Code review used to mean finding problems and asking humans to fix them. Flag the null pointer. Comment on the race condition. Point out the security gap. Then wait for someone to write the fix.

Coding agents changed that. They find the bug, write the fix, test it, and submit a PR within defined workflows and safeguards.

TLDR

Coding agents are autonomous AI systems that fix code issues with minimal human involvement. They take bug reports, analyze repositories, generate patches, run tests, and submit fixes. Performance on industry benchmarks jumped from 40% to over 80% in one year. Teams use them to handle routine fixes automatically while engineers focus on complex problems. cubic integrates coding agent capabilities to provide not just code review, but actionable fixes that developers can apply immediately.

What do coding agents do in automated code fixing?

Coding agents operate differently from traditional code review tools. Static analyzers flag issues. Coding agents go a step further by proposing concrete fixes. Here’s what they can do: 

1. They understand repository-level context

Traditional tools often analyze files in isolation. Coding agents analyze repository structure, dependencies, and relationships between modules. This context helps them generate fixes that account for how changes in one file affect others.

2. They handle multi-step fixes

Simple issues require simple changes. More complex issues may require coordinated updates across multiple files. Coding agents can plan and apply these multi-step fixes based on the surrounding code and existing patterns.

3. They validate fixes before suggesting them

After generating a fix, coding agents run tests, builds, or checks to verify the change works as intended. If validation fails, the agent can iterate on the fix or surface the issue for human review.

4. They learn from real-world code patterns

Coding agents are trained on large volumes of real code changes and issue–fix examples from public and enterprise-grade repositories. Public benchmarks such as SWE-bench Verified show top systems resolving a significant portion of real GitHub issues, reflecting learned patterns from how developers fix bugs in practice.

How teams actually use coding agents

Real-world usage shows how coding agents fit into existing development workflows.

1. Automated fix suggestions during code review

When a review process flags an issue, a coding agent generates a fix suggestion alongside an explanation. Developers review the change and apply it if appropriate, removing the back-and-forth of pointing out problems and waiting for updates.

cubic's AI code reviewer provides this directly in pull requests. When an issue is identified, developers see both the explanation and a ready-to-apply fix.

2. Batch fixes across repositories

Dependency upgrades, API changes, or pattern refactors often require the same update across many files. Coding agents can apply these transformations consistently across the codebase based on a single instruction.

3. Security vulnerability patching

When scanners identify known vulnerability patterns, coding agents can generate patches automatically. This shortens the time between detection and remediation, especially for common and well-understood issues.

4. Technical debt cleanup

Deprecated APIs, outdated patterns, and non-standard implementations accumulate over time. Coding agents can address these mechanical fixes systematically, allowing teams to reduce technical debt without dedicating manual effort.

For teams following the complete code review checklist, coding agents handle the mechanical fixes while humans focus on architectural decisions.

What this means for code review

Coding agents change what human reviewers spend time on.

1. Less time on routine fixes

Formatting issues, deprecated usage, and straightforward logic errors are handled automatically. Reviewers focus on architecture, business logic, and edge cases.

2. Faster feedback loops

Developers receive both problem identification and proposed fixes together, reducing context switching and review delays.

3. More consistent fixes

Because coding agents validate changes with tests and checks, fixes are less likely to introduce new issues compared to manual, rushed edits.

4. Consistent standards enforcement

Teams define quality standards once, and coding agents enforce them consistently across all code. From YAML configuration to production code, automated fixes maintain consistency that manual review struggles to achieve.

The technical foundation

Coding agents work through several integrated capabilities.

The combination of these capabilities enables automated fixes that actually work in production environments, not just theoretical solutions.

How cubic implements coding agents

cubic integrates coding agent capabilities into its code review workflow.

1. Repository-wide context

cubic analyzes entire repositories, not just modified files, allowing fixes that account for dependencies and shared logic.

2. Automated fix generation

When an issue is detected, cubic generates a working fix alongside a clear explanation, so developers can apply it immediately.

3. Alignment with team conventions

cubic adapts fixes to match existing code patterns and conventions rather than applying generic solutions.

4. Policy-driven enforcement

Teams define rules in natural language. When code violates a policy, cubic flags the issue and suggests compliant fixes.

Teams shipping AI-generated code to production use cubic to catch issues in AI-written code and provide automated fixes that maintain quality standards.

Practical limits of automated fixes

Coding agents handle many fixes automatically, but some problems still need human judgment.

1. Architecture decisions still require humans
   They can implement decisions, but do not choose architectural trade-offs.
   
   

2. Business correctness needs domain knowledge
   They can fix technical issues, but cannot determine whether a feature meets business intent.
   
   

3. Novel problems may need creative solutions
   Coding agents perform best on patterns similar to what they’ve seen before.

The effective pattern is human judgment for complex decisions, automated fixes for routine problems.

Evolution from traditional code review

Code review evolved significantly from waterfall processes to AI-native workflows. Coding agents represent the next phase.

Traditional review identified problems. Modern AI code review explains problems and provides context. Coding agents close the loop by fixing problems automatically.

This evolution doesn't eliminate human review. It changes what humans spend time reviewing. Less time on "fix this typo" comments. More time on "does this architecture scale" discussions.

Coding agents in modern automated code review

Coding agent capabilities continue to improve rapidly. Current systems can automatically fix a large percentage of routine, well-scoped problems.

That percentage will increase as training improves and tools learn from more examples.

For most teams, the focus has shifted to integrating coding agents into existing workflows without adding friction.

AI-code review tool, cubic provides coding agent capabilities within code review workflows that teams already use. The integration is natural because fixes appear where developers already look for feedback.

Ready to see automated fixes in your code review? Book a demo today and experience coding agent capabilities that find problems and fix them automatically.