When critical alerts fire, your team shouldn't be burning time hopping between dashboards, reconstructing context, and guessing the safest next step. Our AI agents triage incidents, investigate root causes, and propose remediation — with explicit human approval in Slack before anything executes.
Teams scramble across dashboards, traces, and runbooks — all while the SLA window is already shrinking. Every minute of manual triage is a minute of lost revenue and eroding customer trust.
Autonomous incident response transforms a fragmented, manual workflow into a structured, semi-autonomous process with human oversight at every critical decision point.
Every step is traceable, auditable, and controlled. Your team stays informed and in command — the AI does the legwork, humans make the call.
Automate the slowest parts of investigation and decision support. Teams report 60–80% MTTR reduction by eliminating manual root-cause discovery and runbook lookups.
Engineers receive the likely cause, suggested action, and supporting evidence up front — not after 20 minutes of manual investigation. Less pressure, better decisions.
No remediation executes without explicit Slack approval. Allowlisted action types, rollback limits, approval expiration, and signature verification reduce operational risk at every layer.
Shorter triage and diagnosis cycles mean teams respond within service windows more consistently and dramatically reduce after-hours escalations.
Each agent is purpose-built for a distinct phase of incident response. Together, they form an end-to-end system that handles the cognitive load of triage and diagnosis — so your engineers can focus on decisions, not discovery.
Classifies incoming alerts, prioritizes severity, and orchestrates the full response flow. Eliminates the noisy, manual first step that wastes the most time during an incident.
Investigates root cause using real-time operational data — logs, metrics, traces — and surfaces the most likely explanation with supporting evidence before a human ever looks at a dashboard.
Proposes the safest next action — scale, restart, or rollback — and sends it to Slack for explicit human approval. Nothing runs in production without a human sign-off.
Most incident tools rely on static runbooks and rigid alert rules. This platform reasons with a foundation model — Claude Sonnet 4 via Amazon Bedrock — enabling it to handle novel failure modes that no runbook anticipated. It learns from operational context in real time, not from pre-written decision trees.
Built with extensibility as a first principle, the platform supports additional observability backends including Datadog and orchestration platforms like EKS. Your stack evolves, and so does your incident response tooling.
Every remediation action requires explicit Slack approval before execution. Guardrails include allowlisted action types, rollback limits, approval expiration windows, and cryptographic signature verification — making this the safest path to production automation.
The goal isn't to replace your SRE team. It's to move incident response from reactive firefighting to a structured operational loop — where humans stay in control and AI handles the time-consuming investigative work.
Every layer of the platform is built on trusted, production-grade infrastructure. No proprietary lock-in — just well-understood, auditable technology choices your platform team will recognize.
Amazon Bedrock AgentCore — managed, scalable, and enterprise-ready agent execution with built-in observability and IAM-native security controls.
Strands Agents — a structured multi-agent orchestration framework designed for reliable, traceable agentic workflows in operational environments.
Claude Sonnet 4 via Bedrock — state-of-the-art reasoning for complex, multi-step incident analysis. Deployed through Bedrock for compliance and governance alignment.
New Relic (with extensibility for Datadog and others) — connects to your existing telemetry stack for real-time metrics, logs, and trace ingestion during investigations.
AWS CDK in Python — fully infrastructure as code, version-controlled, and deployable into your existing AWS environment without custom provisioning scripts.
Up to 60–80% reduction in mean time to resolution by automating investigation and decision support
Zero remediation actions execute in production without explicit human approval — every change is approved and audited
Five of the most common production failure patterns handled out of the box — from deploy errors to latency regressions
Triage, Diagnosis, and Remediation — each purpose-built for a distinct phase of incident response
Give your on-call team an AI system that investigates production issues in real time, recommends the next best action, and executes approved remediation safely. Reduce MTTR, lower alert fatigue, and build a more resilient operations practice — without removing humans from the equation.
Start with a pilot in your own environment. See live incident triage, diagnosis, and approval-driven remediation in action — using your alerts, your infrastructure, and your approval workflows.
Deploy into your environment and validate real MTTR reduction against your own incidents within weeks — not quarters.
See the full triage-to-remediation workflow live, with real incident scenarios and Slack approval in action. No commitment required.
AI Agents That Cut Incident Response Time — Without Taking Humans Out of the Loop