Keeping AI-Generated Software Understandable

AI can turn a short request into working code very quickly. That does not mean the resulting system is simple, understandable, or safe to operate.

For a beginner, the distinction is:

working code produces the expected result in the cases that were tried
understandable software has responsibilities, dependencies, decisions, and failure behavior that people can explain
maintainable software can be changed without repeatedly breaking unrelated behavior

Agent-generated code is not automatically worse than human-written code. The risk comes from speed and scale. An agent can produce more changes than a person can meaningfully inspect, and a sequence of individually reasonable changes can create an unreasonable architecture.

The goal is therefore not to avoid AI-generated code. It is to use AI for implementation while preserving human understanding of the system.

The Bottleneck Has Moved

Software development has never been only about translating an idea into programming-language syntax.

The phrase software crisis emerged around the 1968 NATO Software Engineering Conference, when software projects were becoming harder to deliver and control as computers and ambitions grew. In his 1972 Turing Award lecture, Edsger Dijkstra argued that increasingly powerful machines had created a new difficulty: programmers now had to master systems whose complexity could exceed their intellectual control.

The historical analogy is useful, but it is not a prediction that every AI-assisted project will fail. Coding agents create another large increase in production capacity. If a team can generate software faster than it can understand and simplify it, complexity can once again outrun the practices used to manage it.

In No Silver Bullet, Fred Brooks separated software difficulty into two broad categories:

essential difficulty comes from understanding and representing the problem itself
accidental difficulty comes from the tools and mechanisms used to implement that solution

Better languages, libraries, infrastructure, and coding agents can remove substantial accidental work. They do not automatically decide:

what the system should do
which behavior is truly required
where responsibilities should live
which tradeoffs are acceptable
how the system should fail
what must remain compatible

AI makes implementation easier, but the conceptual work remains. In some projects, implementation becomes fast enough that understanding, review, and architecture become the limiting resources.

flowchart LR
    I["Intent and domain knowledge"] --> D["Design decisions"]
    D --> C["Code generation"]
    C --> V["Verification"]
    V --> O["Operable software"]
    A["Coding agent"] -. "Accelerates" .-> C
    H["Human owner"] -. "Applies judgment" .-> D
    E["Tests and runtime evidence"] -. "Ground" .-> V

Easy Is Not The Same As Simple

Rich Hickey’s Simple Made Easy distinguishes two ideas that are often treated as synonyms.

Idea	Practical meaning	Example
Easy	Close at hand, familiar, or requiring little immediate effort	Ask an agent to add one more condition to the existing function
Simple	Not interleaved or entangled with unrelated concerns	Keep authorization, business rules, persistence, and presentation behind clear boundaries

An easy action can make a system less simple.

Suppose an application has one authentication file. A sequence of prompts asks an agent to:

add password login
add OAuth
add session refresh
add enterprise SSO
repair a conflict between the previous changes

Each response may be locally reasonable. If every request modifies the nearest working path, however, authentication, session storage, user provisioning, and authorization can become intertwined.

flowchart TD
    A["Prompt 1<br/>Add authentication"] --> B["Small local solution"]
    B --> C["Prompt 2<br/>Also add OAuth"]
    C --> D["Extend the existing path"]
    D --> E["Prompt 3<br/>Sessions now conflict"]
    E --> F["Add another exception"]
    F --> G["More coupling and hidden assumptions"]
    G --> H["System works, but its structure<br/>is difficult to explain"]

This is a conversation spiral: the architecture begins to reflect the order and wording of the conversation instead of a deliberate model of the problem.

Essential And Accidental Complexity

The distinction is useful when an agent reads an existing codebase.

Complexity	What it represents	Example
Essential	Rules that exist because of the real problem	Only an account owner may approve a transfer
Accidental	Difficulty created by implementation history	The same ownership check is copied into controllers, jobs, and database helpers

An agent sees code, tests, documentation, history, and tool output. It does not automatically know which patterns express current intent and which are old workarounds.

For example, repeated permission checks may mean:

every layer genuinely needs an independent security boundary
the application lacks a central authorization service
a previous migration stopped halfway
an old compatibility rule is still required for one customer
obsolete code was copied because it appeared nearby

Similarity alone cannot resolve that question. The agent needs evidence from domain owners, architecture decisions, production behavior, history, and tests.

This is why “follow existing patterns” is necessary but insufficient. Existing patterns are evidence, not proof that the design should be repeated.

