Project understanding

We need to discuss a basic question of project understanding and manageability. This may sound obvious, but it becomes easy to forget when working with LLM-based coding agents.

They often behave like reliable, intelligent collaborators: they explain code, propose designs, write tests, and produce confident implementation plans.

But this fluency is not the same as grounded understanding. Coding agents can hallucinate facts, infer requirements that were never stated, invent architectural intentions, or generate locally plausible code that violates the deeper constraints of the system. Coding agents may construct a useful local model from the context they are given, but that model is fragile, incomplete, and not accountable. It does not reliably include the project’s history, production constraints, implicit product decisions, operational risks, or future maintenance burden. The engineer remains responsible for integrating those dimensions into every accepted change.

Therefore the engineer must remain the holder of the project model. If the engineer loses that model, there may be nobody left who can reliably judge whether the agent is improving the system or merely adding plausible damage.

Project ownership (or the responsibility for project understanding) cannot be delegated to the agent.

Ownership#

The engineer must maintain a working mental model of the system. Coding agents can generate, modify, and explain code quickly, but they do not remove the need for human comprehension. In fact, they increase the need for it, because implementation velocity can exceed the engineer’s ability to evaluate whether a change is coherent.

A dangerous pattern is: “I don’t fully understand this part yet, but the agent says it works.” The dangerous word is yet. Temporary non-understanding is normal during investigation, but it becomes fatal when you accept and build on changes before closing the understanding gap. At that point you have created comprehension debt. Like technical debt, it may not break the system immediately, but it compounds.

This can be acceptable for throwaway scripts, experiments, migration helpers, or isolated developer tools, where the contract is small and the cost of failure is limited. It is not acceptable for core product code. Once the engineer loses the ability to judge the design, the agent can keep adding plausible abstractions, edge cases, classes, state machines, and rules until the system becomes incomprehensible.

Never let implementation velocity exceed comprehension velocity for core product code.

The Law of Inevitable Comprehension Collapse#

If the system grows faster than the engineer’s understanding of it, and new changes continue to build on top of that gap, collapse is only a delayed consequence. At first, the gap is easy to ignore: the code works, the tests pass, the agent can explain its own changes, and the product appears to move forward.

But once you stop fully understanding the project, you also stop being able to evaluate whether the agent’s next proposal is necessary, coherent, or harmful. Then one day the agent suggests another abstraction, another class, another state machine, or another function with thirty rules, and you no longer have the mental model needed to reject it. You accept complexity you cannot judge, on top of complexity you already did not understand.

The crash happens later, but the failure begins earlier: at the moment comprehension stops being a requirement for accepting changes. In core product code, “it works for now” is not enough. If you cannot explain it, you cannot safely evolve it.

Afterword: This is not only an agent problem#

None of this means human teams automatically maintain perfect project understanding.

They do not.

Project understanding is often distributed across code, tests, documentation, tickets, incident history, production feedback, and the memories of senior engineers. Teams can lose that understanding too. The bus factor, abandoned modules, weak ownership boundaries, outdated documentation, and over-specialized knowledge all create similar risks.

So why single out coding agents?

Because agents can accelerate the existing failure mode.

A team may already have weak ownership, incomplete documentation, fragile tests, or unclear architectural boundaries. A coding agent can continue building on top of that weakness at high speed, producing changes that look coherent locally while the human project model becomes thinner and thinner.

The issue is not that humans always understand and agents never do. The issue is accountability. Human ownership creates a place where judgment, responsibility, memory, and consequences are supposed to accumulate. When that ownership is weak, the team has an organizational problem. When a coding agent fills that gap, the problem can become invisible for longer because the agent remains fluent.

This article focuses on the engineer-agent boundary, but the team-level version matters too.

Tooling#

We need organizational, architectural, testing, and workflow practices that keep understanding, correctness, and maintainability moving at the same speed.