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.

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.

Figure 1: Architecture of the multi-agent engineering assistant on Amazon Bedrock AgentCore

Amazon Bedrock AgentCore is a platform to build, connect, and optimize agents at scale, with any framework or model. In this solution, chat requests from the engineer’s application flow into a multi-agent graph hosted on AgentCore runtime: a planner (Claude Haiku 5.5) produces a routing plan, specialists (Claude Sonnet 5.5) execute their tools, and a supervisor (Claude Opus 5.5) synthesizes their results when more than one specialist ran. The specialists retrieve from a managed knowledge base that we created with Amazon Bedrock Managed Knowledge Bases using Amazon Nova Multimodal Embeddings, routed through an AgentCore Gateway, a capability of Amazon Bedrock AgentCore, alongside the Web Search connector for live web data. For image-as-query (uploading a chart to identify a part), the agent calls a native Amazon S3 Vectors index directly, because the managed knowledge base retrieval API doesn’t accept image input. Amazon S3 Vectors is a capability of Amazon Simple Storage Service (Amazon S3). A separate fixed chain (Knowledge, then Evaluation, then Supervisor) produces the formal Integration Impact Assessment (IIA) document.

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.

The complete code sample is a set of five Jupyter notebooks in the GitHub repository , and each notebook runs on its own. The notebooks import from mei_core , a self-contained copy of the agent and tool modules in the same folder, and they include their own parts data, chart crops, and evaluation query set. You can run every snippet in this post from the notebooks without other application code.

The sample dataset is seven reaction wheels from a fictional manufacturer (AnyCompany) spanning 0.003–5.0 Nms of momentum storage. Each part is a folder that pairs structured data with the source documents an engineer actually reads, and this mix is what makes the corpus multimodal. The specs.json file holds the machine-readable fields that drive deterministic scoring, while the ICD, product photo, and rendered datasheet page carry the visual and narrative detail that structured fields leave out.