What changed is the release of a concrete reference design that stitches together Amazon Bedrock AgentCore, Amazon Nova Multimodal Embeddings, and the open‑source Strands Agents SDK to create a multi‑agent engineering assistant for part‑substitution tasks. Practitioners care because the pattern replaces days of manual document cross‑referencing with an orchestrated, auditable workflow that still requires the engineer’s final sign‑off.
Solution Overview
The assistant follows three design pillars: deterministic spec‑fit evaluation, multi‑agent orchestration, and human‑in‑the‑loop decision making. Deterministic evaluation is performed by Python code that applies tolerance rules to candidate parts, guaranteeing repeatable pass/flag outcomes suitable for formal review. Orchestration is handled by a lightweight planner (running Claude Haiku 5.5) that routes incoming queries to three specialist agents – Catalog, Evaluation, and Knowledge – each powered by Claude Sonnet 5.5. A supervisor agent (Claude Opus 5.5) synthesizes results when more than one specialist contributes, producing a draft justification memo and an Integration Impact Assessment (IIA).
Agent Roles and Data Flow
- Catalog agent retrieves part specifications from a managed knowledge base built with Nova embeddings. The knowledge base stores datasheets, interface control documents, and performance charts in a shared semantic space, enabling text or image queries.
- Evaluation agent runs the deterministic Python tolerance‑rule code against the retrieved candidates, scoring each against the reference part and the engineer’s stated requirements.
- Knowledge agent supplements the evidence with live web data via the Bedrock AgentCore Web Search connector, and can also query image‑as‑query vectors stored in Amazon S3 Vectors when a chart image is uploaded.
The planner constructs a fixed chain – Knowledge → Evaluation → Supervisor – for complex requests such as “check end‑of‑life status, find alternatives, and score them.” The supervisor aggregates the specialist outputs, adds source attribution, and writes an audit‑trail entry aligned with AS9100 sections 8.4 and 8.5.6.
Implementation Considerations
- Managed knowledge bases must be populated with Nova multimodal embeddings; the embeddings index both textual and visual content, allowing cosine similarity searches across diagrams and charts.
- Image queries cannot be sent directly to the managed KB API, so the assistant invokes a native
Amazon S3 Vectorsindex for those cases. - The Strands Agents SDK provides the plumbing for defining the planner, specialist, and supervisor agents, as well as the tool‑calling contracts used by each specialist.
- Deterministic evaluation code should be version‑controlled and tested independently of the LLM components to ensure compliance‑ready results.
Operational and Compliance Implications
Because the deterministic evaluation runs in pure Python, its outputs can be logged, audited, and referenced in formal engineering reviews without relying on LLM inference. The multi‑agent graph runs on the Bedrock AgentCore runtime, which abstracts scaling and monitoring, but operators must still provision appropriate logging for each agent and for the supervisor’s synthesis step. The audit‑trail entries generated for every evaluation satisfy AS9100 documentation requirements, providing traceability for part‑substitution decisions.
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What This Means For Practitioners
Adopting this pattern lets engineering teams replace manual cross‑referencing with a reproducible, LLM‑augmented workflow while keeping the decision authority firmly with the human engineer. Teams should focus on building a reliable Nova‑indexed knowledge base, isolating deterministic evaluation logic, and configuring Bedrock AgentCore monitoring to capture the full evidence chain. The result is a faster, auditable substitution process that integrates cleanly into existing compliance frameworks.