Building a Scalable Knowledge Base Agent with Amazon Bedrock and the MCP Gateway

Enterprises today face a persistent challenge: knowledge fragmentation. Critical information—such as architecture decisions, API documentation, and runbooks- often resides across wikis, code repositories, and collaboration tools. This fragmentation leads to reduced productivity, slower onboarding, and repeated effort across teams.

In this article, we’ll show how to build a scalable, centralized Knowledge Base Agent using Amazon Bedrock Knowledge Bases and the Model Context Protocol (MCP) Gateway. This solution applies a Retrieval-Augmented Generation (RAG) approach to deliver instant, contextual responses while maintaining enterprise-grade security, governance, and observability.
 
Organizations consistently report similar pain points around knowledge access:
Engineering teams spend 15–20 hours per week searching for documentation
New hires take 4–6 weeks to reach full productivity due to scattered information
QA teams recreate existing test cases because they’re difficult to find
Product teams struggle to stay aligned due to outdated or conflicting documentation
These inefficiencies translate directly into delayed releases, higher onboarding costs, and duplicated work.

The Business Challenge

The Knowledge Base Agent creates a unified retrieval layer that ingests enterprise knowledge, vectorizes content, and provides contextual answers through a standard API. The architecture combines several AWS services to achieve scalability, reliability, and governance by design.

Solution Overview

Key AWS Components:

Amazon Bedrock Knowledge Bases  - The foundation of the solution, offering fully managed vector storage, embedding, and retrieval capabilities. Bedrock handles chunking, indexing, and vectorization, eliminating the need to manage infrastructure.

Amazon S3  - Provides durable, versioned storage for all source documents and artifacts, supporting audit and compliance use cases.

AWS Lambda  - Powers ingestion and query workflows using serverless, event-driven execution for seamless scaling.

Amazon ECS with AWS Fargate  - Runs distributed Celery worker pools for bulk ingestion, long-running vector processing, and parallel document transformations.

Amazon ElastiCache for Redis  - Manages distributed task coordination, caching, and rate control to optimize query performance.

Amazon CloudWatch  - Delivers end-to-end monitoring, metrics, and structured logging for full operational visibility.

Standards-Based Integration with the MCP Gateway
The Model Context Protocol (MCP) Gateway standardizes access to the Knowledge Base Agent, enabling consistent integration across clients such as IDEs, chat interfaces, and internal portals.

The gateway provides multiple endpoints:

Security and governance are built in at the API boundary, with centralized authentication, authorization, rate limiting, and audit logging.

Implementation Best Practices

Document Ingestion and Chunking
Chunking plays a crucial role in retrieval quality. Tailor your chunking strategy based on document type:

Attach metadata such as document source, author, version, and creation date to every chunk for traceability.
 
Retrieval and RAG Orchestration
Hybrid retrieval - combining vector similarity with metadata filtering - improves precision:
 

Always include source citations in generated responses to enhance transparency and trust.
Security and Governance
Follow AWS’s defense-in-depth model across all layers:

Operational Excellence

Monitoring: Use CloudWatch dashboards to track query latency, cache hit rates, Bedrock API throttling, and worker queue depth.
Alerting: Set alarms for latency >5s, error rates >1%, and queue saturation.
Cost Optimization:
Use S3 Intelligent-Tiering for document storage
Batch embeddings for efficiency
Cache frequently accessed queries
Right-size ECS tasks based on workload

Measured Impact

Early adopters of this pattern have reported measurable benefits:

Response Time
Reduced from 12–15 minutes (manual search) to 3–5 seconds (automated retrieval)
 
Onboarding Efficiency
30–40% reduction in new engineer ramp-up time

Documentation Reuse
20–30% increase in reuse of existing test cases and specifications
 
Operational Efficiency
30–50% reduction in duplicated documentation
 
Example Use Cases Across Roles

Future Enhancements
The architecture can evolve with new AWS capabilities:
 
Multi-Modal Support
Use Bedrock’s multi-modal models to process diagrams and annotated screenshots.
Proactive Knowledge Delivery
Use Amazon EventBridge to surface relevant content contextually.
Human-in-the-Loop Validation
Integrate Amazon Augmented AI (A2I) for expert validation in regulated domains.
Regional Data Residency
Deploy multi-region knowledge bases with AWS PrivateLink for compliance
 
 
Integration with Xebia's AI-native Engineering Platform

This solution pattern is part of Xebia AI Native Engineering Solution | Xebia framework, which accelerates enterprise adoption of AI-driven architectures. It provides reusable blueprints for knowledge agents, observability, and secure model orchestration - enabling organizations to operationalize generative AI responsibly across their ecosystem.

Conclusion

The Knowledge Base Agent built with Amazon Bedrock and the MCP Gateway demonstrates how enterprises can transform fragmented institutional knowledge into a strategic, AI-augmented asset. With a serverless, standards-based architecture, organizations can securely scale contextual intelligence across teams - without compromising governance or control.
This reference pattern helps accelerate software delivery, reduce redundancy, and improve cross-team collaboration - all while adhering to AWS’s best practices for operational excellence.
Deploy via AWS Marketplace

You can explore and deploy this pattern directly from the [Amazon Bedrock Knowledge Base Agent on AWS Marketplace (to accelerate setup and integration within your AWS environment.


Additional Resources
Amazon Bedrock Knowledge Bases Documentation
AWS Lambda Best Practices
Amazon ECS Task Definitions
Model Context Protocol Specification
AWS Well-Architected Framework