Written by: Nimesh Chakravarthi, Co-founder & CTO, Struct
Key Takeaways for Sentry Seer and Struct
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Sentry Seer is an AI-powered debugging agent that automates root cause analysis, alert grouping, and autofix PR generation within the Sentry ecosystem.
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Seer effectively reduces alert noise for application-level exceptions but cannot investigate infrastructure incidents outside Sentry telemetry.
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Seer Agent enables natural-language queries in Sentry and Slack, yet its scope remains limited to Sentry data sources.
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Seer’s autofix and local debugging features streamline code fixes and shift-left testing but do not address multi-tool on-call scenarios.
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Teams that need automated investigations across Datadog, CloudWatch, and PagerDuty use Struct to automate their on-call runbook.
The Problem: Alert Noise and Manual Correlation in Sentry Workflows
On-call engineers at Seed-to-Series C companies typically face alerts that span Sentry exceptions, Datadog metrics, CloudWatch logs, and PagerDuty incidents simultaneously. Correlating those signals manually takes 30–45 minutes per incident, and that figure compounds across a week of on-call rotations.
Sentry Seer addresses the Sentry-side of that problem well. Seer is limited to the Sentry ecosystem and does not investigate infrastructure incidents such as pod crashes, resource exhaustion, network failures, or configuration drift. When the root cause lives in a Datadog metric or a CloudWatch log group, Seer cannot autonomously retrieve or correlate that data.
Engineers still context-switch between tools, and the 3 a.m. investigation still requires manual effort for anything outside Sentry’s telemetry boundary.
How Seer Reduces Alert Noise Inside Sentry
Seer automatically identifies the most actionable production issues and performs root cause analysis in the background. Engineers do not need to open a ticket or write a query to start that analysis. Seer uses Sentry’s production telemetry, including errors, traces, logs, and metrics, to determine why a failure occurred, not just where it surfaced.
Seer combines source code with live application behavior to identify failures that propagate across services or network boundaries. It also highlights latency spikes caused by contention or resource saturation and errors that occur only under production traffic patterns.
For teams whose alert noise is primarily application-level exceptions, this background triage meaningfully reduces the volume of issues that require human review.
Seer vs Manual Triage in Sentry and Slack
Seer Agent, launched in beta in April 2026, adds a natural-language investigation layer on top of Sentry’s telemetry across errors, spans, logs, traces, and code context. Engineers can ask questions like “Why is this page slow?” or “What changed before this started?” without knowing exactly where to look inside Sentry.
Seer Agent is also available in Slack, so users can start an investigation by messaging the agent in any channel. Multiple people can query it, redirect mid-step, add context, or observe the incident-to-resolution process in real time.
That Slack integration narrows the gap between alert notification and investigation start. However, Seer Agent’s answers are bounded by what Sentry’s telemetry contains. Questions about infrastructure state, external service health, or non-Sentry log sources fall outside its scope.
See how Struct investigates across all your tools, not just Sentry
Seer Pull-Request Generation and Autofix Capabilities
Seer can generate pull requests for new errors in GitHub as part of an automated remediation workflow. For production incidents where sufficient context exists, Seer generates suggested code changes or delegates fixes to supported coding agents.
Seer does not provide autonomous remediation beyond generating pull requests. It can suggest fixes and open PRs but cannot execute operational actions such as rollbacks or restarts. This limitation becomes critical when the fix requires operational intervention rather than code changes. Configuration updates, feature flag toggles, or database queries all fall outside Seer’s autofix scope.
Even within code fixes, Sentry has an MCP server and Autofix feature that can suggest code fixes for certain errors, yet its platform complexity works against the simplicity required for AI agents to operate quickly across incident data.
Seer Local Debugging and Shift-Left Features
In January 2026, Sentry expanded Seer beyond production debugging to support local development and code review debugging under a single flat pricing model.
For local development, Seer connects to coding agents through the Sentry MCP server so application telemetry from reproductions can be analyzed for root cause and used to generate fixes before code is committed. For code review, Seer analyzes pull requests to identify defects likely to cause production failures and focuses on high-impact bugs rather than stylistic feedback.
These shift-left capabilities reduce the rate at which bugs reach production. They do not address the on-call scenario where a production incident has already fired and the root cause spans multiple observability platforms.
Comparison Table: Seer vs Traditional Sentry
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Capability |
Traditional Sentry (no Seer) |
Sentry with Seer |
|---|---|---|
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Root-cause scope |
Manual stack-trace review, engineer-driven |
Automated background RCA using Sentry errors, traces, logs, and metrics, bounded to Sentry telemetry |
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Alert grouping |
Rule-based fingerprinting, manual tuning required |
AI-assisted grouping using stack traces, event history, and performance profiles |
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Autofix PRs |
Not available, engineer writes fix manually |
PR generation for new errors in GitHub, no operational actions such as rollbacks or restarts |
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Natural-language queries |
Not available, requires navigating Sentry UI manually |
Seer Agent (beta, April 2026) queries errors, spans, logs, traces via natural language in Sentry or Slack |
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Local debugging |
Not available, developer reproduces bugs without telemetry context |
MCP server connects Seer to local coding agents for telemetry-informed fix generation (January 2026) |
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Multi-tool coverage |
Sentry data only, no native Datadog, CloudWatch, or PagerDuty correlation |
Sentry data only, infrastructure incidents outside Sentry scope remain uninvestigated by Seer |
The Limitation: Sentry-Only Scope in Multi-Tool Environments
Most engineering teams at this scale run Sentry alongside Datadog, CloudWatch, Grafana, and PagerDuty. A production incident frequently involves signals from several of these simultaneously. A Datadog anomaly detector fires, CloudWatch shows elevated error rates, and Sentry captures the resulting exceptions.
