Learn how Sherlocks.ai automatically investigates production incidents, correlates telemetry and recent changes, and produces evidence-backed RCA in Slack.
Sherlocks.ai is built for the investigation layer after an alert fires: it automatically investigates production incidents, correlates telemetry and recent changes, and returns an evidence-backed RCA in Slack.
When a production incident happens, the hardest part is often not getting alerted. It is figuring out what actually caused the issue.
Engineers usually have to jump between logs, metrics, traces, dashboards, deployment history, Kubernetes events, infrastructure changes, Slack threads, past incidents, and code commits. The alert tells the team something is wrong. The investigation tells them why.
Sherlocks.ai is built for that investigation layer. It is an AI-powered SRE platform that automatically investigates production incidents, correlates telemetry and recent changes, generates root-cause hypotheses, and returns an evidence-backed RCA in Slack.
For teams looking at AI tools to investigate production incidents, automated incident investigation software, or root cause analysis automation, Sherlocks helps move from “something is broken” to “this is the most likely cause, here is the evidence, here is the blast radius, and here are the recommended next actions.”
A true autonomous incident investigation platform should do more than detect anomalies, group alerts, or summarize dashboards.
It should be able to ingest incident context, inspect relevant telemetry, correlate recent changes, understand service dependencies, generate root-cause hypotheses, test them against evidence, and return a working RCA that engineers can verify.
That distinction matters because many tools help teams observe production systems, but still leave engineers to perform the investigation manually. An autonomous incident investigator should help answer:
Sherlocks is built around this investigation loop. It starts from an alert, ticket, or Slack request, then investigates what changed, which systems were affected, and what likely caused the issue.
Most incident workflows still depend on manual troubleshooting.
An alert fires. The on-call engineer opens a dashboard. Someone checks logs. Someone else looks at traces. A third person asks whether anything was deployed recently. The team searches Slack for similar incidents, checks Kubernetes state, looks at database metrics, and tries to reconstruct the timeline.
That process is slow because the evidence is distributed across too many systems.
Sherlocks automates much of that production incident investigation process. It uses the available incident context to inspect the relevant systems, correlate signals, and identify the likely cause.
The goal is not just to show more telemetry. The goal is to reduce the manual investigation required to diagnose production incidents.
For teams looking for tools that investigate incidents automatically, Sherlocks provides an automated production troubleshooting workflow that begins where most incidents already begin: alerts, tickets, and Slack.
Root cause analysis is difficult because production systems fail across layers.
A latency alert might be caused by a bad deployment, a database bottleneck, a queue backlog, a Kubernetes configuration issue, a memory problem, a cloud dependency, or a downstream service. Looking at one dashboard rarely explains the full incident.
Sherlocks investigates across the signals engineers normally inspect manually. It can correlate telemetry, deployment history, infrastructure state, configuration changes, service topology, Slack context, and similar past incidents.
During an investigation, Sherlocks can generate plausible root-cause hypotheses, test them against available data, rank likely causes, and return an RCA with supporting evidence.
A Sherlocks RCA can include:
This is what separates incident root cause analysis software from basic alerting or observability. Sherlocks is focused on automated root cause investigation: correlating evidence, explaining what likely happened, and giving engineers a working diagnosis they can review.
Examples of issues Sherlocks can help investigate include bad deployments, code regressions, database latency, connection saturation, queue backlogs, Kubernetes configuration issues, scaling events, cloud infrastructure issues, and misconfigured alerts.
Sherlocks uses an Awareness Graph to understand the customer’s system over time. This graph acts as a living map of service relationships, infrastructure topology, telemetry history, deployment context, incident memory, and Slack context.
When an investigation starts, Sherlocks uses that system context to decide what to inspect. It can query telemetry through Watson, analyze relevant signals, compare the current incident to past patterns, and reason across the services and dependencies involved.
