Built on an AI engine and a dedicated simulation knowledge base, the agent sets up simulation scenarios, tunes parameters in batches, analyses results and produces reports automatically. Running around the clock, it shortens the simulation and verification cycle considerably.
Regression verification is shortened from weeks to days, keeping pace with development iterations.
Runs automatically around the clock with no manual parameter tuning, freeing up engineering time.
Generates anomaly explanations and visualised reports automatically, with accurate data, clear conclusions and a full audit trail.
The AI engine draws on the ZD simulation knowledge base to resolve address mapping, timing relationships and cycle calculations precisely.
A large language model serves as the reasoning core, tuned on automotive R&D and testing corpora to support requirement parsing, root cause reasoning and code generation.
Brings together ZD simulation knowledge, historical fault cases and diagnostic experience with retrieval-augmented generation, so output matches real engineering contexts.
Supports local and private-cloud deployment so R&D data never leaves the company, meeting OEM data security and compliance requirements.
Agent output connects directly to test execution, simulation scheduling and ticketing systems, so results flow automatically and are tracked to closure.
Engineers supply DBC, ARXML or JSON interface definitions and the AI generates executable ZD Box Python simulation scripts: over 95% script accuracy, generation time down from days to minutes and 80% better interface reuse.
The regression verification cycle drops from weeks to days, parameters are tuned in batches around the clock without supervision, and abnormal values are filtered automatically with an explanatory report.
ZD Technology · AI agents for vehicle R&D and testing