Despite the expectation that modern AI agents would excel at routine office tasks, studies reveal that these agents fail 70% of the time due to issues not inherent in the language model itself but rather in the coordination of multiple agents. These failures often occur when agents must share context, hand off tasks, and recover from errors, leading to system-wide breakdowns. Microsoft's AutoGen framework addresses this by enabling agents to use natural-language conversations for coordination, which reduces the complexity and fragility of traditional API pipelines. This conversation-first approach simplifies debugging, accelerates development cycles, and offers flexibility by allowing the integration of various language models without vendor lock-in. Additionally, AutoGen's architecture supports robust monitoring and security features, crucial for enterprise deployment, while maintaining flexibility and scalability. However, production deployment of AutoGen comes with challenges such as non-deterministic agent conversations, inconsistent agent states, and resource contention, which can be mitigated through strategic solutions and tools like Galileo for real-time monitoring and optimization.