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This 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.
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AI agents Workflow automation Model evaluation Task selection Agent performance