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What does “big ball of mud” mean for an AI-assisted codebase?
Here, a “big ball of mud” is a metaphor for software that has become hard to understand, change, and maintain. It is not a precise metric used by the studies discussed below. Researchers instead measure related signals, such as architectural complexity, structural anti-patterns, static-analysis warnings, cognitive complexity, and maintenance activity.
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Those signals can indicate that a codebase is becoming more difficult to work in, but they are not interchangeable, and none alone proves that a system has become a big ball of mud. Nor does code written by an agent become harmful simply because it was generated: its effects depend on the task, the codebase, the feedback available to the agent, and the way changes are reviewed and maintained.
What does the evidence say about agents and code quality?
The findings point to a real concern, not a universal outcome. One study examines architecture and maintenance without evaluating coding agents; another studies agent adoption in open-source repositories. Their results are informative in different ways and should not be collapsed into a single causal claim.
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| Source and scope | Reported finding | What it establishes—and what it does not |
|---|---|---|
| Google Research, 2025: a large-scale study of architectural complexity, maintenance burden, and developer sentiment | Based on 7,200 survey responses, the study found that higher architectural propagation cost and more structural anti-patterns were associated with more lines of code devoted to bug fixing rather than feature addition. | It connects architectural problems with maintenance activity, but it does not evaluate coding agents or show that agents caused the reported patterns. |
| AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development, 2026: a longitudinal study of autonomous-agent adoption in open-source repositories | The authors report roughly 18% more static-analysis warnings and roughly 35% more cognitive complexity in the study’s agent-adoption settings. | These are study-specific findings tied to its sample, period, measures, and comparison—not guaranteed effects for every tool, team, or private repository. The paper uses a longitudinal causal design, but its results do not establish a universal outcome. |
| Software Improvement Group (SIG), State of Software 2026 | SIG reports that 50% of code in its analyzed benchmark falls below its recommended architecture quality score, and that stronger architecture cuts issue-resolution time by 30%. | These are SIG’s own reported findings; they should not be generalized to all codebases. SIG also reports that roughly 1.5% of enterprise systems in production qualify as AI systems and that 72% of those score below its recommended maintainability rating. That is context about deployed AI systems, not direct evidence about code written by coding agents. |
Other work helps explain what agents can and cannot do reliably. UC Berkeley EECS’s 2026 thesis, Scaling Environments and Verifiers for Software Engineering Agents, describes how compilers, tests, type checkers, and profilers can provide actionable feedback, while cautioning that even strong environments and verifiers do not close the gap on the hardest tasks. ETH Zürich SRI Lab’s 2026 study, Coding Agents Don’t Know When to Act, highlights a different risk: acting on stale issue reports instead of recognizing that an issue is resolved or that no code change is needed.
Long-term effects remain an open question. The 2026 paper AI Writes Code, Humans Pay the Debt is described as an ongoing study of quality, issue localization, and debt evolution; the available information does not establish a universal long-horizon rate at which agent use creates architectural decay.
How can teams keep agent-written code maintainable?
The safeguards below are practical synthesis, not a universal scoring rubric or a quoted standard. They follow from the measured quality risks, the importance of useful feedback, and the documented stale-issue failure mode.
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Give an agent a specific outcome, the relevant files or component boundaries, and explicit constraints. A small, well-specified change is easier to inspect than a broad request to “clean up” or redesign a system. For work that spans components, break the request into reviewable stages and ask a maintainer to check the intended design before implementation spreads.
Provide realistic feedback, then verify independently
Run changes in an environment that resembles the project’s actual build and execution conditions, using relevant tests and tools such as type checkers or static analysis. A passing test establishes only that the change passed the tests that were run; untested behavior and difficult failures can still escape. Have reviewers examine behavior and design fit as well as test results.
Allow the agent to stop when action is unnecessary
Confirm that an issue is still open and describes a current problem before authorizing code changes. An agent should be able to report that a request is already satisfied, stale, or too ambiguous to act on. Avoiding an unnecessary patch can prevent avoidable churn.
Limit permissions to the work at hand
Use scoped access and staged delegation so the agent can change only what the task requires. Keep consequential actions—such as merging, deploying, or broad architectural changes—with an accountable human review process. These are risk controls, not a guarantee that any individual change is correct.
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What should maintainers check before merging?
A merge review should ask whether the change belongs in the architecture, not just whether it compiles. Use the checks that fit the project and the change:
- Purpose: Does the patch solve a current, clearly stated problem, or has the issue already been resolved?
- Scope: Is the change limited to what the task needs? Are cross-component edits explained and consistent with existing boundaries?
- Behavior: Do tests cover the relevant behavior and failure cases? What remains untested?
- Maintainability: Does the code add unnecessary complexity, duplication, dependencies, or static-analysis warnings?
- Reviewability: Can a maintainer explain the design and safely modify the result later?
- Permissions and impact: Did the agent change only the intended files and avoid consequential actions that were not part of the task?
Passing this checklist is a review aid, not a formal guarantee. Generated line counts, task completion, or one successful pull request are not evidence by themselves that a change will remain maintainable.
How can a team tell whether quality is changing over time?
Evaluate the trend across repeated changes rather than judging the workflow by a single successful patch. Track a small set of measures consistently, such as static-analysis warnings, complexity, structural anti-patterns, bug-fixing activity, and the time needed to resolve issues. Interpret them in context: a warning count may rise because the codebase grew, while more bug-fix work may have causes unrelated to agent use.
Compare like with like, document the period and repositories being assessed, and inspect examples behind any worsening measure. A quality trend is more useful when it can be connected to actual maintenance work than when it is treated as a single score. Luc Brandts, CEO of SIG, wrote in the foreword to State of Software 2026: “You cannot manage what you cannot measure, and you cannot move fast for long on a foundation you do not understand.”
Which agent workflow should a team choose?
There is no universal autonomy level or scoring formula established by these sources. A team can compare workflows by how they handle the factors most likely to affect code quality:
- Change scope: Is the task bounded and well specified, or does it invite broad cross-component edits?
- Feedback quality: Can the agent run the relevant code and receive useful feedback, and is there independent verification?
- Human review: Does a maintainer assess design fit and behavior beyond whether tests pass?
- Abstention: Can the agent recognize a stale issue, an already-satisfied request, or uncertainty that calls for a human?
- Longitudinal quality: Does the team monitor complexity, warnings, and maintenance burden across repeated changes?
A workflow with narrow tasks, strong feedback, meaningful review, and permission to abstain is easier to govern than one that grants broad authority without comparable checks. The right balance depends on the repository and the consequences of a mistake.
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