Sentry announced new capabilities for Seer, its AI-powered debugging agent, extending debugging support to local development and code review, and introducing a simplified, flat pricing model with unlimited usage.
“Wherever code breaks, Seer helps you fix it faster,” said Sentry CEO Milin Desai. “It’s the next step in Sentry’s evolution; a fundamentally smarter way to understand what’s happening with your code from development to production, and how to fix it.”
Seer is built on Sentry’s production telemetry, including errors, traces, logs, and metrics. That runtime context allows Seer to diagnose failures that cannot be reliably identified through static code analysis alone, particularly in distributed systems where issues often cross service and infrastructure boundaries.
“After more than a decade of helping developers find and tackle bugs, Sentry has an unrivaled understanding of what breaks in production and why,” Desai said. “With that context, we can move beyond flagging issues after the fact to explaining them in real time, automatically identifying the root cause for developers and their coding agents, and even anticipating problems before code goes live.”
Seer combines source code with live application behavior to identify these problems, including:
- Failures that propagate across services or network boundaries
- Latency spikes caused by contention or resource saturation
- Errors that occur only under production traffic patterns
By analyzing how software actually behaves, Seer focuses on determining why a failure occurred, not just where it surfaced.
“Failures in modern systems often cannot be debugged by simply reading the code,” Indragie Karunaratne, director of engineering at Sentry said. “Seer is designed for that reality. It looks at how services behave together at runtime, which makes it possible to identify root causes that static analysis and code review alone can’t catch.”
With this release, Sentry is expanding Seer’s role beyond production debugging to support developers earlier in their workflow.
Seer connects to local coding agents through the Sentry MCP server. As developers reproduce bugs locally, application telemetry is sent to Sentry, where Seer can analyze raw events and perform root cause analysis. That context can be used by coding agents to generate fixes before code is committed.
Seer can analyze pull requests to identify defects likely to cause production failures. The system is designed to focus on high-impact issues rather than stylistic feedback, helping teams catch real bugs before code is merged.
When bugs reach production, Seer automatically identifies the most actionable issues and performs root cause analysis in the background. When sufficient context exists, it can generate suggested code changes or delegate fixes to supported coding agents.
Sentry is also developing an experimental capability that allows developers to ask Seer open-ended questions about their telemetry. This feature is intended for investigations that begin with symptoms rather than a clearly identified bug, such as unexplained performance regressions or anomalous trends. The capability is currently available in early preview for select customers.
“Seer represents our broader vision: Sentry as the intelligence and reasoning layer for modern software development, and as a platform teams use to see, explain, and fix what matters,” Desai said.
The Latest
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...
When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...