Autonomous driving paper index
Multi Agent Systems In The Lean Startup Cycle: Operationalising Dynamic Capabilities
One-line summary
Generative, agentic AI promises to accelerate venture learning, yet we lack concrete designs for embedding them into entrepreneurial experimentation.
Engineering notes
Key topics: autonomous driving, control. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Generative, agentic AI promises to accelerate venture learning, yet we lack concrete designs for embedding them into entrepreneurial experimentation. This design science study proposes a multi-agent artefact that operationalises the Build–Measure–Learn (B-M-L) cycle as a closed-loop control system. Drawing on the Dynamic Capabilities View, we derive fifteen meta-requirements and thirty-three design principles (consolidated into seven goal-directed groups) for sensing, seizing, reconfiguring, orchestration, and governance. We instantiate them in a Node.js package instrumenting a production-grade SaaS codebase. Controlled simulations compare agentic and manual B-M-L cycles on feature ideas. The Multi Agent System reduces time-to-validated-learning by roughly an order of magnitude while preserving statistical rigour, traceability, and nuanced Persevere/Iterate decisions. Logs render capabilities observable at the feature level, turning “agentic AI” into a disciplined experimentation infrastructure rather than a generic assistant. We discuss implications for IS design and future field evaluations.
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