle one at a time. This aligns with human best practices too (e.g., “a single function should handle a single task”), but with AI there is an added practical reason – to fit relevant code within the token limit of the model at any given time. In essence, AI-friendly code is broken into digestible pieces with clear interfaces, which conveniently also aids human comprehension. The difference is that a human might manage to understand a bit of tangled logic by recalling context or drawing on intuition, whereas an AI will strictly perform best when the code is structured in a predictable, well-encapsulated way with all necessary context either in the prompt or in the code comments.

In summary, code optimized for humans focuses on readability by intuition and experience – clean code with minimal necessary comments – whereas code for AI agents favors explicit clarity and consistency – thorough comments, verbose naming, and very regular structure – to ensure the AI correctly parses and manipulates the code. Notably, these practices often improve overall code quality for any reader, but the balance shifts: what humans consider over-documentation or obvious naming might be justified when an AI is the one stepping through the code’s logic.

Performance and Optimization Trade-offs

Human-Centric Performance Trade-offs: Human developers typically follow the mantra of avoiding “premature optimization.” Code is first written in a clear, correct way; performance tweaks are applied only when profiling or requirements show they’re needed. This means that humans often choose simplicity over maximum speed in initial implementations. For example, a developer might use an easy-to-read algorithm with O(n²) complexity for moderate data sizes rather than a more complex O(n log n) algorithm, until it’s proven that the simpler approach is too slow. When performance is critical, humans will indeed employ optimizations – they might use specialized data structures, bit-level operations, or concurrency to speed things up – but these changes come with a maintenance cost and are done judiciously. Crucially, a human can understand the system’s bottlenecks and decide where the trade-off of clarity vs. speed is worthwhile. They will document non-obvious optimizations for future maintainers (e.g., a comment “using a binary search here for performance”) and ensure the design can handle the added complexity.

AI-Agent-Centric Performance Trade-offs: AI agents tend to prioritize correctness and completeness in code generation over micro-optimizations, unless explicitly directed otherwise. An AI writing code will usually produce a straightforward solution that meets the requirements, often including extensive error checking and logging by default. This results in highly robust code (with fewer edge-case bugs) but potentially with some runtime overhead – for instance, the AI might add validation steps that a human might skip in a quick prototype. In many cases, the AI’s solution might be slightly less efficient than what an experienced human would devise, especially if the optimal solution requires a clever insight. For example, the AI might use a simple library sort function for convenience even if a custom tweak could be faster, or it might not automatically apply an advanced algorithmic optimization that wasn’t explicitly in the prompt. That said, AI agents have an immense knowledge of algorithms and APIs; they “know every API of every library that is in common use” and can quickly pull in well-optimized library functions. In some situations, an AI agent may surprise the human by suggesting a more efficient algorithm or a cutting-edge library that the developer hadn’t considered. Indeed, AI pair programmers often introduce optimizations by recalling solutions from a vast corpus of code (for example, using a more efficient data structure or parallelizing a task using a known library). This can improve performance if the AI correctly identifies the opportunity, demonstrating how AI’s breadth of knowledge can enhance efficiency.

The trade-offs thus shift in an AI-centric context. Clarity vs. efficiency: An AI will happily write more verbose code or repeat logic if it makes the solution easier to generate and verify, whereas a human might refactor duplication even if it means a minor performance cost or more abstraction. Conversely, given an objective to optimize, an AI can aggressively refactor code for performance – for instance, unrolling loops or using lower-level optimizations – but it must be explicitly instructed or guided by failing tests/benchmarks to do so. Humans are better at intuitively sensing where the slow parts of a program might be; an AI lacks this intuition and relies on either training data or feedback. This means in an AI-maintained codebase, performance tuning might be an iterative process: the AI writes a correct solution, tests (possibly guided by a human) reveal a bottleneck, and then the AI is prompted to optimize that part. The AI’s strength is that it can refactor code quickly and systematically once pointed in the right direction (e.g., converting a recursive solution to an iterative one for speed, or applying memoization if told the function is called frequently). The human’s role remains crucial in identifying where such optimizations are needed and ensuring they make sense in the broader system context.

Resource Use and Scalability: Another aspect of performance is how code uses memory and CPU resources. Human developers, especially in resource-constrained domains, often write code mindful of memory footprints and edge conditions. An AI agent might not inherently consider memory efficiency unless it was included in its instructions or training examples. For instance, an AI might read an entire file into memory because it’s a simpler coding pattern, whereas a human who knows the file could be huge might stream it line by line. Such differences are not absolute – AI can certainly be told to use memory-efficient patterns, and modern LLMs do have some notion of algorithmic complexity – but humans are typically better at scenario-based judgment (like “what if the input is 10x larger?”). Therefore, when optimizing code for an AI agent’s involvement, developers often make those concerns explicit (e.g., prompt the AI with performance constraints or incorporate tests for large inputs).

In summary, human-optimized code tends to strike a balance favoring maintainability, adding performance tweaks carefully, whereas AI-generated code prioritizes correctness and completeness first, and relies on guidance to reach equivalent performance optimization. The AI’s expansive knowledge can inject high-performance techniques when prompted, but it doesn’t inherently prioritize execution speed over clarity the way a human might in performance-critical sections. The end result is that a partnership of human insight and AI’s extensive knowledge can produce code that is both clean and efficient, but the path to get there differs from a purely human workflow.

Conclusion and Summary

The emergence of AI agents in software development is shifting some coding best practices. Good code for human developers and good code for AI agents share the fundamental goals of correctness and maintainability, but they emphasize different qualities:

• Design Patterns: Human-oriented code uses design patterns as a communication and design tool, enforcing architecture and DRY principles from the start. AI-oriented code might default to simpler or repetitive designs unless explicitly guided, requiring humans to enforce higher-level patterns and refactor structural issues. Ensuring that an AI is aware of or instructed in the project’s design conventions is key to maintaining architectural consistency.

• Readability & Documentation: Code for humans optimizes for readability by other humans – clear structure, meaningful (but not overly verbose) naming, and minimal but helpful comments. In contrast, code for AI agents may include more verbose naming and thorough documentation embedded in the code (which the AI can maintain automatically) to provide context within the agent’s limited view. Consistency in style and naming is especially crucial for AI parsing. Interestingly, these agent-focused practices (like exhaustive docstrings) can improve readability for future human maintainers as well, albeit at the cost of writing more text up front.

• Performance: When writing for humans, developers often prioritize clarity, optimizing only as needed and carefully balancing complexity with speed. AI agents will usually produce a correct solution with little optimization unless asked, focusing on completeness (e.g. including extensive error-handling) over raw performance. However, an AI’s vast knowledge can introduce performance enhancements (such as suggesting a more efficient algorithm or library) that complement human intuition. The trade-offs in an AI-managed codebase lean toward safer, clear code that can be iteratively optimized, versus a human expert sometimes writing a clever high-performance implementation from the outset.

The table below summarizes these differences:

Ultimately, good code is good code – many best practices overlap whether the consumer is human or AI. Clean architecture, readable style, and efficient execution benefit both. The key differences lie in emphasis: AI agents “read” code differently than humans, so code meant for them leans into consistency, explicit context, and simplicity of structure. As AI agents improve and become more context-aware, these distinctions may

, but for now, understanding them helps teams effectively blend human and AI strengths in software development.

Photo Tara Winstead