How Struct’s Agentic AI Pipeline Handles Incident Triage

Written by: Nimesh Chakravarthi, Co-founder & CTO, Struct

Key Takeaways

• Struct uses a four-stage agentic AI pipeline of data normalization, dynamic triage plans, automated investigation, and transparent verdicts to cut false positives and manual work for on-call engineers.
• The pipeline logic proven in security operations now powers engineering reliability workflows, turning 30–45 minute manual investigations into 5–10 minute automated reviews.
• Struct delivers an 80% reduction in triage time and an 85–90%+ helpful investigation rate while requiring only a 10-minute setup for teams using Slack, GitHub, and standard observability tools.
• Junior engineers receive a fully contextualized starting point for every alert, which makes on-call rotations safer without relying on deep tribal knowledge.
• See how Struct handles your next alert end-to-end with automated investigation and clear verdicts.

Struct’s Four-Stage Incident Triage Pipeline

1. Intelligent Data Pipeline normalizes and enriches raw alert data from disparate sources into a unified context layer.
2. Dynamic AI Triage Plans generate a custom investigation plan per alert instead of following a fixed static playbook.
3. Automated Investigation executes the plan autonomously, querying relevant data sources and correlating findings.
4. Transparent Verdicts deliver a human-readable conclusion with full evidence chains, which enables one-click escalation or dismissal.

Apply this four-stage pipeline to your engineering alerts and reduce manual triage time.

1. Objective and Current On-Call Reality

It is 3 a.m. An alert fires in a Slack channel. A senior engineer earning $200,000 per year opens their laptop and begins the familiar ritual: acknowledge the alert in PagerDuty, switch to Datadog to assess the blast radius, pivot to Sentry for exception traces, cross-reference GitHub for recent deploys, and attempt to correlate everything manually. Thirty to forty-five minutes later, a root cause emerges, or the engineer escalates and the cycle repeats.

This pattern defines the current state for most Seed-to-Series-C engineering teams. Alert volume scales with product growth, but investigation capacity does not. Senior engineers absorb the overflow because newer engineers lack the systemic context to triage complex outages independently. Product velocity drops to zero during incident spikes, and SLA windows erode with every manual step.

Security operations teams pioneered a four-stage agentic pipeline to handle alert triage at scale. Struct adapted this proven approach for engineering on-call workflows, where the same investigation bottlenecks exist but the data sources differ.

2. Step-by-Step Agentic Triage Process

Stage 1: Intelligent Data Pipeline

Goal: Normalize heterogeneous alert data into a single enriched context before any analysis begins. This unified view prevents engineers from stitching together partial signals across tools.

Inputs: The pipeline pulls raw alerts from monitoring tools, log streams, exception trackers, and ticketing systems. These feeds provide the raw material for enrichment.

Outputs: Struct converts these inputs into a unified, deduplicated event record with metadata attached. That record becomes the foundation for every downstream investigation step.

Trade-offs: Pipeline quality depends entirely on the maturity of upstream instrumentation. Teams with sparse logging or missing trace IDs receive proportionally thinner context, which limits the effectiveness of later investigation stages.

Stage 2: Dynamic AI Triage Plans

Goal: Generate a per-alert investigation plan instead of executing a fixed static runbook. Each incident receives a plan tailored to its specific signals and affected systems.

Inputs: The AI uses the enriched alert record, historical incident patterns, and any custom runbook instructions provided by the team. These inputs shape the sequence of checks.

Outputs: Struct produces a prioritized sequence of queries and checks that match the alert type and impacted services. This plan defines exactly what the automated investigation will do next.

Trade-offs: Dynamic plans require the AI to have sufficient system context. Teams that encode their on-call runbooks explicitly generate more accurate plans than teams relying solely on inferred context.

Stage 3: Automated Investigation

Goal: Execute the triage plan autonomously without human prompting so engineers receive results instead of raw data.

Inputs: The system consumes the dynamic plan, live log queries, observability metrics, and code context. These sources allow Struct to trace issues across services and time.

