Coding agent benchmarks for legacy codebase refactors

Legacy codebase refactoring is the deliberate restructuring of software so teams can change, test, and operate it more safely without losing critical business behavior. Coding agents can accelerate research, implementation, and verification, but they do not remove the need for architecture decisions, scoped requirements, and human review. The practical question is not whether an agent can generate a large patch. It is whether the resulting system is complete, understandable, and ready to support future product work. An agent-first control plane can help teams connect requirements, implementation tasks, test evidence, and approvals while the refactor moves forward.

At a Glance

A large refactor is worthwhile when technical debt is blocking delivery, reliability, and developer participation.

• Do not wait blindly: Coding agents improve quickly, but a more capable model does not repair unclear requirements or missing verification.
• Benchmark completion, not output: A fast patch that creates scaffolding while omitting critical behavior is not a successful refactor.
• Use high confidence thresholds: Long-running agent tasks need much stronger reliability than a 50 percent success rate.
• Refactor for the whole delivery loop: Repository structure, tests, deployment, observability, and review all affect maintainability.
• Measure the human outcome: Sustainable velocity includes whether more developers can confidently contribute to the codebase.
  

When is a legacy codebase refactor worth doing?

A refactor is worth doing when accumulated complexity makes customer work too slow, operational changes too risky, or developer contributions too difficult. The decision should compare business impact against the cost of delaying features, rather than treating technical debt as a purely aesthetic engineering concern.

Technical debt is often compared to financial debt because both create future obligations. That comparison is useful only if teams also consider the return received when the debt was taken on. Shipping an urgent feature, supporting a new customer, or testing a new product direction may justify short-term complexity.

The problem begins when the cost of carrying that complexity exceeds the value it created. Symptoms commonly include:

• Simple customer requests require changes across many services or repositories.
• Teams avoid certain components because changes feel unsafe or difficult to test.
• Pipeline performance, reliability, or operating costs cannot improve without broad rewrites.
• Only a few people understand critical paths and deployment behavior.
• Feature work slows because developers spend most of their time navigating incidental complexity.

In the source case, an ML pipeline for extremely large medical-claims documents had grown across more than ten repositories. The refactor focused on consolidating that environment into a monorepo and creating a cleaner foundation for new product work. The resulting value was not only faster commits. It included lower pipeline time and cost, support for larger files, and broader developer participation.

Why does AI-generated code make technical debt easier to create?

AI-generated code can reduce the effort required to make changes, but it can also let teams add complexity faster than they can understand or validate it. Without clear boundaries, generated code may produce a codebase that works locally while becoming difficult to maintain, review, and operate. This is exactly the pattern behind avoiding the common traps of agentic development — fast, unverified output that looks like progress until the missing pieces surface later.

Coding agents are increasingly capable of handling well-scoped implementation tasks. This changes the economics of code production, not the fundamentals of software ownership. A system still needs coherent domain boundaries, reliable tests, production controls, and people who can explain why the software behaves as it does.

The risk is subtle. A team may see an agent complete a ticket quickly and conclude the process is working. Yet the generated change may duplicate existing logic, introduce weak abstractions, skip an edge case, or silently leave a deployment path unfinished. Those defects become more expensive when they are repeated at high speed.

Research should receive the same skepticism. Agentic research workflows can gather documentation, compare tools, and draft evaluation matrices quickly. They should not be trusted simply because the output is long and polished. A reported feature may be experimental, absent from the installed version, or unsuitable for the actual operating environment.

The Claude Code best-practices documentation similarly emphasizes giving coding agents clear tasks, using planning, and validating output. Those practices matter most in a refactor, where an apparently plausible implementation can conceal an incomplete migration.

How should you benchmark coding agents for a refactor?

Benchmark coding agents with representative tasks and acceptance criteria that prove real behavior, not by timing a single prompt or counting lines changed. A useful benchmark measures completeness, correctness, required human intervention, test results, and the cost of recovering from mistakes.

Start with one bounded task from the intended refactor. It should contain enough complexity to expose realistic failure modes, such as workflow migration, model integration, configuration, or cross-service verification. Avoid a toy exercise that has no dependencies, no test surface, and no operational requirements.

Build an evaluation contract before prompting

Define the expected behavior before the agent starts. The contract should include:

• Functional requirements: What must work after the change.
• Nonfunctional requirements: Performance, cost, security, and reliability constraints where relevant.
• Repository constraints: Existing patterns, modules that cannot change, and compatibility needs.
• Verification steps: Unit tests, integration tests, build commands, and deployment checks.
• Completion boundaries: The exact artifacts required for the task to count as complete.

