Responsible for:

Project “SMART GUIDE”

Bio:

Elias Jelinek studied Mechanical Engineering at the University of Duisburg-Essen, where he completed his master's on analyzing nanomaterials using neural networks. Alongside his studies, he founded the startup Diffusione, which focuses on generating AI-powered product images for the fashion industry and contributes to AI-related projects. Through this experience, Elias gained valuable insights into entrepreneurship, agentic AI and interdisciplinary collaboration.

His expertise spans through engineering and nanomaterial sciences, generative AI, agentic AI and computer vision and more. Elias is now joining Hannes Rothe's team as a research assistant, where he will work on the development of a peer-to-peer living platform and researching on scaling startups and reducing the time-to-MVP through templating.

Elias is passionate about emerging technologies and is eager to tackle new and exciting challenges.

Publications:

• Jelinek, Elias; Rothe, Hannes: Multi Agent Systems In The Lean Startup Cycle: Operationalising Dynamic Capabilities.. In: Proceedings of the European Conference on Information Systems (ECIS). Milan, Italy, 2026. Abstract Details Full textCitation

  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.