Agentic engineering intelligence with Amazon Nova Multimodal Embeddings and Amazon Bedrock AgentCore
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 evidence while keeping the engineer as the decision-maker.
A multi-agent engineering assistant leverages Amazon Bedrock AgentCore, Amazon Nova Multimodal Embeddings, and the Strands Agents SDK to streamline the process of evaluating part substitutions. This system eliminates the need for engineers to manually cross-reference specifications across various documents, which can lead to costly mistakes and mission failures. The assistant comprises three main components: deterministic spec-fit evaluation, multi-agent orchestration, and human-in-the-loop decision-making.
Deterministic spec-fit evaluation employs Python scripts to score candidate parts against reference parts and engineer-specified requirements. The use of deterministic code ensures that the results are repeatable and suitable for formal engineering reviews. The multi-agent orchestration involves a lightweight planner that directs queries to three specialist agents: Catalog, Evaluation, and Knowledge.
These agents work together to search multimodal knowledge bases, score candidates against tolerance rules, compare part images, and verify lifecycle claims with live web data.
Human-in-the-loop decision-making ensures that engineers remain the final decision-makers. The system drafts justification memos and Integration Impact Assessments, but it does not approve substitutions on its own. Every evaluation also generates an audit trail aligned with AS9100 sections 8.4 and 8.5.6. The architecture is illustrated in Figure 1, which shows how the system is built around a planner that routes work across specialist agents using Amazon Bedrock AgentCore.
Amazon Bedrock AgentCore facilitates the orchestration of agents at scale, utilizing Claude Haiku 5.5, Claude Sonnet 5.5, and Claude Opus 5.5 as the agents. The specialists retrieve information from a managed knowledge base created with Amazon Bedrock Managed Knowledge Bases, which utilizes Amazon Nova Multimodal Embeddings to embed diverse content types into a single semantic space.
This enables retrieval of text and visual content based on semantic similarity. Visual similarity scoring adds quantitative support to deterministic spec comparisons, providing crucial evidence for pass/flag verdicts.
To implement this solution, you need an AWS account with access to Amazon Bedrock in the US East (N. Virginia) region, along with access to specified models in Amazon Bedrock. A managed knowledge base, populated with parts data such as datasheets and ICDs, is necessary for text and document retrieval. An Amazon S3 Vectors index stores part photos and chart crops for image-as-query functionality.
An AgentCore Gateway with necessary connectors and a managed knowledge base target is required to host the agent on AgentCore runtime. Finally, Python 3.10 or later, Jupyter, and the repository sample code are essential for running the notebooks and deploying the system.
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