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SRE Resources · 2026-06-17 · 10 min read

Automate L1 Incident Investigation and Root-Cause Analysis with Sherlocks.ai

Automate alert triage, production incident investigation, and root-cause analysis across logs, metrics, traces, deployments, infrastructure, Slack, and historical incidents.

Sherlocks Team

L1 incident investigation typically begins with an alert and quickly becomes manual triage across logs, metrics, traces, deployments, infrastructure, Slack threads, and past incidents. Tools that automate L1 investigation reduce that manual work by collecting operational context, correlating signals, triaging alerts, generating root-cause hypotheses, and producing evidence-backed RCAs before an engineer needs to dashboard-hop.

Sherlocks.ai is built for this workflow: automated alert investigation, incident triage, production issue diagnosis, SRE investigation workflows, and root-cause analysis across telemetry, infrastructure, deployment, and historical incident context.

Automated Alert Investigation and L1 Incident Triage

Many incident workflows begin with alerts, but alerts rarely contain enough context to explain what happened.

Sherlocks supports alert-driven investigations from sources such as Grafana, PagerDuty, and Slack. Once an alert comes in, Sherlocks can collect surrounding context, correlate it with topology and dependency data, compare it with historical incident patterns, and investigate whether the alert points to a real production issue, a known false-positive pattern, or a broader incident.

For L1 and on-call teams, this helps automate the first layer of incident triage: understanding whether an alert is urgent, which service or dependency is involved, whether the issue is isolated or part of a larger incident, and what evidence should be reviewed before escalation.

Automated Incident Context Collection and Correlation

Manual L1 investigation is slow because incident context is scattered across observability tools, Kubernetes, cloud infrastructure, databases, queues, CI/CD systems, incident channels, and historical RCA notes.

Sherlocks uses Watson and its Awareness Graph to bring this context together during investigations. It can investigate across logs, metrics, traces, deployments, infrastructure metadata, cloud resources, databases, queues, CI/CD events, Slack context, and historical incidents. Supported data sources:

This makes Sherlocks more than a dashboard summary tool. It is designed to connect signals across the production environment so engineers can see how alerts, telemetry, dependencies, deployments, and historical context relate to one another.

Production Incident Investigation Across Changes and Dependencies

Production incidents often stem from recent changes or failing dependencies. A latency spike may be connected to a recent deployment, a database query regression, a queue backlog, a Kubernetes crash loop, an external API issue, or a pattern that appeared in a previous incident. A useful incident investigation tool needs to connect those events into a likely explanation.

Sherlocks correlates operational signals across logs, metrics, traces, deployments, infrastructure metadata, cloud resources, database and queue health, Kubernetes topology, Slack conversations, historical incidents, and CI/CD events.

It also correlates deployments, CI/CD failures, GitHub commits, pipeline executions, and infrastructure changes with incident timelines. For SRE and on-call workflows, this helps answer one of the fastest diagnostic questions: what changed before this broke?

Sherlocks’ Awareness Graph also contains service maps, infrastructure relationships, database dependencies, queue dependencies, Kubernetes topology, cloud resources, and deployment relationships. This lets Sherlocks use dependency context during investigations instead of analyzing each service in isolation.

Automated Root-Cause Investigation for L1 Incidents

Sherlocks generates hypotheses, tests them against available evidence, ranks likely causes by likelihood and impact, and produces an RCA summary with supporting context so engineers can review findings quickly. A Sherlocks investigation can include:

Crucially, Sherlocks is designed to perform much of the investigation workflow before human involvement by collecting evidence, validating hypotheses, and producing an evidence-backed RCA rather than merely summarizing a human-driven investigation.

This makes Sherlocks relevant for teams looking for automated root-cause investigation software, AI root-cause analysis, incident diagnosis, and tools to identify root causes automatically.

Historical Incident Memory for Faster L1 Investigation

L1 investigations often repeat work the team has already done. Relevant knowledge is frequently buried in Slack, postmortems, dashboards, or a senior engineer's memory.

Sherlocks stores historical incidents, previous RCAs, deployment history, documentation, Slack conversations, service relationships, and prior remediation patterns in its Awareness Graph. This incident memory lets Sherlocks compare current symptoms with past incidents, retrieve relevant context, recognize recurring failure patterns, and reuse prior RCAs during investigations.

For teams trying to reduce L1 incident response workload, this matters because the tool can reuse institutional knowledge instead of forcing every on-call engineer to rediscover the same context manually.

Sherlocks as an AI Incident Investigation Agent

Many teams search for AI agents, AI copilots, or autonomous incident investigation platforms because they want more than another dashboard.

Sherlocks is an autonomous investigator rather than a passive copilot. Its workflow can receive alert context, plan an investigation, query telemetry, generate and validate hypotheses, rank likely causes, and return RCA findings and recommendations.

L1 Investigation Workflows for SRE and On-Call Teams

Incident response often happens in Slack. Sherlocks integrates with Slack so teams can trigger investigations, receive RCA reports, ask follow-up questions, and review findings without opening multiple dashboards first.

For SRE, DevOps, platform, and IT operations teams that already have observability and incident response tools, Sherlocks automates the initial evidence collection and correlation that otherwise falls to humans.

Alert Noise Reduction for L1 Incident Workloads

L1 teams often spend too much time on noisy, duplicate, or low-context alerts. Sherlocks reduces alert fatigue through alert classification, contextual investigations, anomaly identification, topology-aware triage, and learning from historical false-positive patterns stored in the Awareness Graph.

Security and Deployment Controls

Incident investigation tools require broad read access, so deployment and security controls matter. Sherlocks supports SaaS, hybrid, and fully self-hosted in-VPC deployments and integrates with private LLM providers such as Azure OpenAI, Anthropic Claude, AWS Bedrock, and self-hosted models.

The platform uses a least-privilege, read-only architecture: Watson has no infrastructure modification rights, no database or queue writes, and no command execution. Security controls include TLS 1.3 in transit, AES-256 at rest, customer-specific keys, configurable retention policies, and data deletion support.

For stricter environments, the private deployment and read-only architecture are important because L1 investigation automation requires access to sensitive operational context without granting broad production control.

Where Sherlocks Fits Best

Sherlocks is best suited for teams looking to automate L1 incident investigation, alert investigation, incident triage, SRE incident investigation, on-call investigation, production issue diagnosis, and automated root-cause analysis.

Its strength is as an autonomous incident investigator and RCA engine focused on evidence-backed investigation, triage, recommendations, and incident memory rather than unrestricted auto-remediation.

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