Why Passing Tests Does Not Guarantee Understanding

Tests are essential, but they answer bounded questions.

A passing suite can show that:

specified examples still work
known invariants were preserved
interfaces remain compatible
a regression was not reproduced

It may not show that:

responsibilities are in the correct component
two concepts have become unnecessarily coupled
a copied workaround is now spreading
an undocumented behavior should have existed at all
the design will remain comprehensible after the next five changes

Verification and architectural review therefore solve different problems. Use both.

Signs That Understanding Is Falling Behind

Slow down when several of these appear:

reviewers can describe the diff but not the resulting execution path
the agent repeatedly edits the same central file for unrelated features
each new request adds another exception to the previous solution
generated abstractions duplicate an existing responsibility
a change passes tests but nobody can explain why the affected boundaries are correct
the plan names files without explaining their roles
the agent cannot distinguish required behavior from compatibility scaffolding
generated documentation describes an architecture that the code does not actually implement
rollback depends on reconstructing decisions from a long chat transcript

The signal is not merely a large diff. A mechanical large diff can be easy to understand. A small change that mixes two previously separate concepts can be much riskier.

Keep A Human-Owned Model Of The System

Before substantial implementation, a responsible person should be able to explain:

Outcome: What problem are we solving?
Essential behavior: Which domain rules must remain true?
Boundaries: Which component owns each decision and side effect?
Dependencies: What calls, stores, or trusts what?
Failure behavior: What happens when a dependency is slow, unavailable, or inconsistent?
Compatibility: Which current consumers and data must continue to work?
Evidence: What will prove that the change is correct?
Recovery: How can the change be rolled back or repaired?

The person does not need to memorize every line. They need a coherent model that lets them notice when generated code contradicts the intended design.

Current evidence supports this division of work. Anthropic’s June 2026 analysis of approximately 400,000 Claude Code sessions found that people made most planning decisions while the agent made most execution decisions. It also found that task-specific domain expertise was associated with higher success and better recovery from problems. The study is observational and uses model-based classification, so it should not be treated as universal proof, but it reinforces a practical rule: agents amplify the quality of the understanding supplied to them.

Use Research, Planning, And Implementation As Separate Phases

For consequential work, do not begin with an editing prompt.

flowchart TD
    G["Goal and constraints"] --> R["Research<br/>map current behavior and dependencies"]
    R --> RH{"Human checkpoint<br/>Is the system map accurate?"}
    RH -->|"Revise"| R
    RH -->|"Approve"| P["Planning<br/>design the change in detail"]
    P --> PH{"Human checkpoint<br/>Are boundaries and tradeoffs sound?"}
    PH -->|"Revise"| P
    PH -->|"Approve"| I["Implementation<br/>execute bounded steps"]
    I --> V["Verification and review"]
    V --> U{"Can a responsible person<br/>explain the resulting system?"}
    U -->|"No"| R
    U -->|"Yes"| M["Merge or release"]

1. Research The Current System

The research phase should reconstruct reality before proposing changes.

Useful evidence includes:

relevant entry points and execution paths
interfaces, schemas, and type definitions
architecture and design documents
tests and production runbooks
recent incidents and known failure modes
git history for confusing decisions
migration notes and compatibility requirements
questions that require domain or architectural judgment

The output should be a bounded research document, not a dump of every file the agent read. It should separate:

confirmed facts
reasonable inferences
unresolved questions
suspected accidental complexity

This is context compression: turn a large, noisy codebase into a small, reviewable model of the part that matters.

2. Plan The Change

Once the current system is understood, define:

the target behavior
component and ownership boundaries
exact interfaces that change
invariants that must be preserved
files and symbols likely to be modified
rollout and compatibility stages
tests, operational checks, and rollback
explicit non-goals

The plan should be precise enough that implementation is mostly execution rather than continuing architectural discovery.

Use Spec-Driven Development with Coding Agents when requirements, design, tasks, and evidence need durable traceability. Use Plan Before You Code for a lighter checkpoint.

3. Implement In Bounded Steps

Give the agent one reviewable step at a time or a dependency-aware set of independent steps.

For each step, specify:

allowed scope
required behavior
constraints and non-goals
verification commands
conditions that require stopping

If implementation reveals that the research or design is wrong, return to the earlier artifact. Do not hide the discovery under another local patch.

Create One Reference Change Before Automating A Migration

For a repetitive refactor or migration, manually completing one representative change can be more valuable than immediately dispatching many agents.

