Are Bugs and Incidents Inevitable with AI Coding Agents?

The promise of AI coding agents is undeniable: faster development, increased productivity, and the ability to tackle complex coding tasks with minimal human intervention. But as companies rush to adopt these tools, a critical question emerges: are we trading speed for stability?

The Reality Check: AI Creates More Bugs

Recent research from CodeRabbit analyzed 470 open-access GitHub repositories to understand the real impact of AI-generated code. The findings are sobering: AI coding tools create 1.7 times as many bugs as human developers. This isn't just about minor typos—AI-generated pull requests contained 1.3-1.7 times more critical and major issues.

Where AI Falls Short

The most concerning finding? Logic and correctness errors. AI-created PRs had 75% more of these errors, totaling 194 incidences per hundred PRs. These include:

• Logic mistakes
• Dependency and configuration errors
• Control flow errors

These are precisely the kinds of issues that slip through code reviews because they can look reasonable at first glance. Yet they're capable of causing the kind of production outages that make headlines and require shareholder notifications.

The Security and Performance Impact

Beyond logic errors, AI coding agents introduced other serious problems:

• Security issues: 1.5-2x higher rate of improper password handling and insecure object references
• Performance issues: While less frequent, the ones found were heavily AI-created, with excessive I/O operations occurring ~8x more often
• Concurrency and dependency errors: AI was twice as likely to make these mistakes
• Error handling: AI-generated PRs were almost twice as likely to include defensive coding practices, but often implemented them incorrectly

The Readability Crisis

Perhaps most surprisingly, AI code had 3x the readability issues compared to human code. This included 2.66x more formatting problems and 2x more naming inconsistencies. While these don't directly cause outages, they create a maintenance nightmare that compounds over time.

Why AI Makes These Mistakes

The fundamental issue is context. AI models are trained on next-token prediction using vast amounts of public code, but they lack understanding of your specific codebase. When you provide context through system prompts or documentation files, the AI eventually needs to compact or forget information, leading to compounding errors over long-running sessions.

The Review Challenge

AI-generated code is harder to review for several reasons:

• Massive diffs: Agentic tools can generate hundreds of lines of code in minutes
• Poor readability: The code is often harder to understand
• Volume: More code means more potential issues

This creates a perfect storm where serious logic errors can slip through unnoticed.

How to Mitigate AI Coding Risks

If you're using AI coding tools, here's how to protect your codebase:

Pre-Planning

• Use spec-driven development to crystallize requirements
• Create comprehensive context documents
• Define clear style guidelines

Tool Selection

• Don't let users choose their own LLMs—models behave differently
• Use tools that benchmark models for specific tasks
• Understand which models work best for different types of coding tasks

Task Management

• Break tasks into the smallest possible chunks
• Actively engage with the agent rather than letting it run autonomously
• Create small, reviewable commits

Review Process

• Know that AI-assisted PRs will have more issues
• Understand the types of errors AI typically produces
• Consider AI-powered review tools to catch problems

Quality Assurance

• Follow QA checklists rigorously
• Instrument unit tests
• Use static analysis tools
• Ensure solid observability
• Consider fighting AI with AI—use AI in reviews and testing

The Bottom Line

2025 was the year of AI coding speed, but 2026 needs to be the year of AI coding quality. Companies bragging about the percentage of AI-generated code in their repositories are missing the point—lines of code have never been a good productivity metric, and they're even less relevant when those lines introduce technical debt.

The question isn't whether bugs and incidents are inevitable with AI coding agents—it's whether we're willing to accept the trade-offs. With proper planning, tooling, and review processes, we can harness the productivity benefits while minimizing the risks. But ignoring these issues in the name of speed is a recipe for the kind of production outages that no company wants to explain to their users or shareholders.

As one engineering leader put it: "Less haste, more speed." The future of software development isn't about who can generate the most code the fastest—it's about who can deliver reliable, maintainable software that actually works.