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- Guidance for Building Agentic AI powered Engineering Knowledge Assistants on AWS
Guidance for Building Agentic AI powered Engineering Knowledge Assistants on AWS
Overview
This Guidance demonstrates how to implement intelligent AI assistants that revolutionize enterprise knowledge management and accessibility. It helps organizations break down information silos and accelerate data access by creating a unified knowledge ecosystem that combines industry expertise with company-specific insights. The solution shows how to enable multi-modal interactions for instant knowledge retrieval and contextual recommendations, serving diverse stakeholders from design to manufacturing teams. Furthermore, it demonstrates how continuous learning capabilities can enhance cross-team collaboration, streamline decision-making processes, and drive engineering innovation, ultimately reducing cycle time to value across the organization.
Benefits
Transform complex R&D workflows by connecting specialized AI agents to your engineering data sources. Reduce time-to-insight from days to minutes while maintaining technical accuracy through grounded responses.
Break down information silos by integrating disparate data sources into a unified AI assistant. Enable cross-functional teams to access critical engineering insights regardless of their technical expertise.
Deploy specialized AI agents that handle routine engineering tasks independently. Free your experts to focus on innovation while maintaining compliance and quality standards.
How it works
Diagram 1
This diagram illustrates a flexible agentic AI framework on AWS that dynamically assigns specialized agents based on user identity and needs. The system authenticates users and provisions appropriate agent capabilities, maintaining security boundaries while enabling modular addition of new agents and tools for specific engineering domains.
Diagram 2
This architecture diagram implements key components from the previous framework: Authentication (Section 2) with Keycloak, Agent Management (Section 3) via the Session Agent Manager, specialized agent routing (Section 4) using AWS Strands Agents SDK, and Custom Tool integration (Section 6) including RAG operations with Bedrock Knowledge Bases and RFQ processing - all hosted on Amazon Elastic Kubernetes Service to provide a secure, flexible framework for engineering R&D.
Diagram 3
This architecture diagram shows a knowledge base for retrieval augmented generation (RAG) and GraphRAG pipeline.
Diagram 4
This architecture diagram shows continuous integration and continuous deployment diagram of the end-to-end solution.
Diagram 5
This architecture diagram shows alternative agent hosting on Amazon Bedrock AgentCore. Deploy secure, scalable AI agents on AWS with AgentCore's comprehensive set of enterprise-grade services, enabling complex workflows across tools and data sources while eliminating infrastructure management overhead.
Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
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