{"id":694,"date":"2026-06-29T05:00:22","date_gmt":"2026-06-29T05:00:22","guid":{"rendered":"https:\/\/struct.ai\/articles\/coroot-alternative-root-cause-analysis\/"},"modified":"2026-06-29T05:00:22","modified_gmt":"2026-06-29T05:00:22","slug":"coroot-alternative-root-cause-analysis","status":"publish","type":"post","link":"https:\/\/struct.ai\/articles\/coroot-alternative-root-cause-analysis\/","title":{"rendered":"Struct vs Coroot: Automated RCA for On-Call Engineers"},"content":{"rendered":"<p><em>Written by: Nimesh Chakravarthi, Co-founder &amp; CTO, Struct<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways for Busy Engineering Teams<\/h2>\n<ul>\n<li>Automated root cause analysis uses AI to correlate logs, metrics, traces, and code the moment an alert fires. Engineers receive a complete investigation report in under 10 minutes.<\/li>\n<li>Manual triage across siloed observability tools creates alert fatigue, longer MTTR, and burnout, especially for small teams handling recurring incidents.<\/li>\n<li>Struct connects to tools like Slack, Datadog, Sentry, and GitHub and posts structured investigation reports directly into alert threads, so engineers stay in their existing workflow.<\/li>\n<li>Teams evaluating automated RCA should confirm telemetry readiness, alert hygiene, and compliance needs before rollout to get reliable, high-confidence investigations.<\/li>\n<li><a href=\"https:\/\/cal.com\/deepanm\/struct-demo\" target=\"_blank\">Automate your on-call runbook<\/a> with Struct to cut triage time by roughly 80% and remove manual log-hunting from incidents.<\/li>\n<\/ul>\n<h2>The Problem: Manual Root Cause Analysis at 3 AM<\/h2>\n<p>A distributed microservices environment generates alerts from dozens of independent services at once. When one fires at 3 AM, the on-call engineer acknowledges the page, opens Datadog or Grafana to assess blast radius, checks exceptions in Sentry, traces correlation IDs through AWS CloudWatch or GCP logs, and maps findings back to a recent GitHub commit. All of this happens while half-asleep and under SLA pressure.<\/p>\n<p>This workflow has three structural failure modes. First, context is siloed. Logs live in one tool, traces in another, and the code change that caused the regression in a third. No single pane of glass exists by default. Second, tribal knowledge dominates. Newer engineers lack the systemic context to navigate these tools confidently, so senior engineers get escalated into every non-trivial incident. Third, alert volume compounds the problem. When every alert demands the same manual investigation workflow, engineers start ignoring low-priority pages and occasionally miss a critical one buried in the noise.<\/p>\n<p>These structural failures translate directly into measurable operational costs. A senior engineer earning $200,000 per year who spends a full week reacting to recurring alerts produces no product output during that period. For Series A and B companies with small engineering teams, this scenario is common. It becomes a recurring drag on product velocity that grows with every new service added to the stack.<\/p>\n<h2>What Automated Root Cause Analysis Actually Delivers<\/h2>\n<p>Automated RCA tools intercept an alert the moment it fires and run the investigation workflow autonomously. They replace the manual, multi-tool triage process with a single, pre-populated report that an engineer can review and act on instead of assembling context from scratch.<\/p>\n<p>The category spans three maturity levels. Assisted RCA tools surface relevant logs and metrics but still rely on the engineer to draw conclusions. AI-correlated RCA tools identify anomalies and suggest probable causes, while requiring human confirmation at each step. Fully automated RCA tools, the most advanced tier, execute the entire first-pass investigation without any human prompt. They deliver a root cause, impact summary, and suggested fix before the engineer opens a laptop.<\/p>\n<p>Effective tools in this category share several capabilities. They correlate multiple signals, such as logs, metrics, traces, and code, into a single timeline. They quantify blast radius by showing how many users or services are affected. They provide conversational follow-up interfaces for hypothesis testing. They also apply runbook-aware investigation logic that mirrors how a senior engineer would approach a known alert type.<\/p>\n<h2>How Struct Fits Into Existing Incident Response Workflows<\/h2>\n<p>Automated RCA only creates value when it works inside the tools engineering teams already use, not by replacing them. <a href=\"https:\/\/www.producthunt.com\/products\/struct-2\" target=\"_blank\">Struct integrates with tools like Slack, GitHub, and observability platforms for quick deployment<\/a>, connecting to Datadog, Prometheus, Grafana, Sentry, AWS CloudWatch, GCP Logs, Azure Logs, Loki, Sumo Logic, Better Stack, PagerDuty, Linear, and Jira.