How to Understand Code Written by AI Agents

TL;DR

Understanding agent-written code keeps humans capable of contributing ideas, not merely checking correctness. Use personalized explainer documents that establish background, build intuition before presenting details, include purposeful interactive figures, and arrange code changes as a guided narrative. Pair explanations with quizzes to expose false confidence and prevent cognitive debt from gradually removing you from the creative process.

Transcript

[music] What's up? Yeah, thank you for coming to the design engineering track at AI. Is everyone having fun? Yeah, I think this is going to be a great track, so get excited. All right, let's get going. Um, my name is Jeffrey Lit. I'm a design engineer at Notion currently and I'm here to drop a hot take for this room. Maybe I think it is still impor... Read More

Key Insights

• Understanding is necessary for creative participation, not only correctness checking. A human who understands the system can recombine concepts, make informed creative leaps, and generate the next idea, while someone several layers removed from the implementation has fewer meaningful ways to guide the project.
• Correctness checking is becoming less dependent on humans because agents can increasingly evaluate whether work matches a specification, avoids production failures, and follows an acceptable architecture. Better verification loops therefore reduce one human role without eliminating the separate need to understand what was built.
• Cognitive debt is the accumulated loss of understanding that develops when people accept rapid agent-generated changes without keeping up conceptually. It may remain hidden while development feels productive, but eventually the human can no longer participate effectively because the system's behavior and structure have become unfamiliar.
• Education provides practical models for understanding agent-written code. Instead of relying exclusively on line-by-line review, developers can apply teaching principles, personalized explanations, interactive experiences, and knowledge checks to absorb changes more efficiently while continuing to benefit from the speed of coding agents.
• An effective code explanation begins with relevant system background rather than immediately describing the latest change. This foundation can be personalized or skipped when already understood, allowing the material to meet the reader at an appropriate level before introducing implementation-specific details.
• Intuition should precede implementation details in an explainer document. A concise description of the change's essential goal, supported by concrete examples, gives the reader a mental model that makes subsequent code easier to interpret than an unexplained sequence of modified files.
• Interactive figures can reveal behavior that static prose cannot communicate as directly. In the Zen garden example, moving rocks through a small simulation exposes coordinates and painting-layer changes, although interactivity must be used purposefully because unnecessary interaction can become a distraction or crutch.
• Quizzes expose the gap between reading and genuine comprehension. A developer can finish an explanation believing the material was understood, yet fail to answer a basic question afterward. Active recall questions make this misunderstanding visible and can reinforce memory beyond the initial review.

Questions & Answers

Q: Why should developers understand code written by AI agents?

Developers should understand agent-written code because understanding enables them to participate creatively in future work. Correctness review often produces a simple approval or rejection, but comprehension changes what a person knows. That knowledge carries into later development loops, supports informed decisions, and allows concepts to be recombined quickly when generating the next idea or directing an agent.

Q: What is cognitive debt in AI-assisted software development?

Cognitive debt is the gradual loss of a developer's understanding as agent-generated changes accumulate faster than the developer can absorb them. Like technical debt, it may appear manageable for a while. The cost becomes clear when the developer realizes that the system is too unfamiliar to guide, modify, or discuss meaningfully, even if earlier coding sessions felt productive.

Q: Why is correctness review not enough for agent-generated code?

Correctness review is not enough because it primarily asks whether an output matches a specification, avoids production problems, or uses an acceptable architecture. Those decisions can often be reduced to approval or rejection, and agents are improving at making them. Understanding serves a different purpose: it equips humans with the conceptual structures needed to develop new ideas and participate actively in later work.

Q: How should an explainer document for a code change begin?

An explainer document should begin with background about how the relevant system works, rather than immediately listing modifications. The background gives the reader a foundation for interpreting the change and can be personalized according to existing knowledge. Readers who already understand the system can skip it, while others receive the context required before encountering the implementation details.

Q: Why should intuition come before code details?

Intuition should come first because a clear mental model makes technical details easier to organize and interpret. The explainer can state the essential goal of a change and provide examples before presenting code. In the Zen garden example, the central idea was creating a three-dimensional feeling through two-dimensional drawing techniques, which prepared the reader to understand the later implementation.

Q: How can interactive figures help explain software changes?

Interactive figures can let developers manipulate a simplified representation of the behavior affected by a code change. For the Zen garden perspective change, a small simulation allowed rocks to be dragged while displaying coordinates and painting-layer behavior. This offered an intuitive view of the system that would be difficult to reproduce through prose alone, although interaction should be used selectively.

Q: What are literate code diffs?

Literate code diffs present modified code as a guided explanation rather than as a raw list of files. Prose introduces each relevant section, explains what is happening, and orders the material according to the conceptual story of the change. This structure makes a large update easier to follow and can turn review into something resembling a personalized textbook chapter about the project.

Q: How do quizzes improve understanding of agent-written code?

Quizzes test whether a developer can actively recall and use what an explanation presented. Passive reading can create false confidence, as shown when the speaker believed he understood a pull request but could not answer a basic question from a coworker. Knowledge checks expose that gap immediately and can reinforce the material after the initial explanation has been read.

Summary & Key Takeaways

• AI agents can produce enormous code changes faster than humans can review them line by line. Although agents are improving at correctness checks, humans still need understanding to participate creatively. Knowledge gained from one development loop becomes the conceptual foundation for generating ideas, making decisions, and guiding the work in later loops.

• A lack of understanding creates cognitive debt. A developer may move quickly while agent-generated code appears to work, then discover that the system has become too unfamiliar to guide effectively. The proposed response is not abandoning agents, but applying proven ideas from education and cognitive science to build understanding more efficiently.

• A useful explainer document begins with system background, states the change's central intuition, offers examples or purposeful interactive figures, and presents code in a logical narrative rather than file order. Quizzes add active verification of comprehension, helping developers detect when passive reading has created confidence without genuine understanding or durable memory.