In one refactor experiment, an earlier model completed an implementation task more quickly than a human could, but still made ten major mistakes over several hours of back-and-forth work. Re-running the same task on newer models required much less manual correction. That is real progress, but it does not mean every large refactor is ready for one-shot execution.

For teams benchmarking beyond a single large refactor, see our broader framework for ongoing coding agent benchmarking in multiplayer team workflows.

Score the work after execution

Assess the result with a consistent rubric. A useful scorecard includes:

• Requirement coverage: Did the change meet every stated acceptance criterion?
• Behavioral correctness: Do the tests demonstrate the intended behavior?
• Integration completeness: Are configuration, bootstrapping, deployment, and runtime dependencies actually present?
• Review burden: How much human editing, rework, or explanation was required?
• Maintainability: Does the new structure reduce or add complexity?

This approach catches a common false positive: an agent claims success after creating a convincing structure, but key business logic or operational commands are still absent. A short completion time is not evidence of a complete system.

What success rate is safe for long-running coding agent tasks?

Long-running coding agent tasks need a high probability of success because failed runs consume compute, review time, and developer attention. For consequential refactor work, teams should think in terms of 80, 90, or 99 percent reliability, not whether an agent succeeds half the time.

A 50 percent success rate can be useful for research into model capabilities. It is a poor operating threshold for an engineer who delegates an hour-long task and needs usable results when returning to it. At coin-flip reliability, half of the runs can create more work than they save.

The METR time-horizons research offers a useful way to reason about this problem. As task duration rises, success rates tend to decline. Model progress can extend the duration of tasks agents handle, but it does not make long tasks uniformly dependable.

Use task duration as a planning input. If an agent can reliably complete a 15-minute change, split a four-hour refactor into independently verifiable stages. Each stage should have an explicit artifact, test result, or review checkpoint. This reduces the chance that an early flawed assumption corrupts the entire run.

Which parts of a refactor should coding agents handle first?

Coding agents are best used first on bounded, testable work with clear context, such as repository analysis, code transformation, test generation, and migration scaffolding. Architecture decisions, ambiguous domain rules, and production cutovers still need deliberate human ownership and validation.

A practical sequencing model separates the refactor into five layers:

1. Discovery: Map repositories, dependencies, workflows, deployment paths, and undocumented assumptions.
2. Design: Define the target architecture, migration boundaries, and acceptance criteria.
3. Implementation: Delegate scoped transformations, adapters, tests, and documentation updates.
4. Verification: Run deterministic checks, integration tests, and targeted agent-assisted review.
5. Cutover: Move traffic or workloads gradually, observe outcomes, and retain rollback paths.

Agents can help at every layer, but their role should change. During discovery, use them to summarize code and produce dependency inventories. During implementation, use them to execute narrow plans. During verification, use them to identify inconsistencies while deterministic tests and humans decide whether the result is acceptable.

Do not ask an agent to “refactor the whole codebase” without a definition of done. A broad instruction forces the model to infer architecture, priorities, and hidden requirements. It can produce a lot of code while missing the parts that make the system run.

Give coding agents work with a defined finish line.

Levr keeps issues, acceptance criteria, tests, execution evidence, and human gates connected, so agents can work from shared project context instead of an isolated prompt.

Explore an agent-first workflow

How can a monorepo improve an AI-native refactor?

A monorepo can simplify an AI-native refactor by bringing related code, tests, and shared interfaces into one navigable workspace. It does not solve architecture automatically, but it can make cross-cutting changes, end-to-end validation, and sandbox setup easier to manage.

Multiple repositories are not inherently wrong. Modern coding agents can navigate directory structures containing several repositories, particularly when a task is clearly scoped. The difficulty appears when a change must cross service boundaries and then prove that the whole system still works.

A unified repository can reduce friction in several areas:

• End-to-end testing: Dependencies are available in one workspace, making integrated test environments easier to assemble.
• Shared contracts: Schema, API, and library changes can land with their consumers.
• Agent context: A coding agent can inspect related modules without switching between disconnected environments.
• Developer onboarding: Engineers can locate workflows and make small cross-functional contributions more easily.
• Sandbox setup: Fewer clones and setup steps can reduce the time needed to start a full-system evaluation.

