Accelerating Incident Response with Multi-Agent AI
New model for modern operations
Operations teams are overwhelmed by rising alert volumes and manual triage processes, leading to delayed response times, inconsistent investigations, higher costs, and engineer burnout. Booz Allen addressed this by deploying a multi-agent AI system that automates incident triage and investigation in real time, delivering consolidated findings and recommended actions before engineers even begin their review. This resulted in faster response and resolution; reduced cogitative load for engineers; improved system availability; consistent and repeatable workflows; and better workforce allocation.
Challenge
Operations teams across large digital environments face increasing pressure as alert volumes and ticket queues grow beyond human capacity. High-frequency, repetitive incidents often overwhelm engineers, leading to delayed triage, mis-prioritized work, and widespread alert fatigue. Because traditional incident management depends heavily on manual investigation, teams lose valuable time performing the same diagnostic steps repeatedly, while expertise remains fragmented across siloed groups.
This reactive posture extends downtime, increases operational cost, and diverts highly skilled engineers away from complex problem solving toward routine, low-value investigative tasks. The result is a cycle of prolonged outages, inconsistent triage quality, and burnout within engineering teams.
Solution
To break this cycle, the Booz Allen team implemented a multi-agent AI solution built on Amazon Web Services (AWS) Bedrock, LangGraph, and Anthropic Claude 3.7 Sonnet to automate and optimize incident triage. The architecture centers on a supervisor agent that evaluates each new ticket and orchestrates a set of specialized worker agents, including contextualization, observability, network investigation, and evaluation agents. Each agent applies structured reasoning methodologies to its assigned task, producing consistent and comprehensive investigative outputs.
An event-driven design automatically launches agents the moment a ticket is created, enabling parallel background analysis without waiting for human intervention. Model Context Protocol (MCP) servers connect agents to external systems, ensuring deep diagnostic visibility and broad data correlation. By the time an engineer receives a ticket, the system has already performed the initial investigation, delivering consolidated findings, recommended next steps, and relevant evidence.
The Booz Allen team implemented a multi-agent AI solution built on AWS Bedrock, LangGraph, and Anthropic Claude 3.7 Sonnet to automate and optimize incident triage.
Impact
The AI-driven triage solution significantly accelerates response times by eliminating delays between ticket creation and initial investigation, improving system availability, and reducing mean time to resolution. Engineers experience lower cognitive load as alerts are enriched with contextual insights, system changes, and correlated telemetry, making prioritization more intuitive and informed. Automated, repeatable workflows ensure every incident is handled consistently, reducing variability and raising the overall quality of triage operations. Most importantly, the approach enables more strategic allocation of engineering talent: routine diagnostics are automated, allowing human experts to focus on complex incidents that require deeper problem solving. As a result, organizations achieve faster recovery, higher uptime, and a more resilient operations model powered by multi-agent AI.
Summary
Faster Response and Resolution
- Instant triage initiation as agents launch automatically at ticket creation
- Significantly reduced time to investigate due to automated diagnostics
- Faster time to resolution through pre-populated findings and next steps
Reduced Cognitive Load for Engineers
- Alerts enriched with context, correlated system insights, and relevant data
- Engineers receive action-ready tickets, not raw alerts
- Lower burnout risk by eliminating repetitive, low-value diagnostic work
Improved System Availability
- Faster triage leads directly to fewer and shorter outages
- Event-driven AI minimizes delays between detection and response
- More consistent handling of incidents improves overall reliability
Consistent and Repeatable Workflows
- Every incident follows the same structured triage pipeline
- Automated evaluation improves quality and standardization
- Reduces human error and variability in triage practices
Better Workforce Allocation
- Engineers focus on complex, high-value problems, not routine checks
- Staff capacity increases without growing team size
- AI absorbs repetitive triage steps, freeing human expertise
Tech Stack
AI Models and Frameworks
- Anthropic Claude 3.7 Sonnet for structured reasoning and multi-agent task execution
- AWS Bedrock to host and orchestrate LLMs securely at scale
- LangGraph to build agent workflows, orchestration logic, and stateful reasoning paths
Multi-Agent Architecture Components
- Supervisor Agent for ticket intake, task routing, and orchestration
- Worker Agents (Contextualization, Observability, Network Investigation, Evaluation)
- Agent-to-agent communication for collaborative analysis
Cloud and Infrastructure
- AWS cloud environment for hosting, scaling, and secure operation
- Serverless or containerized execution for rapid agent invocation
- Parallel processing architecture for simultaneous policy and incident evaluations
