dSPACE has introduced MCP-enabled AI workflows that connect AI agents directly with the company’s tools. Based on the open Model Context Protocol (MCP), this approach helps engineers transition from isolated prompt-based tasks toward connected, specification-driven validation workflows.
Requirements, specifications, simulations, implementations and tests in modern automotive development are often managed in different tools and environments. MCP reduces the need for manual handoffs by enabling AI agents to interact with dSPACE tools through standardized interfaces while keeping engineers in full control of reviewing and approving all activities and results.
dSPACE’s MCP-enabled tools enable AI agents to support engineering workflows spanning requirements analysis, software development, simulation preparation and validation across VEOS, ConfigurationDesk, SystemDesk, Bus Manager, ControlDesk and real-time hardware. The platform also feeds validation results back into the development process to support iterative engineering.
“AI delivers the greatest value when it can actively support engineering workflows rather than operate outside them,” said Jann-Eve Stavesand, director product portfolio management at dSPACE. “With MCP-enabled dSPACE tools, engineers can connect requirements, specifications, implementation and validation activities through AI-supported workflows while maintaining full transparency, traceability and engineering control. This allows engineering teams to streamline their validation workflows and transition more efficiently from requirements to verified implementations.”
By connecting AI Agents with simulation, configuration, measurement and validation tools, MCP-enabled workflows help reduce manual setup effort and streamline engineering processes. Engineers can transition more quickly from requirements and specifications to software validation on real hardware, while maintaining full traceability throughout the process. Validation results can be fed back directly into development workflows, enabling continuous refinement and accelerating the path from concept to validated implementation.
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