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Sentry Releases Seer Agent in Beta

Sentry announced the launch of Seer Agent, a new feature that enables developers to investigate and resolve production problems using natural language.

Seer Agent uses Sentry’s complete telemetry (errors, spans, logs, traces, and code context) to surface answers and connections that would take considerable time and deep insights for teams to find on their own. The launch marks a major addition to Sentry’s Seer platform and dramatically reduces the time spent debugging, so developers can get back to building what they want to build instead of chasing bugs.

Developers can ask Seer Agent questions like:

Why is this page slow?
What caused this spike?
What changed before this started?

“When something breaks in production, you’re working across errors, spans, logs, metrics, and more, simultaneously. The volume of data alone makes it hard to know where to start,” said Indragie Karunaratne, Senior Director of Engineering, Sentry. “Seer Agent queries all of the sources, connects the relevant signals, and identifies what went wrong and where. Investigations that used to take hours now take minutes.”

Seer Agent is built on three core capabilities:

  • Natural language queries: Ask any question about your application without needing to know exactly where to look in Sentry.
  • Connected context: Surface relationships across errors, spans, logs, traces, and code context that a developer might never have found through manual navigation.
  • Agentic investigation: Walks developers through complex production problems by reasoning through evidence in real time, surfacing what matters from Sentry’s vast data.

In addition, Seer Agent is now available in Slack, allowing users to start an investigation by messaging Seer Agent in any channel. It makes the experience multi-player, by allowing anyone in the channel to query it, redirect mid-step, and add context the agent didn’t previously have. Alternatively, channel participants can watch the team go from incident to resolution as an observer to better learn the system.

“Most teams don’t struggle to know something’s broken. They struggle to know what to fix. Sentry has spent more than a decade building the production telemetry that answers that, and Seer is how we put it to work everywhere developers already are - powering the most complete root cause analysis, automations that hand fixes off to coding agents like Cursor and Claude Code, and opening up our data through MCP and the CLI,” said Milin Desai, CEO, Sentry. “In Slack, the investigation becomes multiplayer. The dev team can swarm an incident, redirect Seer mid-step, and leave the thread behind as a record of how it got solved. Seer Agent is one more way to engage with it.”

Seer Agent is available for all Sentry users while in beta.

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Sentry Releases Seer Agent in Beta

Sentry announced the launch of Seer Agent, a new feature that enables developers to investigate and resolve production problems using natural language.

Seer Agent uses Sentry’s complete telemetry (errors, spans, logs, traces, and code context) to surface answers and connections that would take considerable time and deep insights for teams to find on their own. The launch marks a major addition to Sentry’s Seer platform and dramatically reduces the time spent debugging, so developers can get back to building what they want to build instead of chasing bugs.

Developers can ask Seer Agent questions like:

Why is this page slow?
What caused this spike?
What changed before this started?

“When something breaks in production, you’re working across errors, spans, logs, metrics, and more, simultaneously. The volume of data alone makes it hard to know where to start,” said Indragie Karunaratne, Senior Director of Engineering, Sentry. “Seer Agent queries all of the sources, connects the relevant signals, and identifies what went wrong and where. Investigations that used to take hours now take minutes.”

Seer Agent is built on three core capabilities:

  • Natural language queries: Ask any question about your application without needing to know exactly where to look in Sentry.
  • Connected context: Surface relationships across errors, spans, logs, traces, and code context that a developer might never have found through manual navigation.
  • Agentic investigation: Walks developers through complex production problems by reasoning through evidence in real time, surfacing what matters from Sentry’s vast data.

In addition, Seer Agent is now available in Slack, allowing users to start an investigation by messaging Seer Agent in any channel. It makes the experience multi-player, by allowing anyone in the channel to query it, redirect mid-step, and add context the agent didn’t previously have. Alternatively, channel participants can watch the team go from incident to resolution as an observer to better learn the system.

“Most teams don’t struggle to know something’s broken. They struggle to know what to fix. Sentry has spent more than a decade building the production telemetry that answers that, and Seer is how we put it to work everywhere developers already are - powering the most complete root cause analysis, automations that hand fixes off to coding agents like Cursor and Claude Code, and opening up our data through MCP and the CLI,” said Milin Desai, CEO, Sentry. “In Slack, the investigation becomes multiplayer. The dev team can swarm an incident, redirect Seer mid-step, and leave the thread behind as a record of how it got solved. Seer Agent is one more way to engage with it.”

Seer Agent is available for all Sentry users while in beta.

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Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

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