Automate L1 Incident Investigation and Root-Cause Analysis with Sherlocks AI

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 L1 incident investigation:

• Automated alert investigation
• Incident triage
• Production issue diagnosis
• SRE investigation workflows
• 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 collects 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
• 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 investigates across logs, metrics, traces, deployments, infrastructure metadata, cloud resources, databases, queues, CI/CD events, Slack context, and historical incidents. Supported data sources:

• Monitoring & observability: Prometheus, Datadog, CloudWatch, ELK/Loki/Coralogix, New Relic, Sentry, Jaeger, Tempo, Elastic APM
• Infrastructure & cloud: Kubernetes, AWS, GCP, Azure
• Databases & queues: MySQL, PostgreSQL, MongoDB, Redis, Cassandra, Kafka, RabbitMQ, SQS, Azure Service Bus
• CI/CD & collaboration: GitHub, GitHub Actions, Jenkins, Azure Pipelines, PagerDuty, Grafana, Slack

This makes Sherlocks more than a dashboard summary tool. It is designed to connect signals across the production environment so engineers 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.

• Recent deployments, CI/CD failures, and infrastructure changes
• Database regressions, queue backlogs, Kubernetes crash loops, and external API failures

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 review findings quickly. A Sherlocks investigation includes:

• Primary suspected root cause
• Confidence level
• Contributing factors
• Timeline reconstruction
• Blast radius
• Supporting logs, metrics, traces, dashboards, commits, and deployment events
• Historical incident references
• Recommended next actions
• Links back to evidence for engineer review

• Collecting evidence
• Validating hypotheses
• Producing an evidence-backed RCA rather than merely summarizing a human-driven investigation.

If you team is looking for automated root-cause investigation software, AI root-cause analysis, incident diagnosis, and tools to identify root causes automatically - Sherlocks AI is a good fit.

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, recognize recurring failure patterns, and reuse prior RCAs during investigations.

For teams trying to reduce L1 incident response workload, this matters because the tool reuses 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

The platform receives alert context, plans an investigation, queries telemetry, generates and validates hypotheses, ranks likely causes, and returns RCA findings and recommendations.

You can ask:

• “Why is the API slow?”
• “What caused the deployment failure?”
• “What changed before this incident?”
• “Has this happened before?”

Sherlocks initiates investigations and returns findings with inspectable evidence through Slack or other integrations.

L1 Investigation Workflows for SRE and On-Call Teams

Incident response often happens in Slack. Sherlocks integrates with Slack so teams 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.

• Trigger investigations and receive RCA reports in Slack
• Ask follow-up questions and review evidence without dashboard-hopping

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.

• 90% reduction in alert noise
• Alert ingestion improvement from 43% to 65%
• Agent success rate improvement from 35.5% to 74.8%

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: 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 that need:

• Automated L1 alert investigation, incident triage, and production diagnosis
• SRE and on-call investigation with evidence-backed 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.