Outputs: Struct returns correlated findings, a unified timeline, and a candidate root cause with supporting evidence. Engineers see a coherent narrative instead of scattered charts.

Trade-offs: Fully automated execution introduces the risk of querying stale or incomplete data sources. Clear logging of every query made during investigation is essential for engineer trust and post-incident review.

Stage 4: Transparent Verdicts

Goal: Deliver a human-readable conclusion with a full evidence chain so engineers can verify, escalate, or dismiss in one step.

Inputs: The verdict engine consumes the correlated investigation findings from Stage 3. It then synthesizes these into a concise explanation.

Outputs: Struct produces a structured verdict containing root cause, blast radius, suggested fix, and all supporting queries. Engineers can scan the summary and drill into details when needed.

Trade-offs: Verdict quality is only as strong as the investigation that precedes it. Black-box outputs without evidence chains erode engineer trust and increase the likelihood of incorrect dismissals.

3. Embedding the Pipeline in Engineering Workflows

These four stages form a conceptual framework, and their practical value depends on how they integrate with existing engineering workflows. Security teams route alerts through SIEM platforms, while engineering teams route alerts through Slack channels and ticketing systems like Linear or Jira. The data sources differ, with AWS CloudWatch, Grafana, Prometheus, and GitHub replacing SIEM feeds, but the pipeline logic remains identical.

Struct implements this pipeline natively for engineering operations. When an alert fires in a configured Slack channel, Struct automatically initiates Stage 1 by pulling logs, metrics, and traces from connected observability platforms. It generates a dynamic investigation plan in Stage 2 using any custom runbook instructions the team has encoded.

Stage 3 then runs autonomously, correlating trace and correlation IDs, mapping a unified timeline across the full stack, and identifying the likely root cause. Stage 4 surfaces a structured verdict with a dynamically generated dashboard directly in Slack, often before the on-call engineer has opened their laptop.

4. Measuring Impact and Improving Over Time

Struct reduces active triage time by 80%, compressing the half-hour investigation ritual described earlier into 5–10 minute reviews. These performance gains are measurable, and the automated verdict provides the correct root cause and actionable next steps in the overwhelming majority of cases. Setup time stays under 10 minutes for teams with Slack, GitHub, and a cloud observability stack already in place.

Improvement compounds over time as teams encode more runbook specificity. Custom correlation ID formats, alert-type-specific widget configurations, and proprietary investigation sequences all increase verdict accuracy without requiring engineering effort beyond initial configuration.

5. Common Pitfalls and Best Practices

These performance gains assume a baseline level of operational maturity. Teams evaluating Struct should confirm they meet the following prerequisites before expecting an 80% reduction in triage time:

• Minimum tooling maturity: Struct requires functional logging, trace IDs, and active alerting triggers because the AI’s investigation depends on runtime observability data to determine system state. Teams without this basic instrumentation will not benefit from automated investigation, since the AI cannot infer what happened in production by analyzing code alone.
• Rollout effort: The 10-minute setup applies to teams already using Slack, a cloud observability platform, and GitHub. Teams migrating from email-based alerting or legacy ticketing systems should budget additional time for alert routing configuration.
• Compliance: Struct is SOC 2 and HIPAA compliant, and logs are accessed and processed ephemerally. Teams with strict VPC egress restrictions that prohibit any log data leaving internal infrastructure are not currently a fit.
• Junior engineer participation: Struct’s automated first-pass investigation provides new engineers with a fully contextualized starting point for every alert. This support makes it safe to include them in on-call rotations without requiring deep systemic knowledge upfront.

Book a demo to see Struct’s automated investigation in action with your own alerts.

Security Operations vs. Struct: Side-by-Side Automation Comparison

The table below maps each pipeline stage across both domains, highlighting how Struct translates security operations principles into engineering-specific tooling and workflows.

