Engineering AI assistants answer questions and provide decision support — they retrieve information from PLM knowledge bases, standards libraries, and engineering data sources and synthesise it into clear answers with citations. Engineering AI agents go further: they can take actions in enterprise systems — creating change requests in Teamcenter, querying SAP BOM data, generating impact analysis reports, and executing multi-step workflows that cross PLM and ERP system boundaries. EMUG AGENT programmes deploy both: assistants for knowledge access and decision support, and agents for workflow automation and multi-system task execution. The distinction matters for governance: assistants carry low risk as they only read and present data, while agents require carefully designed permission boundaries and human-in-the-loop checkpoints for consequential actions.

EMUG AGENT is the five-phase engineering AI assistant and agent delivery methodology: Assess — workflow analysis and agent scoping with integration architecture definition; Govern — agent safety design, permission matrix, guardrail configuration, and audit trail specification; Embed — agent development, PLM and SAP API integration, vector database build, and enterprise system tool-use connection; Navigate — user interface deployment, UAT, and phased go-live; Train — adoption enablement, feedback loop activation, and continuous improvement pipeline. AGENT addresses the core risk of engineering AI deployment: agents that have access to PLM and SAP systems without defined permission boundaries, human approval checkpoints, and audit trails create compliance and IP risk rather than productivity value.

EMUG AGENT connects AI agents to PLM and SAP systems through two integration layers: a retrieval layer using REST APIs to query PLM document content, BOM structures, and change history that is indexed into a vector database for semantic search; and an action layer using PLM and SAP APIs configured as agent tools that the LLM can call to execute specific actions — creating change requests, querying where-used, updating attributes, or triggering workflow steps. Teamcenter integration uses the Teamcenter Services REST API and Active Workspace APIs. Windchill integration uses the Windchill REST Services. SAP integration uses BAPIs, OData services, and the SAP Business Technology Platform (BTP) for cloud deployments. Access control policies are applied at both the API and vector database layer.

EMUG AGENT governance is designed in the Govern phase before any agent development begins: a permission matrix defines precisely which PLM and SAP actions each agent can execute autonomously (read operations, report generation, draft creation) versus which require explicit human approval before execution (creating change orders, modifying BOM records, triggering workflow approvals). Guardrail configurations prevent agents from accessing data outside their defined scope or executing actions above their permission level. Every agent action and data access is logged to an immutable audit trail for compliance reporting. Output confidence thresholds route low-confidence responses to human review rather than presenting uncertain answers as definitive. EU AI Act risk classification is performed for each agent deployment.

The highest-value engineering AI agent use cases share three characteristics: they involve accessing and synthesising information from multiple systems, they require domain knowledge to interpret correctly, and they currently consume significant senior engineer time. Top use cases include: engineering change impact analysis (querying PLM where-used, identifying affected documents, drafting change descriptions); BOM interrogation and cross-system reconciliation; standards and regulatory compliance checking against applicable codes; maintenance troubleshooting support from repair manuals and equipment history; RFQ technical response assembly from product capability data; and design review preparation from PLM data and customer requirements. Use cases where the decision carries safety or regulatory consequences are best deployed with human-in-the-loop approval rather than full autonomous execution.

Accuracy for RAG-based engineering AI assistants is measured on answer accuracy (correct information from the knowledge base), hallucination rate (fabricated information not present in sources), and citation correctness (source documents cited are relevant to the answer). EMUG AGENT programmes define acceptance criteria for each metric at the Assess phase — typically targeting 85 to 95 percent answer accuracy, below 2 percent hallucination rate, and above 90 percent citation relevance — validated with engineering subject matter experts before production go-live. Accuracy is highly dependent on knowledge base quality: well-structured PLM documentation with consistent metadata produces significantly higher accuracy than inconsistently tagged document collections.

A focused single-use-case engineering AI assistant — for example, a knowledge Q&A agent connected to one PLM knowledge domain — typically reaches production in 6 to 10 weeks. A multi-capability agent programme covering knowledge access, BOM query, and ECM automation typically takes 4 to 6 months from Assess to full Navigate go-live. A complex multi-agent workflow automation programme with SAP and PLM action integration may take 6 to 12 months depending on API availability and access control complexity. The most common schedule constraint is enterprise system API access provisioning and security review, not model development.

Engineering AI assistant and agent programmes are delivered across automotive OEMs and Tier 1 suppliers, aerospace and defense organisations, industrial machinery manufacturers, energy companies, and high-tech electronics firms in 20 countries: Germany, France, UK, Netherlands, Sweden, Italy, Spain, Poland, Czech Republic in Europe; India, Japan, South Korea, China, Malaysia, Thailand in Asia-Pacific; UAE and Saudi Arabia in the Middle East; USA, Canada, Mexico, Brazil in the Americas. Engineering AI agent delivery from Hyderabad, Germany, and Dubai with on-site deployment and integration capability at client engineering centres globally.