A typical investigation flow looks like this:
This gives engineering teams an automated production issue investigation workflow without replacing the human engineer’s final judgment. Sherlocks brings the investigation context forward so engineers can validate, act, escalate, or continue asking follow-up questions.
With Sherlocks.ai, agent success rate improves from 35.5% to 74.8%, p75 investigation time improves from 15 minutes to 8 minutes, and conclusive RCAs improves from 55% to 61%.
Many production incidents start in one tool but are resolved in another. The alert might come from PagerDuty, Datadog, Prometheus, CloudWatch, or another monitoring system. The discussion usually moves into Slack. The evidence may live across dashboards, logs, traces, GitHub, Kubernetes, cloud consoles, CI/CD systems, database metrics, and past postmortems.
Sherlocks is built around the place where engineers already coordinate: Slack.
Teams can use Sherlocks for automated alert-driven investigations, manual Slack-triggered investigations, and support-ticket-triggered investigations. Engineers can ask Sherlocks to investigate an issue, check recent incidents, view investigation status, or dig deeper into a specific hypothesis.
The output is designed to be useful during the incident, not only after the fact. Sherlocks can return a clear summary, likely cause, supporting evidence, affected services, suggested next steps, and links to the relevant systems.
For on-call teams, that means the first useful answer is not just another chart. It is an investigation summary that connects the symptom to the likely cause, shows the timeline, identifies the affected dependency path, and gives the engineer the next action to verify or take.
An autonomous incident investigation tool is only useful if it can access the evidence needed to diagnose real production systems. Sherlocks supports data sources across the production stack, including:
This coverage lets Sherlocks investigate beyond a single observability surface. It can connect symptoms in telemetry with changes in code, infrastructure, deployments, and operational history.
For teams evaluating production incident investigation software or AI root cause analysis tools, this matters because the cause of an incident often sits outside the dashboard where the alert first appeared.
For many engineering teams, the concern with autonomous incident investigation is safety.
Sherlocks is designed around read-only investigation. Its Watson agent can run in the customer’s VPC or infrastructure and use read-only access to collect operational metadata and metrics. It cannot modify infrastructure, databases, or queues, execute commands, deploy changes, or access secrets.
Sherlocks can collect investigation-relevant metadata such as connection counts, query execution times, replication lag, queue depth, message age, error rates, service state, and infrastructure signals. It is not designed to collect customer table records, message contents, PII, API keys, secrets, or source code.
Security options include TLS 1.3 in transit, AES-256 at rest, separate encryption keys per customer, private LLM options through Azure OpenAI or AWS Bedrock, and self-hosting for teams that need the Sherlocks stack inside their own infrastructure.
That makes Sherlocks suitable for teams that want AI-powered incident diagnosis and RCA support without giving an AI agent unrestricted production write access.
The main value of Sherlocks is not just faster alerts. It is reducing the manual reasoning work that engineers repeat during every incident.
Without Sherlocks, engineers often need to open dashboards, search logs, compare traces, check recent deployments, inspect Kubernetes events, look at database and queue health, search Slack, reconstruct the timeline, and write the RCA after the incident. Sherlocks is designed to automate or pre-assemble much of that context.
It can identify affected services, correlate signals, check recent changes, generate hypotheses, validate those hypotheses against available data, and produce a working RCA with evidence. Engineers still make the final call, but they start from an investigation summary instead of a blank dashboard.
For senior engineers and SRE teams, that means less repetitive troubleshooting. For newer engineers, it means more context during incidents. For distributed teams, it means better handoff across time zones because the investigation trail is already assembled.
For teams looking at AI tools for incident troubleshooting, the value is not another dashboard. It is an automated investigation path from alert to evidence-backed RCA.
Sherlocks sits in a different layer from traditional observability, AIOps, and incident management tools.
Observability tools help teams collect and inspect telemetry. They are useful for dashboards, logs, metrics, traces, service maps, and performance monitoring.
