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

This article demonstrates building a multi-agent engineering assistant using Amazon Nova Multimodal Embeddings and Amazon Bedrock AgentCore to automate aerospace component substitution evaluation while keeping engineers in control of final decisions.

• Multi-agent orchestration routes queries to specialist agents (Catalog, Evaluation, Knowledge) that search multimodal knowledge bases, score candidates against tolerance rules, and compare part photos
• Deterministic Python code produces all pass/fail verdicts for formal engineering reviews, ensuring reproducible results independent of LLM outputs
• Amazon Nova Multimodal Embeddings enables visual similarity scoring between part photos and retrieval of charts and diagrams from datasheets using image-as-query
• Three-tier model strategy uses Haiku for routing, Sonnet for tool execution, and Opus for synthesis, optimizing cost and latency
• Human-in-the-loop design ensures engineers make all substitution decisions while the system assembles evidence and drafts formal documents with audit trails
• AgentCore Evaluations continuously scores agent quality across seven metrics including goal success rate, correctness, and tool accuracy

The solution combines multimodal retrieval, deterministic evaluation, and agentic orchestration to replace manual cross-referencing of engineering specifications with automated evidence assembly for aerospace component substitution decisions.