Obermeier, DanielBatikas, MichailVeltrup, Leon2026-05-272026-05-272026-01-282025-12-17http://hdl.handle.net/10362/203507This thesis evaluates the automation potential of knowledge work tasks using multi-agent AI frameworks. A structured scorecard assesses task suitability across project roles. Selected tasks are implemented with distinct agent design patterns in CrewAI, using GPT-4o. Performance is evaluated via workflow-level metrics, including Task Success Rate and Effective Task Success Rate. Findings reveal that pattern-specific configurations significantly influence agent success, robustness, and resource efficiency. The study provides empirically grounded guidance onorchestrating design patterns for agents with varying task complexity in business analytics workflows.engAI agentsWorkflow automationModel evaluationTask selectionAgent performancemaster thesis204242380