Agentic engineering intelligence with Amazon Nova Multimodal Embeddings and Amazon Bedrock AgentCore

Amazon's walkthrough shows how to build a multi-agent engineering assistant that assembles part-substitution evidence for aerospace components, cutting the days engineers spend cross-referencing datasheets, interface control documents and qualification records.

• The design rule is that the engineer makes every decision while the system gathers the evidence, which decides where LLMs are used and where deterministic code is used instead.
• Tolerance-rule code in Python scores each candidate part against the reference part and the engineer's requirements, so identical inputs produce identical pass or flag verdicts that can go into a formal review.
• A planner routes each query to Catalog, Evaluation and Knowledge specialists, and a supervisor synthesizes their findings only when more than one contributed; complex questions chain all three in sequence.
• The system drafts justification memos and Integration Impact Assessments but does not recommend or approve a substitution, and every evaluation writes an audit trail entry aligned with AS9100 sections 8.4 and 8.5.6.
• Nova Multimodal Embeddings serves two roles: retrieving matching diagrams from text or an uploaded chart image with source attribution, and producing a cosine similarity score between candidate part photos that supports the deterministic fit verdict.