<\/p>\n<p>The integration model is additive. Struct listens to designated Slack channels or PagerDuty alert streams. When an alert fires, it queries the connected observability and code sources in the background. It then posts a structured investigation report, including dynamically generated charts, a unified timeline, and a root cause assessment, directly into the alert thread. Engineers interact with the report in Slack and avoid constant tool switching.<\/p>\n<p>Two prerequisites determine integration quality: telemetry completeness and alert hygiene. Teams without structured logging, trace IDs, or consistent alerting triggers receive lower-confidence investigations. The ideal starting point is a team already using Sentry for exceptions, Datadog or cloud-native logs for infrastructure telemetry, and Slack for alert routing.<\/p>\n<h2>Rolling Out Automated RCA: Stages and What to Check<\/h2>\n<p>Evaluating an automated RCA tool follows four sequential stages. First, assess telemetry readiness by confirming that logs, metrics, and traces are structured and queryable. Without clean telemetry, no RCA tool can produce reliable results.<\/p>\n<p>Next, verify alert hygiene to ensure alert channels are scoped and not saturated with noise. High-quality investigations depend on high-quality alert triggers. Once telemetry and alerts are validated, move to workflow integration by connecting the RCA tool to existing alert sources and observability platforms.<\/p>\n<p>Finally, customize runbooks to encode team-specific investigation logic and correlation ID formats. This step tailors the tool to your operational patterns and makes automated investigations feel like a senior engineer wrote them.<\/p>\n<p>The following table compares five evaluation dimensions across manual triage, a representative open-source observability tool, and Struct.<\/p>\n<table>\n<thead>\n<tr>\n<th>Evaluation Dimension<\/th>\n<th>Manual Triage<\/th>\n<th>Open-Source Observability (e.g., Coroot)<\/th>\n<th>Struct<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Setup Time<\/td>\n<td>N\/A (existing workflow)<\/td>\n<td>Hours to days (self-hosted deployment)<\/td>\n<td><a href=\"https:\/\/www.producthunt.com\/products\/struct-2\" target=\"_blank\">Under 10 minutes<\/a><\/td>\n<\/tr>\n<tr>\n<td>Slack \/ PagerDuty Integration<\/td>\n<td>Manual alert acknowledgment only<\/td>\n<td>Webhook-based, no native conversational interface<\/td>\n<td>Native Slack thread investigation and conversational follow-up<\/td>\n<\/tr>\n<tr>\n<td>Kubernetes \/ eBPF Support<\/td>\n<td>Requires manual kubectl and log queries<\/td>\n<td>eBPF-based auto-instrumentation (core Coroot feature)<\/td>\n<td>Kubernetes-aware via connected observability integrations (Datadog, Prometheus, GCP)<\/td>\n<\/tr>\n<tr>\n<td>MTTR Impact<\/td>\n<td>30\u201345 min investigation phase per alert<\/td>\n<td>Reduces instrumentation overhead, triage still manual<\/td>\n<td><a href=\"https:\/\/www.producthunt.com\/products\/struct-2\" target=\"_blank\">80% reduction in triage time, investigation completed in about 5\u201310 minutes<\/a><\/td>\n<\/tr>\n<tr>\n<td>Runbook Customization<\/td>\n<td>Engineer-dependent tribal knowledge<\/td>\n<td>Not applicable (observability layer only)<\/td>\n<td>Custom runbooks, correlation ID formats, and composable investigation widgets<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Teams that want to see this workflow in their own Slack or PagerDuty setup can <a href=\"https:\/\/cal.com\/deepanm\/struct-demo\" target=\"_blank\">schedule a demo to see Struct in action<\/a>.<\/p>\n<h2>Build vs. Buy: Security, Compliance, and Time-to-Value<\/h2>\n<p>Seed-to-Series C engineering teams sometimes consider building internal RCA tooling. The build path demands sustained investment in prompt engineering, observability API integrations, data pipeline maintenance, and security review. That work grows with every new service and every new data source.<\/p>\n<p>The opportunity cost lands on product engineering. Time spent on internal RCA infrastructure rarely produces direct revenue impact. It also creates a long-term maintenance burden that competes with feature delivery and reliability improvements.<\/p>\n<p>The buy path introduces compliance and data-handling questions. Struct is SOC 2 and HIPAA compliant, with logs accessed and processed ephemerally. For most Seed-to-Series C companies, this posture satisfies security requirements. Teams with strict on-premise or zero-egress mandates should confirm that a cloud-integrated tool fits their constraints before moving forward.<\/p>\n<p>On pricing, Struct offers a Startup tier (up to 5 users, 30 issues per month), a Growth tier (unlimited users, 200 issues per month), and an Enterprise tier with custom volume, dedicated support, and sidecar or on-prem options. All tiers include a 30-day risk-free pilot with white-glove onboarding. This structure reduces evaluation risk that often makes enterprise AIOps platforms impractical for smaller teams.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>Does Struct replace Coroot or other observability tools already in use?