There is a gap that most AI coverage ignores. Everyone talks about AI writing code faster. Very few people talk about what happens when that code breaks in a live system at 2 AM on a Saturday.
The gap is real and it is growing. AI tools now write or assist with roughly 41% of all code shipped globally. That speed comes with a catch: a December 2025 analysis of nearly 500 open-source projects found that AI-assisted code contains about 1.7 times more serious issues than human-written code. Security misconfigurations were 75% more common in AI-generated code, and logic errors were widespread. The code compiles, tests pass, and then production breaks it.
Sentry, a San Francisco-based company with over 4 million developers on its platform, has launched Seer Agent to address exactly this problem. It is not an AI coding assistant. It is a diagnostic tool. When something goes wrong in a live application, you describe what you are seeing in plain language, and the agent investigates on its own, pulling together errors, logs, deployment history, and the connections between different parts of the system.
The key detail that separates Seer Agent from a generic AI chatbot is that Sentry has been collecting this data for years. The company processes around 790 billion application events per month. When you ask Seer Agent about a problem, it is not guessing. It is walking through a connected map of everything that happened in that system, in sequence, with full context. It can trace a failure in one part of an application back to a quiet problem in a completely different service that started ten minutes earlier.
Sentry demonstrated this with its own internal incident. Their AI debugger started failing on a Saturday. The errors were vague. Their head of AI opened Seer Agent before the on-call engineers had even joined the call. Within seconds, it identified that a specific AI model was being rate-limited in specific geographic regions by their cloud provider, a pattern that would have taken a human engineer at least 30 minutes of manual investigation to reconstruct.
This matters to non-technical business operators for a specific reason. Your software teams are almost certainly using AI coding tools. In a 2025 survey, 85% of developers reported regularly using AI for coding and debugging. Those tools are accelerating delivery, but they are also introducing a new category of production failure: bugs that are subtle, that slip through testing, and that require someone with deep system knowledge to diagnose. That person may not be available at the moment the failure happens.
Tools like Seer Agent represent the next layer of the AI development stack. The first layer was AI for writing code. The second layer, now emerging, is AI for understanding what the code does when it runs in the real world. Sentry's advantage is that it already sits in that real world. It has been watching how software behaves in production for over a decade across hundreds of thousands of companies, including Anthropic, Reddit, Uber, and Disney.
Seer Agent is currently free to use in open beta for all Sentry customers with the Seer product enabled. The full Seer product is priced at $40 per active developer per month on paid plans. The Slack integration, which lets a whole team watch and redirect an investigation in a shared channel, is also live.
The competitive positioning is worth noting. Datadog and New Relic, the two largest observability platforms, have far more infrastructure monitoring capability. But Sentry's advantage has always been its depth at the code and developer layer, not the server layer. Seer Agent deepens that bet significantly. It is a reasonable place to be when the biggest emerging source of software bugs is the AI tools developers use every day.