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They can contribute to code that becomes harder to understand and maintain, but the available evidence does not show that AI coding agents inevitably create a “big ball of mud.” That phrase is an architecture metaphor, not an outcome directly measured in the agent studies cited here. The evidence instead points to measurable warning signs—including growing complexity, structural anti-patterns, static-analysis warnings, and maintenance work—and to the importance of review and verification.
What makes a codebase a “big ball of mud”?
It is code that has become difficult to understand, change safely, and maintain as a system. A pull request can pass its tests and still make the architecture harder to work with: tests establish only the behavior they cover, not whether a change fits the design or makes future changes more difficult.
Because the metaphor is not a precise metric, studies use related indicators. These include architectural complexity, structural anti-patterns, cognitive complexity, static-analysis warnings, and the amount of work spent fixing bugs rather than adding features. Each indicator captures part of the problem; none alone proves that a codebase is “mud.”
What does the evidence say about agents and code quality?
Architecture and maintenance are connected, but this study did not test agents
Google Research’s 2025 large-scale study collected 7,200 developer survey responses and examined architectural complexity, maintenance burden, and developer sentiment. It 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. This is relevant to what “mud” can cost a team, but it is not evidence that coding agents caused those architectural conditions.
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A study of agent adoption reported rising quality risks in its sample
The 2026 study AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development reports roughly an 18% increase in static-analysis warnings and roughly a 35% increase in cognitive complexity in the open-source settings it studied. The authors describe a longitudinal causal study of agent adoption, but these figures remain specific to its repositories, period, measures, and comparison. They are not a forecast for every tool, team, or private codebase, and they do not establish that agents produce a “big ball of mud” in general.
Architecture-quality figures need their source and scope attached
Software Improvement Group’s State of Software 2026 report says that 50% of the code in its analyzed benchmark falls below its recommended architecture quality score. It also reports 30% lower issue-resolution time with stronger architecture. These are SIG’s reported findings, not universal rates or guaranteed savings for an individual team.
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Why can an agent make architecture harder to maintain?
Fast changes can outpace design review
An agent can produce or alter code quickly, but speed does not establish that the change belongs in the chosen component, reuses an existing abstraction, or avoids unnecessary duplication. The risk is not simply that generated code is wrong; it is that a series of locally plausible changes can accumulate without anyone assessing their combined effect on the system.
Feedback is necessary, but it is not a complete guarantee
UC Berkeley’s 2026 thesis, Scaling Environments and Verifiers for Software Engineering Agents, explains how agents can work with realistic execution environments and feedback from tools such as compilers, tests, type checkers, and profilers. It also finds that strong environments and verifiers do not close the gap on the hardest tasks. A green test run is evidence about tested behavior—not proof of architectural fit, completeness, or long-term maintainability.
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Agents need to know when not to change code
ETH Zürich’s SRI Lab study Coding Agents Don’t Know When to Act examines stale issue reports: a request may already be resolved or no longer call for a code change. The authors argue that agents should recognize such cases and abstain rather than act unnecessarily. This is a specific failure mode, not evidence that all agents routinely alter code in response to stale reports.
How can a team keep agent-written changes maintainable?
The following safeguards are practical synthesis, not a universal scoring rubric or a quoted standard. They are most useful when applied together: constrain what the agent may change, give it meaningful feedback, and have a maintainer assess the result in context.
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1. Match the task to the agent’s change scope
- Start with bounded, well-specified work whose expected behavior and likely files can be identified.
- For changes spanning components, ask for a plan or a proposed design before authorizing implementation; have a maintainer check where responsibilities belong.
- Use scoped permissions so a task does not grant broader access or authority than it needs.
2. Give the agent an environment that can test the intended behavior
- Use the project’s relevant build, test, type-checking, and static-analysis tools where available.
- Check that the tests exercise the requested behavior, not merely that an existing test suite passes.
- Have an independent verifier or reviewer inspect outputs that automated checks cannot establish, especially design fit and cross-component effects.
3. Review the change as software, not just as a completed task
- Inspect whether the change reuses the project’s existing patterns and keeps responsibilities in the right places.
- Look for unnecessary abstractions, duplicated logic, broad edits unrelated to the request, and behavior that lacks meaningful coverage.
- Ask whether the same design would still be understandable to the next person who has to change it.
4. Make “no change needed” an acceptable outcome
Before an agent edits code in response to an issue, verify that the report is still relevant and unresolved. A workflow that permits the agent to explain why it should abstain can avoid changes made only to satisfy a stale or already-resolved request.
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5. Watch quality over repeated changes
A successful task or pull request cannot show whether the workflow is making the repository healthier over time. Track relevant signals across repeated changes—for example, complexity, static-analysis warnings, structural problems, and maintenance activity—and investigate a persistent worsening trend. Treat these as indicators to review, not as a single score that settles maintainability.
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How should teams interpret the risk?
The evidence supports a careful answer, not a blanket prediction. One study relates architecture problems to maintenance burden without evaluating agents; another reports quality risks after agent adoption in studied open-source projects; research on environments and verifiers describes both their value and their limits. Together, these findings make architecture review and ongoing quality checks sensible. They do not establish a universal rate at which agents create technical debt, nor do they show that a particular agent workflow will produce the “big ball of mud” pattern.
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