An AI agent monitors a support inbox, identifies a bug report, cross-references it with the GitHub codebase to find the issue, suggests probable causes, and then passes the task to another AI to write the fix. This automates the entire debugging lifecycle.

Scanning millions of lines of code is infeasible. Mozilla uses a simple LLM to act as a 'judge,' scoring files on criteria like 'likelihood of a bug' and 'accessibility from the web.' This prioritizes where to focus the more expensive and time-consuming agentic analysis.

The narrative that AI coding decreases quality is outdated. Advanced models like GPT-5.5 excel at complex, systemic tasks that humans often avoid, such as resolving security vulnerabilities or refactoring legacy code, allowing teams to proactively raise their quality bar.

While an AI agent can find and propose a fix for a specific line of code, it often lacks the context to identify and solve the problem class architecturally across the entire codebase. Expert human engineers remain vital for this higher-level reasoning and pattern recognition.

The core open-source belief that enough human experts will find all bugs is invalidated by AI discovering decades-old vulnerabilities in widely scrutinized code. This proves that high-level machine analysis is now essential for security, as human review alone is insufficient.

Most developers admit to giving pull requests only a cursory glance rather than pulling down the code, testing it, and reviewing every line. AI agents are perfectly suited for this meticulous, time-consuming task, promising a new level of rigor in the code review process.

AI coding assistants rapidly conduct complex technical research that would take a human engineer hours. They can synthesize information from disparate sources like GitHub issues, two-year-old developer forum posts, and source code to find solutions to obscure problems in minutes.

While a powerful model like Mythos was helpful, the real breakthrough came from a custom-built 'harness' that gave the AI specific tools and integrated it into Mozilla's existing bug-fixing pipeline, turning raw model output into verified, actionable reports.

AI coding tools have surpassed simple assistance. Expert ML researchers now delegate debugging entirely, feeding an error log to the model and trusting its proposed fix without inspection. This signifies a shift towards AI as an autonomous problem-solver, not just a helper.