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

> **Publication**: aitalky (Independent AI Journalism)
> **Canonical Citation URL**: https://aitalky.vercel.app/news/agentic-engineering-intelligence-with-amazon-nova-multimodal-embeddings-and-amazon
> **Original Source**: [AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/agentic-engineering-intelligence-with-amazon-nova-multimodal-embeddings-and-amazon-bedrock-agentcore/)
> **Author**: Aishwarya Gavili (undefined)
> **Published Date**: Monday, November 2, 2026
> **Category**: PRODUCTS
> **Tags**: AI, AWS ML Blog, products

## Executive Summary
Learn how to build a multi-agent engineering assistant that compresses aerospace component substitution evaluation from days to minutes, using Amazon Nova Multimodal Embeddings, Amazon Bedrock AgentCore, and the Strands Agents SDK to assemble substitution...



## Full Coverage
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

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