The reference change can reveal:

undocumented invariants
misleading abstractions
special compatibility cases
the actual review standard
commands and tests that prove correctness
differences between the intended architecture and the current code

The accepted pull request, its review discussion, and its verification evidence then become a concrete example for later agents.

This does not mean every migration must begin manually. Use a reference change when hidden knowledge or architectural judgment is more likely to dominate than mechanical editing. The complete procedure is covered in Parallel Coding Agents for Large Refactors.

Ask For A Teach-Back Before Acceptance

Do not ask only, “What files did you change?”

Ask the agent to explain:

Describe the implemented execution path from input to side effect and response.

For each affected component, state:
- its responsibility
- why the change belongs there
- the invariants it preserves
- dependencies it calls
- expected failure behavior
- evidence that supports these claims

Identify any behavior copied from the existing code that may be compatibility
scaffolding or technical debt rather than intentional design.

Then compare the explanation with the code and runtime evidence.

A fluent explanation is not proof. Its value is that discrepancies become visible. If the explanation names a boundary that the code violates, or claims an invariant without a supporting test or source, the review has found useful work.

Scale The Process To The Risk

Not every change requires a research report and architecture review.

Change	Appropriate process
Typo or isolated documentation correction	Direct edit and rendered check
Small behavior change in a familiar component	Short plan, focused tests, normal review
Cross-component feature	System map, reviewed plan, integration tests, teach-back
Security, billing, identity, or data change	Explicit invariants, threat and failure analysis, human approval, rollback evidence
Repository-wide migration	Reference change, dependency map, staged execution, layered verification

The cost of the process should follow the cost of being wrong and the difficulty of recovering understanding later.

A Practical Responsibility Split

Responsibilities vary by team and risk, but this is a useful starting point.

Work	Human responsibility	Agent contribution
Problem definition	Own outcome, priorities, and domain truth	Clarify ambiguity and summarize evidence
Research	Validate important facts and answer judgment questions	Search, trace paths, and produce a system map
Architecture	Own boundaries, tradeoffs, and risk acceptance	Compare options and test assumptions
Implementation	Set scope and stopping conditions	Produce code and mechanical transformations
Verification	Decide what evidence is sufficient	Run checks and collect reproducible results
Review	Accept accountability for the resulting system	Explain the change and perform independent checks

Avoid turning this table into a rigid rule. Agents can propose architecture and people can implement code. The important point is that consequential decisions have an identifiable owner who understands their effects.

Common Failure Modes

The research document is plausible but imaginary

Require file paths, symbols, test names, command output, or approved documentation for important claims.

The plan preserves accidental complexity

Ask which existing behaviors are essential requirements and which are only implementation history. Escalate uncertain cases to domain owners.

The human checkpoint becomes ceremonial

An approval button is not review. Require the reviewer to resolve open questions and confirm boundaries, invariants, and verification.

The agent rewrites the plan while implementing

Allow discoveries, but make them visible. Pause, update the reviewed artifact, and obtain approval when scope or architecture changes materially.

The reference change is not representative

Choose a slice that exercises real interfaces and failure paths. If later batches differ materially, create another reference class instead of forcing one pattern everywhere.

Understanding exists only in one person’s head

Record the accepted system map, decisions, and operational evidence in repository documentation, design records, tests, or runbooks.

Checklist

Before substantial generation:

The outcome and non-goals are explicit.
Essential behavior is separated from suspected implementation debt.
Current execution paths and dependencies are mapped.
Important claims point to evidence.
Human judgment questions are resolved or recorded.
The proposed boundaries and tradeoffs were reviewed.
Verification and recovery are defined.

Before accepting the result:

The complete diff matches the approved scope.
A reviewer can trace the changed execution paths.
The code follows the intended component boundaries.
Tests cover required behavior and important invariants.
Known accidental complexity was not copied without justification.
Operational failure and rollback behavior remain understood.
The resulting system can be explained without reconstructing the chat.

Key Takeaways

Faster code generation moves pressure toward understanding, design, and review.
Easy changes can produce a structurally complicated system.
Existing code contains both intentional design and historical accidents.
Tests verify bounded behavior; they do not replace architectural understanding.
Use research and planning artifacts to compress evidence before implementation.
Preserve human ownership of outcomes, boundaries, tradeoffs, and risk.
For uncertain migrations, solve one representative case before scaling execution.
Accept generated code only when the resulting system remains explainable and operable.