Agentic engineering intelligence with Amazon Nova Multimodal Embeddings and Amazon Bedrock AgentCore
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This article details the development of a multi-agent engineering assistant designed to streamline aerospace part substitution evaluations using Amazon Bedrock AgentCore and Amazon Nova Multimodal Embeddings. The system addresses the complexity of cross-referencing obsolete components by automating evidence assembly while ensuring engineers remain the final decision-makers for safety-critical approvals. It employs three specialist agents—Catalog, Evaluation, and Knowledge—orchestrated by a planner that routes queries based on task requirements. Deterministic Python code handles spec-fit scoring to guarantee reproducible pass/fail verdicts, whereas large language models manage retrieval and synthesis tasks. Amazon Nova Multimodal Embeddings enables both multimodal document retrieval and visual similarity scoring between part photos, allowing engineers to query using images or text. The architecture includes a fixed chain for generating formal Integration Impact Assessments and uses live web data via an AgentCore Gateway to verify component lifecycle status. Online evaluations demonstrate high accuracy in tool selection and parameter extraction, with trace-level metrics consistently exceeding 0.95. The solution is presented as a reusable pattern adaptable to other manufacturing catalogs through simple schema and data updates.
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