Type of Publication: Article in Collected Edition

Multi Agent Systems In The Lean Startup Cycle: Operationalising Dynamic Capabilities.

Author(s):

Jelinek, Elias; Rothe, Hannes

Title of Anthology:

Proceedings of the European Conference on Information Systems (ECIS)

Location(s):

Milan, Italy

Publication Date:

2026

Keywords:

Agentic AI, Multi-agent systems, Lean Startup, Dynamic capabilities, Design science research

Link to complete version:

https://aisel.aisnet.org/ecis2026/entmodel/entmodel/6/

Citation:

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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.