Anthropic’s Own Engineers Use AI More for Debugging Code, Than Writing It
February 2026
The dominant narrative around AI coding tools is generation. Describe what you want, get code back. Vibe coding. Prompt-to-production. The story sells because it’s dramatic: developers replaced by natural language.
The data tells a different story.
What Anthropic Actually Found
In August 2025, Anthropic turned the lens on itself. They surveyed 132 engineers and researchers, conducted 53 in-depth interviews, and analyzed 200,000 internal Claude Code transcripts. These are the people who build the model, using it on their own codebase every day.
The top daily use case? Debugging. 55% of engineers use Claude for debugging on a daily basis. Next is code understanding at 42%, having Claude explain existing code to help navigate the codebase. Feature implementation, the thing most people associate with AI coding, comes third at 37%.
The most sophisticated AI engineering team in the world uses its own model primarily to understand and fix what already exists.
This isn’t a quirk of one company. It reflects what software development actually is. Most engineering time isn’t spent writing new code. It’s spent reading code, tracing execution paths, understanding why something broke, and figuring out what a previous developer intended. The ratio of reading to writing in professional software development has always been heavily skewed toward reading. AI is useful here not because it generates code faster, but because it compresses the time to understand code.
How Debugging with AI Works Today
Most AI-assisted debugging right now is essentially static analysis plus conversation. You paste an error message and stack trace into an LLM. It reads the relevant code files. It reasons about what might have gone wrong. It suggests a fix.
This is genuinely useful. As Simon Willison noted in his 2025 year-end review, reasoning models are now exceptional at debugging because they can start with an error and step through many layers of a codebase to find the root cause. The reasoning trick, where models break problems into intermediate steps, turns out to be more valuable for diagnosis than for generation.
The practical workflow looks something like this:
Log-based debugging. The most common pattern. You feed the AI your error output, stack traces, and log files. It reads the surrounding code and proposes hypotheses. In Claude Code, a common technique is asking the agent to add comprehensive loggers to your code, then iterating: paste terminal output back, ask for more targeted logging, narrow down the root cause through successive cycles. Some developers pipe terminal output directly to Claude for a live feed.
Codebase navigation. You point the AI at a module you don’t understand and ask it to explain the execution flow. Anthropic’s study found that their engineers use this constantly when working outside their core expertise. Security teams analyze unfamiliar code. Research teams build frontend visualizations. The AI acts as a senior colleague who has read every file in the repo.
Pattern matching against known issues. AI excels at recognizing common bug patterns: off-by-one errors, race conditions, null pointer dereferences, incorrect async handling. It’s seen millions of examples in training. For these familiar categories, it’s often faster and more reliable than human pattern matching.
Java-specific patterns. For Java developers, the static analysis approach maps well to enterprise debugging workflows. You can feed the AI stack traces with full package names, Spring context errors, JPA relationship issues, or Maven dependency conflicts. The AI handles verbose Java exception hierarchies better than most humans because it doesn’t get fatigued by the depth. The IDE integration path is also clear: IntelliJ, Eclipse, and NetBeans all have AI plugins now that can analyze errors in context. JetBrains’ Spring Debugger plugin, for instance, gives you the entire ApplicationContext for inspection, which combined with AI analysis lets you trace configuration issues across complex enterprise applications.
All of this works within a static paradigm. The AI reads code and reasons about it. It doesn’t actually run anything or observe runtime behavior.
The Interactive Debugging Frontier
The next step is already emerging: giving AI agents access to actual debuggers. This is where things get interesting, and where the gap between today’s practice and tomorrow’s tooling is most visible.
Microsoft Research’s debug-gym. Released in early 2025, debug-gym is a text-based environment that gives LLM agents access to Python’s pdb debugger. The agent can set breakpoints, step through execution, inspect variable values, and navigate stack frames, exactly what human developers do when they fire up a debugger. The results were striking. On SWE-bench Lite, Claude 3.7 Sonnet went from 37.2% success in rewrite-only mode to 48.4% with debugger access. That’s a 30% improvement just from letting the model interact with runtime state rather than guessing from static code. Even more interesting was their “debug(5)” approach, where the debugger is only made available after five failed rewrite attempts. This scored 52.1%, suggesting the model benefits from trying static reasoning first and escalating to interactive debugging when it gets stuck. Exactly how good human developers work.
