When a qualified aerospace component goes obsolete or hits a long lead time, an engineer spends days cross-referencing specifications across vendor datasheets, interface control documents (ICDs), and qualification records before documenting a substitution justification for review. An incorrect substitution on a flight-critical component can cause mission failure, yet the evidence trail behind each decision is often spread across email and undocumented institutional knowledge, so there is no formal, repeatable package for a reviewer to check.

A previous post, Manufacturing intelligence with Amazon Nova Multimodal Embeddings, showed how the model retrieves answers locked inside engineering diagrams, thermal plots, and datasheet charts that text-only pipelines miss. This post picks up where that retrieval work left off, putting those capabilities inside a multi-agent system that assembles substitution evidence, from retrieval to a drafted assessment.

In this post, we show how to build a multi-agent engineering assistant that cuts the manual cross-referencing behind part substitution evaluation using Amazon Bedrock AgentCore, Amazon Nova Multimodal Embeddings, and the open source Strands Agents SDK. A planner routes work across three specialist agents that search a multimodal knowledge base, score candidates against deterministic tolerance rules, compare part photos, and ground lifecycle claims in live web data. The engineer remains the decision-maker throughout, because the system assembles evidence and drafts documents without ever approving a substitution on its own.

The assistant is built around one design rule, which is that the engineer makes every decision and the system assembles the evidence for it. That rule determines where we use large language models (LLMs) and where we use deterministic code instead.

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