The client is an established IT infrastructure monitoring and observability platform provider operating in the IT operations management domain. Its unified platform combines IT Service Management with comprehensive telemetry monitoring across metrics, logs, flow data, and traces, enabling enterprises to gain end-to-end visibility into their IT environments. With a focus on proactive infrastructure management and intelligent automation, the organization supports businesses in improving operational efficiency, strengthening service reliability, and managing increasingly complex IT environments.
The IT operations management platform required an AI-powered root cause analysis capability to identify the underlying causes of incidents and improve troubleshooting efficiency across complex IT environments. The solution needed to correlate telemetry data across metrics, logs, and traces from dependent services to identify probable root causes and surface supporting evidence. The platform also needed to evaluate the feasibility of using Amazon Bedrock AgentCore as the foundation for an agentic AI-powered RCA solution while assessing accuracy, response time, cost per invocation, and scalability.
The implementation also presented technical challenges around enabling multi-step AI reasoning, integrating multiple operational data sources and AWS services, maintaining conversational context, securely isolating customer environments, and establishing comprehensive monitoring and observability for the agentic workflow. The solution further required a scalable foundation that could support multiple tenants while maintaining appropriate authentication, authorization, data isolation, and operational controls.
To address IT operations management platform’s requirements, Comprinno implemented a Generative AI-powered Root Cause Analysis on AWS using Amazon Bedrock AgentCore and LangGraph. The solution implements a cloud-native, multi-agent architecture designed to investigate operational issues through natural-language interactions and correlate telemetry data across metrics, logs, and traces.
The solution uses a Supervisor Agent to classify user intent, route queries, orchestrate specialist agents, and synthesize responses. Specialized agents provide capabilities across CloudWatch monitoring, Security Hub analysis, AWS Cost Explorer, Trusted Advisor, Jira operations, AWS knowledge retrieval, and deep telemetry investigation. The Investigator Agent performs multi-step SQL-based investigation across OpenTelemetry metrics, logs, and traces stored in PostgreSQL to identify root causes and correlate supporting evidence.
Amazon Bedrock provides the foundation models for the agents, with Anthropic Claude Sonnet 4.5 used for supervisor-level orchestration and Claude Haiku 4.5 used across specialist agents. AgentCore Gateway provides MCP-based tool access, while AgentCore Memory maintains conversational persistence and session context. The architecture also incorporates Amazon ECS Fargate, Amazon RDS for PostgreSQL, Amazon S3, Amazon Cognito, Amazon CloudFront, Amazon API Gateway, and AWS security and observability services.
Key Actions:
Agentic AI-Powered RCA Implementation:
 Multi-Agent Intelligent Operations:
Telemetry Correlation & Conversational Memory:
Secure Multi-Tenant Architecture:
Observability, Evaluation & Production Readiness:
Comprinno designed a secure, highly available AWS architecture for IT operations management platform, the AI-powered Root Cause Analysis solution. The architecture is deployed within an Amazon VPC spanning multiple Availability Zones, providing a resilient foundation for the application and AI workflow. Users access the solution through Amazon CloudFront and Amazon S3 for the frontend, while Amazon API Gateway provides the primary API entry point and routes requests to the application layer. The core application processing layer uses Amazon ECS Fargate across multiple Availability Zones within the VPC. The Fargate services orchestrate application requests and interactions with Amazon Bedrock AgentCore and Amazon Bedrock. Amazon Bedrock AgentCore provides the agentic AI execution layer, while Amazon Bedrock provides access to foundation models supporting the AI workflow. Amazon DynamoDB provides managed storage for application and session-related data. Amazon CloudWatch and AWS X-Ray provide monitoring, logging, and distributed tracing. Security and governance are supported by Amazon Cognito, AWS IAM, AWS KMS, AWS Secrets Manager, AWS Config, AWS CloudTrail, Amazon GuardDuty, AWS Security Hub, and AWS Certificate Manager.
Overall, the architecture follows a multi-AZ, managed-services approach combining CloudFront, S3, API Gateway, ECS Fargate, DynamoDB, Bedrock AgentCore, Bedrock, and AWS security and observability services to provide a secure and resilient foundation for IT operations management platform’s AI-powered RCA capabilities.
The IT operations management platform’s GenAI-powered RCA solution demonstrates how Agentic AI can enhance modern IT observability by automating complex investigation and reasoning across operational telemetry and AWS services. By combining Amazon Bedrock AgentCore, Amazon Bedrock, ECS Fargate, API Gateway, DynamoDB, and multiple AWS security and observability services, the solution provides an intelligent foundation for analyzing operational issues and supporting faster root cause identification.
The multi-agent architecture enables specialized AI capabilities across monitoring, security, cost optimization, AWS recommendations, knowledge retrieval, and incident management, while the underlying AWS architecture provides secure access, high availability, monitoring, and scalability. Overall, the solution provides Enabling Agentic AI-Powered Root Cause Analysis and Intelligent IT Operations with Amazon Bedrock AgentCore for an IT operations management platform with a strong technical foundation to validate AI-driven root cause analysis and evolve its observability platform toward more intelligent, automated, and proactive IT operations.
Comprinno Achieves AWS Data and Analytics Competency | Read More →
