Modernizing Knowledge Management with Agentic AI on AWS

Over time, our client’s knowledge base became outdated and difficult to scale. Thousands of documents, including product manuals, technical specifications, procedures, and field reports, were spread across departments and stored primarily as PDFs. Finding the right information often required knowing exactly where to look or which file name to search for, slowing down employees and increasing operational costs.

At the same time, the organization wanted to give customers easier access to product information. While the data existed, it lived in static documents that were not intuitive to navigate or search. As the company expanded, maintaining this fragmented system became increasingly inefficient, creating friction for internal teams and limiting customer self-service.

The business needed a modern, easy-to-use knowledge platform that could unify information across the organization, support both desktop and mobile users, and deliver accurate answers grounded in source documentation.

The architecture was built on AWS to deliver a fully managed, pay-per-use solution optimized for generative AI workloads. AWS Bedrock serves as the foundation for decision-making and response synthesis, allowing the system to generate high-quality answers grounded in the organization’s proprietary data.

Retrieval-Augmented Generation is implemented using AWS Bedrock Knowledge Bases, which connect directly to document repositories stored in Amazon S3. When users submit questions, the system retrieves the most relevant content and uses it to produce accurate, traceable responses. This approach ensures reliability while maintaining transparency through source citations.

Agentic AI capabilities were implemented using LangChain and LangGraph, enabling the system to autonomously decide which tools to use, orchestrate workflows, and maintain execution state. AWS Lambda orchestrates these interactions, while AWS Fargate hosts the Model Context Protocol (MCP) server used for integrations such as IT service management. Agent state and checkpoints are stored in Amazon Aurora PostgreSQL to support complex, multi-step reasoning.

The entire platform runs within a secure AWS environment using VPC networking, IAM-based access controls, and centralized monitoring through Amazon CloudWatch. Application traffic is managed using Application Load Balancers, Amazon CloudFront, and Route 53 to ensure reliability and low-latency access.

The legacy knowledge base was fully retired and replaced with a modern, AI-driven platform. Today, employees use the system hundreds of times per week to quickly find answers and supporting documentation. Customers now have access to a GenAI-powered interface that allows them to ask questions about products and receive immediate, source-backed responses.

The new platform has reduced time spent searching for information, improved internal efficiency, and created a scalable foundation for future AI-driven use cases across the organization.