Pipeline Stage	Security Operations (SOC)	Struct (Engineering On-Call)	Key Differentiator
Intelligent Data Pipeline	Normalizes SIEM alerts and threat feeds	Ingests Slack/PagerDuty alerts and pulls logs from Datadog, CloudWatch, GCP, Azure, and Grafana	Slack-native trigger with no separate alert console required
Dynamic AI Triage Plans	Generates per-alert SOC investigation plan	Generates per-alert engineering investigation plan using custom runbook instructions	Teams encode proprietary runbooks directly with composable widget configuration per alert type
Automated Investigation	Queries threat intelligence and endpoint data autonomously	Queries observability platforms and GitHub autonomously and correlates trace IDs and timelines	Proactive execution before engineer engagement with a typical 5–10 minute completion window
Transparent Verdicts	Human-readable verdict with evidence chain for SOC analyst review	Dynamically generated dashboard in Slack with root cause, blast radius, suggested fix, and all supporting queries	Sub-10-minute setup, roughly 4x faster triage, and about 9-in-10 investigations rated helpful

Frequently Asked Questions

What minimum tooling maturity does a team need before Struct delivers value?

A team needs three things in place: an active alerting channel such as Slack or PagerDuty, at least one observability platform producing logs or metrics such as Datadog, AWS CloudWatch, GCP Logs, or Grafana, and a GitHub repository containing the relevant service code. Teams already using Sentry for exception tracking see additional investigation depth. If basic logging and trace IDs are not present in the system, Struct cannot infer system state from code analysis alone, and investigation quality will be limited.

How long does rollout actually take, and does it require dedicated engineering time?

As noted earlier, setup takes under 10 minutes for teams already using Slack, a cloud observability platform, and GitHub. The process involves authenticating the alert source, connecting the code repository, and linking the observability stack. No dedicated engineering sprint or professional services engagement is required. Automated investigations trigger as soon as Struct begins receiving alerts. Teams migrating from legacy alerting setups such as email-based notifications or on-premise ticketing systems should budget additional time for alert routing reconfiguration before Struct can intercept issues automatically.

What compliance certifications does Struct hold, and how is log data handled?

As mentioned in the Common Pitfalls section, Struct holds SOC 2 and HIPAA certifications. The ephemeral processing model means log data is never stored persistently, and Struct accesses it only during active investigations before discarding it. This posture covers the requirements of the vast majority of Seed-to-Series-C companies, including fintech and healthtech teams operating under strict data handling mandates. Teams with enterprise-grade VPC egress restrictions that prohibit any data leaving internal infrastructure are not currently a fit, because Struct requires access to external log sources via its integrations to function.

Can junior engineers safely manage on-call shifts using Struct?

Yes. Struct’s automated first-pass investigation provides every on-call engineer, regardless of tenure, with a fully contextualized starting point before they engage with the alert. The dynamically generated dashboard includes the blast radius, root cause, a unified timeline, and suggested fixes. Junior engineers can review this output, ask follow-up questions via the Slack-native conversational interface, and make informed escalation decisions without needing the tribal knowledge that senior engineers accumulate over years. This approach addresses the common bottleneck where only senior engineers can safely triage complex outages and accelerates on-call onboarding for new hires.

Conclusion: Automate Your On-Call Runbook

Security operations’ four-stage agentic pipeline of Intelligent Data Pipeline, Dynamic AI Triage Plans, Automated Investigation, and Transparent Verdicts shows that structured AI automation can eliminate the manual investigation burden that degrades analyst and engineer productivity alike. The architectural principles are not domain-specific. They apply directly to engineering on-call operations, where the cost of manual triage is measured in SLA breaches, senior engineer burnout, and product velocity lost to firefighting.

Struct implements this pipeline for software reliability teams with a 10-minute setup, Slack-native delivery, SOC 2 and HIPAA compliance, and an 80% reduction in triage time. In many cases, the investigation is complete before the engineer opens their laptop.

Automate your on-call runbook and let Struct handle the next alert end-to-end.