What is Agentic Incident Management?

What is Agentic Incident Management?

Key Takeaway: Agentic incident management uses autonomous AI agents to investigate incidents in minutes instead of hours. Unlike workflow automation tools that orchestrate humans, agentic systems autonomously query infrastructure, correlate data across clouds, and deliver root cause analyses — reducing MTTR by up to 80%.

Agentic incident management is an approach to IT operations where autonomous AI agents investigate, diagnose, and help resolve cloud infrastructure incidents without requiring step-by-step human direction. Unlike traditional runbook automation that follows predefined scripts, agentic systems use large language models (LLMs) orchestrated by frameworks like LangGraph to dynamically decide which tools to use, what data to gather, and how to synthesize findings into actionable root cause analyses.

How Agentic Incident Management Works

Traditional incident management relies on on-call engineers receiving an alert, manually querying logs, checking dashboards, and correlating data across systems. This process is slow, error-prone, and depends heavily on tribal knowledge.

Agentic incident management fundamentally changes this workflow:

1. Alert Ingestion: When a monitoring tool like PagerDuty, Datadog, or Grafana fires an alert, a webhook triggers the AI agent to begin investigation automatically.

2. Dynamic Tool Selection: The agent evaluates the alert context and autonomously selects from 30+ available tools — querying Kubernetes clusters, running cloud CLI commands, searching logs, checking recent deployments, and analyzing metrics.

3. Multi-Step Investigation: Unlike simple automation, the agent conducts multi-step reasoning. It might start by checking pod status in Kubernetes, then trace the issue to a misconfigured deployment, then verify by examining the Terraform state.

4. Knowledge Base Search: The agent searches your organization's runbooks, past postmortems, and documentation using vector search (RAG) to find relevant historical context.

5. Root Cause Synthesis: After gathering evidence from multiple sources, the agent synthesizes its findings into a structured root cause analysis with timeline, impact assessment, and remediation recommendations.

6. Postmortem Generation: The agent automatically generates a detailed postmortem document that can be exported to Confluence or other documentation platforms.

Traditional vs. Agentic Incident Management

Aspect	Traditional	Agentic
Response initiation	Human receives alert, begins manual investigation	AI agent automatically triggered by webhook
Tool usage	Engineer manually queries each system	Agent dynamically selects and chains tools
Knowledge access	Depends on on-call engineer's experience	Searches entire knowledge base via RAG
Investigation speed	30-60 minutes for initial diagnosis	Minutes for comprehensive analysis
Cross-system correlation	Manual, error-prone	Automatic correlation across clouds and tools
Documentation	Written after resolution (often delayed)	Auto-generated postmortem during investigation
Scalability	Limited by team size and expertise	Handles multiple incidents concurrently

Key Capabilities of Agentic Systems

Webhook-Triggered Auto-Investigation

When an alert fires from PagerDuty, Datadog, Grafana, or other monitoring tools, the agentic system automatically begins a background investigation. There's no need for a human to initiate the process — the system starts gathering context the moment the incident is detected.

Multi-Cloud CLI Execution

Agentic systems can execute commands across AWS, Azure, GCP, and Kubernetes in sandboxed environments. This means the AI agent can actually run kubectl describe pod, aws cloudwatch get-metric-data, or az monitor metrics list to gather real infrastructure data, not just query APIs.

Infrastructure Knowledge Graph

By building a live dependency graph of your infrastructure (typically using graph databases like Memgraph), agentic systems understand how your services relate to each other. When a database goes down, the system automatically identifies all dependent services and assesses blast radius.

Knowledge Base RAG

Vector search over your organization's runbooks, past incident reports, and documentation means the AI agent has access to your team's collective knowledge. It can find that a similar CPU spike was caused by a memory leak in the payment service three months ago.

Automatic Postmortem Generation

Instead of spending hours writing postmortems after resolution, agentic systems generate structured postmortem documents in real-time, including timeline, root cause, impact, and remediation steps. These can be exported directly to Confluence or other documentation tools.

Why It Matters for SREs

"The biggest challenge in incident management isn't the technology — it's that investigation knowledge walks out the door when your senior SRE goes on vacation. Agentic systems capture and operationalize that knowledge." — Noah Casarotto-Dinning, CEO at Arvo AI

According to Gartner, by 2026, 30% of enterprises will have adopted AI-augmented practices in IT service management, up from less than 5% in 2023. The shift to agentic incident management addresses several critical pain points:

• Alert fatigue: SRE teams handle hundreds of alerts daily. Agents can triage and investigate automatically, escalating only when human judgment is needed.
• Knowledge silos: When your most experienced engineer is on vacation, the AI agent still has access to the full knowledge base.
• Mean Time to Resolution (MTTR): Automated investigation dramatically reduces the time between alert and diagnosis.
• Toil reduction: Repetitive investigation tasks are automated, freeing SREs to focus on systemic improvements.
• Multi-cloud complexity: With organizations using 3+ cloud providers on average, correlating incidents across clouds manually is increasingly impractical.

Limitations to Consider

Agentic incident management is powerful but not a silver bullet. Current limitations include:

• 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 tool permissions.
• LLM costs scale with investigation depth — complex incidents may require many API calls, though local models via Ollama can mitigate this.
• Nascent ecosystem — agentic incident management is a new category, and best practices are still emerging.

Getting Started with Aurora

Aurora is an open-source (Apache 2.0) agentic incident management platform built with Python and Next.js. It uses LangGraph-orchestrated LLM agents with 30+ tools to investigate incidents across AWS, Azure, GCP, OVH, Scaleway, and Kubernetes.

To get started:

git clone https://github.com/Arvo-AI/aurora.git
cd aurora
make init
make prod-prebuilt

Aurora integrates with PagerDuty, Datadog, Grafana, Slack, GitHub, Confluence, and 15+ other tools. It supports any LLM provider including OpenAI, Anthropic, Google, and local models via Ollama.

Learn more at arvoai.ca or read the full documentation. You can also explore how Aurora compares to traditional tools in our Aurora vs Traditional Incident Management Tools comparison.