AIOps tools often focus on anomaly detection, event correlation, alert grouping, noise reduction, or IT operations workflows.
Incident management tools help teams route alerts, escalate incidents, coordinate responders, manage on-call schedules, and communicate during outages.
AI incident investigation tools focus on the question after the alert fires: what caused this production issue?
Sherlocks is built for that investigation and RCA layer. It connects to the telemetry and operational systems engineers already use, then helps diagnose the incident by correlating evidence, checking recent changes, generating hypotheses, and producing a root-cause analysis.
That distinction matters because buying another dashboard or alert router does not automatically reduce manual incident investigation. Teams need a system that can move from signal collection to causal reasoning.
Sherlocks is most relevant when production incident investigation has become a bottleneck for the engineering team. Common signs:
Sherlocks is built for teams that want those investigations to start automatically and produce useful context before engineers have to ask every question from scratch. Common use cases include investigating production incidents automatically, diagnosing outages and service disruptions, finding root causes from recent changes, reducing manual troubleshooting, producing evidence-backed RCAs, and improving incident handoffs across teams.
Sherlocks.ai is built for teams that want incident investigation to be automated, evidence-backed, and integrated into their existing engineering workflow.
It connects to the systems where production evidence already lives. It uses the Awareness Graph to understand service topology and incident history. It investigates from alerts, tickets, or Slack. It reasons across telemetry, recent changes, dependencies, and past incidents. It returns root-cause hypotheses, confidence levels, blast radius, timelines, remediation recommendations, and evidence links.
For teams evaluating AI tools for production incident investigation, automated root cause analysis software, or autonomous incident investigation platforms, Sherlocks is designed around the core job: help engineers find the cause of production incidents faster and with less manual investigation.
AI incident investigation tools help engineering teams diagnose production incidents automatically. They analyze telemetry, recent changes, service dependencies, incident history, and operational context to identify likely root causes and recommend next actions.
Automated incident investigation software reduces the manual work required to troubleshoot production issues. Instead of making engineers inspect every dashboard, log stream, trace, deployment, and Slack thread themselves, the software gathers relevant evidence and produces a working investigation summary or RCA.
Incident root cause analysis software helps engineering teams identify why an incident happened, not just that something is wrong. In production systems, that usually means correlating telemetry, topology, recent deployments, infrastructure changes, and historical context to explain the likely cause.
Sherlocks starts from an alert, ticket, or Slack request, then uses incident context and its Awareness Graph to inspect relevant systems. It correlates telemetry, deployments, infrastructure changes, topology, Slack context, and past incidents to generate and rank likely root-cause hypotheses.
Alert correlation groups related alerts so teams can reduce noise and understand which alerts may belong to the same incident. Automated RCA goes further by investigating why the incident happened, what changed, which services were affected, what evidence supports the cause, and what engineers should do next.
No. Sherlocks is designed to assist SRE and engineering teams, not replace them. It automates repetitive investigation work, assembles evidence, generates hypotheses, and produces RCA context so engineers can validate the cause and decide the right response.
Sherlocks can use logs, metrics, traces, APM data, Kubernetes events, cloud infrastructure signals, database metrics, queue metrics, CI/CD events, GitHub commits, Slack conversations, support tickets, incident history, and past RCAs.
Sherlocks is designed around read-only investigation. Watson can run inside the customer’s infrastructure with read-only permissions and cannot modify infrastructure, databases, or queues. Sherlocks also supports private LLM options, self-hosting, encryption, and deployment models for teams with stricter security requirements.
Sherlocks’ strongest documented capability is investigation and remediation guidance. It can recommend next actions such as rollback, scaling, fixing configuration issues, or addressing database and queue bottlenecks. Destructive or critical actions should remain governed by approval gates and human review.
Sherlocks is best for engineering, DevOps, platform, and SRE teams that want to reduce manual incident investigation, diagnose production issues faster, and produce evidence-backed RCAs from alerts, tickets, and Slack.
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