InspectCoder’s dual-agent approach. InspectCoder, published in October 2025, goes further. It uses two collaborating agents: a Program Inspector that manages breakpoints and inspects runtime state, and a Patch Coder that synthesizes fixes based on what the inspector found. The key innovation is that the inspector can modify runtime values at breakpoints to test hypotheses without changing source files. It achieved bug resolve rates 5% to 60% better than static approaches across different models, with an average of 20 bug fixes per hour. The authors note their approach is fundamentally language-agnostic: Java has JDB, C/C++ has GDB, JavaScript has V8 Inspector Protocol, Go has Delve. The same breakpoint management and variable inspection capabilities exist across the enterprise language ecosystem.
ChatDBG for native code. ChatDBG, published in 2025, takes a different tack: instead of building a standalone agent, it integrates directly into existing debuggers as a plugin. It works with GDB and LLDB for C/C++/Rust and with Pdb for Python. You can ask it natural language questions like “why is x null?” and the LLM takes autonomous control of the debugger to investigate, issuing commands to navigate stack frames and inspect program state. The key insight is that the LLM doesn’t replace the debugger; it becomes a collaborator who can drive the debugger on your behalf while you guide the investigation.
Claude Debugs For You. On the practical tooling side, Jason McGhee built an MCP server and VS Code extension that connects Claude directly to VS Code’s debugger. It’s language-agnostic. Claude can set breakpoints, step through code, and evaluate expressions in the actual debugging session. This is the consumer-grade version of what the research papers describe: an AI that doesn’t just read your code, but watches it run.
Why This Matters Beyond Developer Tools
The debugging pattern maps directly to enterprise software operations. Most enterprise IT time isn’t spent deploying new systems. It’s spent troubleshooting existing ones. Understanding why a pricing rule isn’t firing correctly. Tracing why an integration dropped records. Figuring out what a workflow is actually doing versus what the documentation says.
Today, that’s manual work. Someone opens the admin console, clicks through configuration screens, reads logs, maybe calls the vendor. AI-assisted debugging for enterprise software follows the same trajectory as developer debugging:
Static analysis first. Feed the AI your error logs, configuration exports, and integration mappings. Ask it to reason about what went wrong. This works now with any LLM that has context about the platform.
Then interactive. The real unlock will be AI agents that can connect to enterprise systems through APIs and MCP servers, query live data, test configuration changes in sandboxes, and trace execution through integration pipelines. Not just reasoning about what might be wrong, but observing what is actually happening at runtime.
The organizations building this capability, giving AI agents debugger-level access to their enterprise systems rather than just chat-level access, will resolve issues in minutes that currently take hours of manual investigation.
The Discipline Requirement
There’s an important caveat in the Anthropic data. Despite using Claude for 59% of their work, most engineers report they can fully delegate only 0-20% of tasks. Claude is a constant collaborator, but it requires active supervision and validation.
This holds true for debugging as well. The AI can generate hypotheses faster than any human. It can hold more code context in memory simultaneously. It can pattern-match across more bug categories. But it doesn’t understand your business intent. It doesn’t know that this particular race condition only manifests under a specific load pattern that happens on the third Tuesday of each month during batch processing.
The developers getting the most from AI debugging aren’t the ones who paste an error and accept the first fix. They’re the ones who use AI to accelerate their own diagnostic process, testing hypotheses faster, exploring more code paths, and narrowing the search space. The AI speeds up each cycle. The human directs which cycles to run.
The same principle will apply to enterprise debugging. AI that can connect to your SAP system and trace a pricing discrepancy is valuable. AI that does so while a knowledgeable administrator guides the investigation, confirming or rejecting hypotheses based on business context, is transformative.
Where This Is Heading
The progression is clear. Static analysis is the baseline, already useful, widely available. Interactive debugging with runtime access is emerging in developer tools and will reach enterprise platforms as MCP adoption spreads and vendors expose more debugging-level APIs. The organizations investing in this infrastructure now, building API-first systems, deploying MCP servers, creating sandboxed environments where AI agents can safely inspect and test, will have an enormous advantage when the models are ready for full interactive debugging at enterprise scale.
Anthropic’s own data tells us where the value is. Not in generating new code or new configurations from prompts. In understanding, diagnosing, and fixing what already exists. The unglamorous work that consumes most of every engineer’s day.
The killer app for AI in software isn’t creation. It’s comprehension.
At Rainvent.ai, we’re tracking how AI is transforming enterprise software operations. The shift from manual troubleshooting to AI-assisted debugging is one of the most practical and underappreciated developments in enterprise AI today.
