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

- Company: Amazon (amazon.com)
- Announced: 2026-11-02T17:01:16+00:00
- Category: not stated
- Coverage: not counted
- Announcement: no
- Group: routine
- Source: https://aws.amazon.com/blogs/machine-learning/agentic-engineering-intelligence-with-amazon-nova-multimodal-embeddings-and-amazon-bedrock-agentcore/
- Record: https://forck.live/items/30596-agentic-engineering-intelligence-with-amazon-nova-multimodal-embeddings-and
- Subject: Bedrock / Nova
- Models affected: Amazon Nova Multimodal Embeddings, Claude Haiku 5.5, Claude Sonnet 5.5, Claude Opus 5.5

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.

## Evidence

Verbatim from https://aws.amazon.com/blogs/machine-learning/agentic-engineering-intelligence-with-amazon-nova-multimodal-embeddings-and-amazon-bedrock-agentcore/:

> 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.

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