A monorepo is not required for every AI workflow. It is a strong option when delivery depends on coordinated changes and shared verification across previously fragmented components. The architecture should match the product and team, not an agent’s preference for a larger folder tree.

How do you keep an AI-assisted refactor from becoming a doom loop?

Prevent unproductive agent loops by replacing open-ended prompting with plans, explicit checkpoints, bounded retries, and observable verification. When an agent fails, diagnose the missing context or failed assumption rather than repeatedly sending a broader instruction with higher urgency.

Use this operating checklist before delegating a substantial change:

• Confirm the goal: State the behavior, constraints, and definition of done.
• Inspect the plan: Require the agent to identify affected modules, risks, and verification commands before editing.
• Limit the scope: Set a clear repository area, task boundary, and maximum iteration budget.
• Run independent checks: Do not let the same model’s confidence be the only proof of correctness.
• Capture failures: Convert failed assumptions into project documentation, tests, or future guardrails.
• Escalate intentionally: Move to human review when the agent encounters ambiguous business logic or system-level tradeoffs.

Plan mode became an important workflow improvement during the source refactor as coding tools matured. Planning does not guarantee correctness, but it exposes assumptions before code changes spread through the system. That is usually cheaper than discovering a wrong plan after a large patch has been generated.

How do you measure whether a refactor actually succeeded?

A successful refactor improves business delivery and engineering confidence, not just repository cleanliness or commit volume. Measure pipeline performance, cost, reliability, feature lead time, verification quality, and the number of developers who can safely contribute to the system.

Code volume and commit count can provide context, but neither proves value. A team can generate a large amount of code while creating an unmaintainable system. Focus instead on outcomes tied to the original reason for the refactor.

For a complex AI pipeline, metrics may include:

• Time required to process a customer workload.
• Infrastructure or model-serving cost per workload.
• Maximum file size or workload complexity the system can support.
• Time from approved feature request to production-ready change.
• Test pass trends and escaped defects after cutover.
• Number of engineers contributing outside their original subsystem.

The social signal matters. If developers who once avoided the system now volunteer to work in it, the refactor has likely improved more than code organization. It has improved the team’s ability to own the product.

How can Levr support an agent-assisted refactor workflow?

Levr can support an agent-assisted refactor by keeping intent, issues, acceptance criteria, test runs, pull requests, and approval gates in a shared control plane. This gives coding agents and humans a common record of what changed, how it was verified, and what remains blocked.

One way to structure a refactor in Levr is to create a project around the target architecture, then break it into independently verifiable stories. Each story should describe the relevant repository boundary, expected behavior, test evidence, and rollback consideration.

1. Define intent in natural language: Record the business reason, technical constraints, and acceptance criteria for each migration step.
2. Assign bounded work: Let coding agents pick up clearly scoped tasks with the required context, rather than a vague request to redesign everything.
3. Run automated verification: Connect tests and CI/CD evidence to the work item so completion has proof behind it.
4. Review and approve: Use human checkpoints for architecture, risk, and production-readiness decisions.

Levr’s agentic workflow supports this model across different autonomy levels. Its project, testing, and agent coordination features also help teams keep agent activity attributable while moving through a multi-stage migration.

Refactor Progress, Verified: The Levr Dashboard

A benchmark score means little if nobody can see it evolve across a multi-stage refactor. The Levr Dashboard aggregates staged tasks, test results, and review gates into a single pane of glass, giving teams the same completion evidence this article argues every refactor needs.

The Levr Dashboard tracks staged refactor progress and verification results in real time, across every task.

Technical Deep Dive FAQ

What is a legacy codebase?

A legacy codebase is software that remains important to the business but has become difficult to change safely. Age alone does not make code legacy. The term usually applies when outdated dependencies, unclear ownership, weak test coverage, fragmented architecture, or undocumented behavior make feature delivery and operations increasingly expensive. A recently generated codebase can become legacy quickly if a team cannot understand, validate, or maintain it.

What is an AI-assisted refactor?

An AI-assisted refactor is a software restructuring effort where coding agents help analyze repositories, plan changes, write code, generate tests, review patches, or document system behavior. The human team remains responsible for the target architecture, requirements, verification standard, and deployment decision. The goal is not to delegate judgment blindly. It is to reduce repetitive implementation effort while keeping the system understandable and proven.

Can a coding agent refactor an entire codebase in one prompt?