<\/h3>\n<p>Struct does not replace existing observability tools. It operates as an investigation layer on top of current infrastructure. It reads from tools like Datadog, Prometheus, Grafana, Sentry, and cloud-native log platforms instead of displacing them. Teams that use Coroot for eBPF-based auto-instrumentation can continue doing so. Struct adds the automated investigation and root cause layer that Coroot does not provide natively.<\/p>\n<h3>Does automated RCA replace software engineers?<\/h3>\n<p>Automated RCA handles the first-pass investigation by correlating signals, generating timelines, and surfacing probable root causes. Engineers still confirm the diagnosis, decide on the fix, and execute the resolution. Struct removes the manual log-hunting phase, not the engineering judgment that follows it. Junior engineers can handle on-call shifts with more confidence because they receive a fully contextualized starting point for every alert.<\/p>\n<h3>What data quality is required for Struct to work effectively?<\/h3>\n<p>Struct relies on structured, queryable telemetry. Teams should have basic logging in place with trace or correlation IDs, at least one observability platform connected such as Datadog, CloudWatch, or GCP Logs, and an alerting trigger source like a Slack channel, PagerDuty, or Sentry. Teams without structured logging or consistent alerting receive lower-confidence investigations. Struct does not reconstruct system state from code analysis alone.<\/p>\n<h3>How quickly can a team go from signup to first automated investigation?<\/h3>\n<p>Initial setup is rapid. The process involves authenticating an alert source such as Slack or PagerDuty, connecting a code repository like GitHub, and linking at least one observability platform. Once these connections exist, auto-investigations activate immediately. The first automated investigation runs on the next alert that fires in the configured channel, with no extra configuration required.<\/p>\n<h3>Is Struct suitable for small software engineering teams, or does it require scale to deliver value?<\/h3>\n<p>Struct is purpose-built for Seed-to-Series C teams. Smaller teams often experience the highest per-engineer on-call burden because there are fewer people to absorb alert volume and less redundancy when a senior engineer is unavailable. The rapid setup, startup-tier pricing, and Slack-native workflow are designed for teams that cannot support lengthy enterprise deployments or dedicated reliability engineering headcount.<\/p>\n<h2>Conclusion: When Automated RCA Makes Sense<\/h2>\n<p>Manual root cause analysis acts as a structural bottleneck in any engineering organization that runs distributed systems at meaningful alert volume. The investigation phase, which correlates logs, metrics, traces, and code across siloed tools, consumes 30 to 45 minutes per incident and scales poorly as teams and services grow.<\/p>\n<p>Automated RCA removes this bottleneck by executing the investigation workflow autonomously when an alert fires. <a href=\"https:\/\/www.producthunt.com\/products\/struct-2\" target=\"_blank\">Companies like FERMAT and Arcana use Struct to investigate thousands of alerts monthly, with large-scale customers reporting an 80% reduction in triage time.<\/a> As Struct co-founder Deepan Mehta describes it, <a href=\"https:\/\/www.producthunt.com\/products\/struct-2\" target=\"_blank\">&#8220;Struct gets you from alert \u2192 root cause before you even open your laptop.&#8221;<\/a><\/p>\n<p>Teams considering this category should first assess telemetry readiness, alert hygiene, and compliance requirements. For engineering organizations already using Slack, Datadog or cloud-native logs, and GitHub, and facing recurring 3 AM triage cycles, the time-to-value case for automated RCA is straightforward.<\/p>\n<p>For teams experiencing that recurring 3 AM on-call pattern, you can <a href=\"https:\/\/cal.com\/deepanm\/struct-demo\" target=\"_blank\">book a walkthrough to see how Struct\u2019s automated RCA fits your stack<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Struct delivers automated RCA in under 10 min \u2014 correlating logs, metrics &amp; code. See why on-call engineers choose Struct over Coroot. Try it free.<\/p>\n","protected":false},"author":73,"featured_media":693,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-694","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/posts\/694","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/comments?post=694"}],"version-history":[{"count":0,"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/posts\/694\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/media\/693"}],"wp:attachment":[{"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/media?parent=694"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/categories?post=694"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/tags?post=694"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}