As established earlier, Seer’s scope ends at Sentry’s telemetry boundary. When a production incident involves Datadog anomalies and CloudWatch error rates alongside Sentry exceptions, the non-Sentry signals require separate manual investigation. Any incident with a root cause outside that boundary still depends on human correlation.
For on-call engineers, that ambiguity translates directly into additional manual investigation time at 3 a.m.
Eliminate manual correlation with automated multi-tool investigation
The Solution: Multi-Tool Automated Investigation with Struct
Struct is an AI-powered automated on-call investigation platform that integrates directly with Slack, PagerDuty, Datadog, AWS CloudWatch, GCP Logs, Azure, Grafana, Sentry, and GitHub. When an alert fires, Struct automatically begins its investigation without waiting for an engineer to open a terminal or paste logs into a chat window.
By the time an engineer opens their laptop, Struct has correlated logs across tools, mapped a unified timeline, identified the root cause, and surfaced suggested fixes inside a dynamically generated dashboard. Standard manual investigations are completed by Struct in under 5–10 minutes, reducing the 30–45 minute baseline by 80%.
Where Seer Agent answers questions about Sentry telemetry in Slack, Struct’s Slack-native conversational AI queries the entire stack in a single thread. Datadog metrics, CloudWatch logs, Sentry exceptions, and GitHub commits all appear in one place. Engineers ask follow-up questions, test alternative hypotheses, or request additional log windows without leaving Slack or switching tools.
Struct also encodes team-specific on-call runbooks directly into its investigation logic, so every automated first-pass follows the same operational procedures a senior engineer would apply. This approach makes it safe for junior engineers to handle on-call shifts without escalation and protects SLA windows that a 45-minute manual triage would otherwise breach.
For teams already using Sentry and evaluating whether Seer closes the multi-tool gap, Seer does not close that gap by design. Seer is purpose-built for Sentry telemetry. Struct is purpose-built for the full incident surface.
Frequently Asked Questions
Does Seer require high-quality Sentry data to function?
Yes. Seer’s root cause analysis depends on the richness of Sentry’s telemetry, including stack traces, distributed traces, session replays, performance profiles, and event history. Teams with sparse instrumentation, missing trace IDs, or low event volume will see reduced accuracy from Seer’s automated grouping and RCA features. The same principle applies to Struct. The platform relies on the data your integrations provide. Teams already using Sentry, Datadog or cloud logs, and Slack for alerts are the strongest fit for both tools.
Is Seer suitable for teams smaller than 50 engineers?
Seer is available to Sentry users on Team or Business plans at $40 per active contributor per month, where a contributor is counted if they create at least two pull requests in a connected repository during the month. For small teams with low PR volume, the per-seat cost is manageable. The more relevant consideration is whether the team’s incident surface is confined to Sentry-visible application errors. Teams running multi-tool observability stacks, which is common even at 10–20 engineers, will encounter Seer’s scope limitations on infrastructure-layer incidents regardless of team size.
How does Seer handle compliance and data residency?
Seer operates within Sentry’s existing data infrastructure and inherits Sentry’s compliance posture. Teams with strict data residency requirements should review Sentry’s regional data storage options directly. Struct is SOC 2 and HIPAA compliant, with logs accessed and processed ephemerally. Teams that require full on-premise deployment with zero log egress from their VPC are not currently a fit for Struct’s cloud-based architecture.
Should teams build their own multi-tool agent or buy a platform?
Building a custom agent that queries Datadog, CloudWatch, Sentry, and GitHub requires maintaining authentication, rate-limit handling, schema normalization, and prompt engineering for each tool. Teams must also handle the operational overhead of keeping integrations current as APIs change. The build cost compounds quickly for teams without dedicated platform engineering resources. Struct’s 10-minute setup, composable runbook encoding, and pre-built integrations across the standard observability stack represent the buy-side alternative for teams in this growth stage that need triage automation without a multi-quarter internal build.
Conclusion: Matching Seer and Struct to Your On-Call Scope
Sentry Seer is a well-scoped AI debugging agent. Its January 2026 expansion into local development and code review and the April 2026 Seer Agent beta represent meaningful progress on reducing manual investigation time within the Sentry ecosystem. For teams whose incidents are primarily application-layer exceptions visible in Sentry, Seer shortens triage cycles and automates PR generation without additional tooling.
For teams running Sentry alongside Datadog, CloudWatch, PagerDuty, and GitHub, a production incident often requires correlating signals across all of them. Seer addresses one layer of the problem. The remaining layers still require manual effort unless a multi-tool platform handles the full first-pass investigation automatically.
Struct fills that gap. The platform is proactive, multi-tool, Slack-native, and configured in under 10 minutes.
Start automating your multi-tool investigations in 10 minutes