A coding agent may generate a large patch from one broad prompt, but that result should not be assumed complete. Large refactors include hidden dependencies, configuration, deployment workflows, data contracts, and behavior that may not be visible in a prompt. Split the work into scoped stages with explicit acceptance criteria. Require tests and inspect whether required operational pieces, not just scaffolding, were implemented before accepting the change.

Should you wait for coding agents to improve before refactoring?

Do not defer a needed refactor solely because models will improve. Waiting may reduce implementation effort later, but existing technical debt continues to slow feature work, increase costs, and concentrate knowledge in a small group. Compare the current business cost of delay against the likely benefits of waiting. If the architecture blocks customer demand or reliability now, a staged refactor can be the better decision.

How do you verify code written by a coding agent?

Verify agent-written code with the same rigor applied to human changes, then add checks for incomplete reasoning and hidden assumptions. Use unit tests, integration tests, end-to-end workflows, static analysis, build commands, and deployment validation where applicable. Review the plan and diff for unexpected scope. Most importantly, test the actual acceptance criteria. An agent’s report that the task is complete is not evidence that production behavior is complete. Verification here means the same thing it means anywhere else in agentic development: pairing model output with deterministic verification rather than trusting the agent's own confidence.

What is the best task size for a coding agent?

The best task size is the largest unit that has clear inputs, a testable output, and a reliable success rate for the chosen model and harness. For a new team, begin with small tasks such as a module migration, adapter implementation, or focused test suite. Expand scope only after measuring correction effort and success. If a task runs for a long time without intermediate verification, break it into smaller milestones.

Does a monorepo make coding agents more effective?

A monorepo can make coding agents more effective when a task spans shared libraries, services, schemas, and tests that need to be inspected together. It reduces environment switching and can simplify end-to-end validation. However, a monorepo does not replace module boundaries, documentation, or good task definitions. Agents can work across multiple repositories, but setup and full-system verification may require more coordination.

What should a refactor acceptance criterion include?

A refactor acceptance criterion should state the observable behavior that must remain or improve after the change. Include interfaces that must stay compatible, performance or cost expectations, required test cases, deployment requirements, and rollback conditions. For agent-executed work, also name the files or modules in scope and commands that demonstrate success. Avoid vague criteria such as “clean up the code” because they leave the model and reviewer to infer the outcome.

How do engineering managers measure coding agent productivity?

Engineering managers should measure outcomes rather than raw generated lines or agent run counts. Track feature lead time, review burden, test health, defect trends, blocked work, operational reliability, and the percentage of agent work that passes verification without significant rework. Attribution also matters. Teams need to know which human or coding agent performed an action, what context it used, and which checks supported approval.

Why are human pull request reviews still important during a refactor?

Human pull request reviews are valuable during a refactor because they distribute system context across the team. Reviewers can detect domain assumptions, architecture drift, and operational concerns that tests do not express. Early in a migration, review also prevents knowledge from staying with the small group initiating the work. Coding agents can assist with local checks, but human review remains a useful mechanism for collective ownership and accountability.

What is the biggest mistake when adopting coding agents for legacy systems?

The biggest mistake is treating generated code as the product outcome instead of treating verified behavior as the outcome. This leads teams to reward speed while overlooking missing deployment paths, untested integrations, or code that no one can maintain. Establish requirements, planning, test gates, and review before expanding autonomy. The best agent workflow makes the next change easier to understand, not merely faster to generate.

Key Takeaways

A refactor should make future delivery safer and faster, even as coding agents change the cost of implementation.

Use agents as part of a controlled engineering system, not as a substitute for one.

• Business case: Refactor when technical debt is measurably limiting throughput, reliability, costs, or developer participation.
• Benchmark design: Score complete, verified behavior instead of measuring prompt time, code volume, or model confidence.
• Reliability: Long-running agent tasks require high confidence, staged execution, and independent verification.
• Architecture: A monorepo can improve shared context and integrated testing, but it is not a substitute for good boundaries.
• Ownership: The strongest result is a codebase more engineers can understand, test, and improve.

Models will keep improving. Clear requirements, quality gates, and shared context remain the durable advantage.

Further reading

• METR research on AI agent time horizons
• Claude Code best practices
• Levr agent-first project control plane
• Levr features for agents, issues, tests, and CI/CD
• How Levr’s agentic workflow operates
• AI security and the agent-ready web
  
  

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