Engineering Navigator

Engineering projects generate vast amounts of data—from requirements and simulations to test results and field feedback. The Engineering Navigator use case illustrates how an M4AI Agent can be configured to access and reason over this data. The agent uses the contextual information in the knowledge graph, enriched through DCH, and leverages the M4AI memory and retrieval components to answer questions, summarize traceability, and support engineering reviews.

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Navigate connected data across the engineering lifecycle to speed up decisions.

Instead of hopping between siloed tools, the Engineering Navigator agent enables a unified conversational interface. It understands the relationships between engineering artifacts and can answer questions like: “What requirement does this failed test trace to?” or “Who last modified the model used in this simulation?”

Technically, the agent is powered by a Memory Graph built from DCH, a RAG (Retrieval-Augmented Generation) system to pull relevant details, and optional personas to tailor its tone and focus. It can be embedded in tools like Jira, Confluence, or PLM systems.

Practical Example: A systems engineer needs to understand why a crash test failed. Using the Engineering Navigator agent, they retrieve linked simulation results, recent parameter changes, and related requirements in one prompt. What would have taken days of coordination now takes minutes.

1
min read