Core Insights - The main challenge for AI agents in enterprises is not the technology itself but rather the integration of human processes, legacy systems, and the need for clear workflow definitions [34][38]. Group 1: Importance of AI Agents - AI assistants are becoming ubiquitous, with vendors promising "autonomous driving" capabilities for managing departments while multitasking [2]. - The transition from demonstration to actual production reveals that the real obstacles lie in human factors rather than AI technology [2][34]. Group 2: Key Obstacles - **Obstacle 1 - Ubiquity of Agents**: Many enterprise problems can be solved more effectively with simpler methods rather than relying on AI agents [9][10]. - **Obstacle 2 - Workflow Definition**: Enterprises often lack clear workflows, leading to confusion and inefficiencies [11][12]. - **Obstacle 3 - Integration with Existing Systems**: Integrating AI agents into legacy systems is complicated due to outdated designs and technical debt [18][24]. - **Obstacle 4 - Evaluation**: Continuous assessment of AI agents is crucial to ensure they are functioning correctly and meeting business needs [30][34]. Group 3: Recommendations for Successful Implementation - Modernizing workflows is essential to clarify what can be automated and how [13][36]. - Integration should be approached as a comprehensive project that addresses both workflow and legacy system challenges [38]. - Establishing robust evaluation metrics is necessary to build trust in AI agents and ensure they deliver value [30][33].