How autonomous AI agents are replacing manual incident investigation for SRE teams.
Your on-call engineer gets paged at 3 AM.
They open their laptop. Check PagerDuty. Open CloudWatch. Switch to kubectl. Open Grafana. Check the deployment history in GitHub.
Search Slack for context from the last time this happened.
45 minutes later, they've found the root cause: a misconfigured environment variable in the latest deployment broke the database connection string.
The investigation itself was the bottleneck — not the fix.
This is the reality for most SRE teams. And it's the problem agentic incident management was built to solve.
So What Exactly is Agentic Incident Management?
Agentic incident management is an approach where autonomous AI agents investigate, diagnose, and help resolve cloud infrastructure incidents without step-by-step human direction.
Unlike traditional runbook automation that follows predefined scripts, agentic systems use large language models (LLMs) to dynamically decide which tools to use, what data to gather, and how to synthesize findings into actionable root cause analyses.
The key word is autonomous. The AI doesn't wait for instructions. It investigates.
How It's Different from What You're Using Now
Most incident management tools today — Rootly, FireHydrant, incident.io — focus on workflow automation. They're excellent at:
- Creating a Slack channel when an incident fires
- Paging the right on-call engineer
- Running predefined runbooks
- Generating status page updates
But they don't investigate the incident. A human still has to do that.
Agentic incident management automates the investigation itself:
Traditional approach:
- Response: Human receives alert, starts manual investigation
- Tool usage: Engineer manually queries each system
- Knowledge: Depends on who's on call
- Speed: 30–60 minutes for initial diagnosis
- Documentation: Written after resolution (often days later)
Agentic approach:
- Response: AI agent automatically triggered by webhook
- Tool usage: Agent dynamically selects and chains 30+ tools
- Knowledge: Searches entire knowledge base via RAG
- Speed: Minutes for comprehensive analysis
- Documentation: Auto-generated postmortem during investigation
How It Actually Works
Here's the workflow when a monitoring tool fires an alert:
Alert ingestion → A webhook from PagerDuty, Datadog, or Grafana triggers the AI agent.
Dynamic tool selection → The agent evaluates the alert context and autonomously selects from 30+ tools — querying Kubernetes clusters, running cloud CLI commands, searching logs, checking recent deployments.
Multi-step investigation → The agent conducts multi-step reasoning. It might check pod status in Kubernetes, trace the issue to a misconfigured deployment, then verify by examining the Terraform state.
Knowledge base search → Vector search (RAG) over your organization's runbooks, past postmortems, and documentation surfaces relevant historical context.
Root cause synthesis → The agent synthesizes findings into a structured root cause analysis with timeline, impact assessment, and remediation recommendations.
Postmortem generation → A detailed postmortem is automatically generated and can be exported to Confluence.
No human had to initiate any of these steps.
Why This Matters Now
Three trends are making manual incident investigation unsustainable:
Alert fatigue is real. SRE teams handle hundreds of alerts daily.
Most are noise, but each one requires triage. Agentic systems handle this automatically, escalating only when human judgment is needed.
Multi-cloud is the norm. Organizations use 3+ cloud providers on average.
Correlating incidents across AWS, Azure, and GCP manually — with different CLIs, different consoles, different authentication — doesn't scale.
Knowledge walks out the door. When your most experienced SRE goes on vacation, their investigation knowledge goes with them. Agentic systems with knowledge base RAG always have access to your team's collective expertise.
According to Gartner, by 2026, 30% of enterprises will adopt AI-augmented practices in IT service management — up from less than 5% in 2023.
What About Limitations?
Agentic incident management is powerful but not a silver bullet:
- Complex systemic issues still require human judgment — AI agents excel at data gathering and correlation but may miss organizational or process-level root causes
- Initial setup requires configuring cloud connectors, knowledge base ingestion, and permissions
- LLM costs scale with investigation depth, though local models can mitigate this
- Nascent ecosystem — best practices are still emerging
The goal isn't to replace on-call engineers. It's to give them a head start. When a human opens their laptop at 3 AM, the AI has already gathered the context, correlated the data, and narrowed down the root cause.
We Built an Open Source Version
We built Aurora because we believe incident investigation tooling should be transparent, self-hosted, and free.
Aurora is an open-source (Apache 2.0) agentic incident management platform that uses LangGraph-orchestrated LLM agents to investigate incidents across AWS, Azure, GCP, OVH, Scaleway, and Kubernetes.
What makes it different:
- Open source — audit every line of code the AI runs on your infrastructure
- Self-hosted — your incident data never leaves your environment
- Any LLM — OpenAI, Anthropic, Google, or local models via Ollama
- 22+ integrations — PagerDuty, Datadog, Grafana, Slack, GitHub, Confluence
- Free — no per-seat or per-incident pricing
Get started in 3 commands:
git clone https://github.com/Arvo-AI/aurora.git
cd aurora
make init && make prod-prebuilt
Originally published at https://www.arvoai.ca/blog/what-is-agentic-incident-management
