Multi-Agent Engineered Substitution Assistant Using Amazon Bedrock and Nova Embeddings
· Engineer's Notes · Cem Koyluoglu
A multi‑agent system that uses deterministic scoring and multimodal embeddings to assemble evidence for part substitution decisions.
What happened
The post describes a solution that builds a multi‑agent engineering assistant to reduce manual cross‑referencing when evaluating part substitutions. The assistant is constructed on Amazon Bedrock AgentCore and uses Amazon Nova Multimodal Embeddings to index a multimodal knowledge base that contains datasheets, ICDs, performance charts, and product photos. Three specialist agents—Catalog, Evaluation, and Knowledge—are orchestrated by a planner that routes queries to the appropriate agents. The Evaluation agent runs deterministic spec‑by‑spec comparisons and visual similarity scoring, while the Knowledge agent answers questions from documents and checks live end‑of‑life status. The system drafts justification memos and Integration Impact Assessments but never approves a substitution, keeping the engineer as the final decision maker. The solution is demonstrated with a synthetic dataset of seven reaction wheels, each represented by a folder that holds structured specs, text, PDFs, and images.
Why it matters in production
Reliability is increased because the deterministic spec‑fit evaluation produces consistent pass/flag results that can be formally reviewed. Cost and time savings come from automating the retrieval of evidence that would otherwise require days of manual cross‑referencing. Latency is mitigated by using a lightweight planner for routing and a large model only for synthesis, keeping response times reasonable for interactive engineering workflows. Security is addressed by keeping the decision‑making authority with the engineer and by writing audit‑trail entries that align with AS9100 sections 8.4 and 8.5.6, ensuring traceability of evidence. The multimodal embeddings allow the system to retrieve visual data such as torque curves that text‑only pipelines miss, improving completeness of the evidence base.
Engineering takeaways
- Use deterministic code for spec‑fit scoring to guarantee repeatable results that can be formally reviewed.
- Combine a lightweight routing model with a large synthesis model to balance latency and reasoning quality.
- Embed both text and visual content in a shared vector space to enable queries that match diagrams, charts, and photos.
- Keep the engineer as the sole approver by having the system draft but not approve substitution documents.
- Record audit‑trail entries aligned with industry standards (e.g., AS9100) to support traceability and compliance.
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This note was drafted with AI assistance from the primary source credited on this page, and automatically checked against that source before publishing.