AI agents have gained significant attention due to their ability to interact with external tools, maintain state, and execute complex workflows, akin to a personal assistant managing tasks like emailing, documenting, and scheduling. However, developing effective agents poses challenges, as seen in the author's experience of building a personal assistant app. Key pitfalls included underestimating the importance of clear, detailed instructions in prompts, overloading a single agent with too many tools, and failing to properly name and describe each tool. The solution involved adopting a multi-agent architecture where specialized agents focus on specific tasks, with an orchestrator agent managing and delegating tasks to ensure efficiency. The author emphasizes the critical role of precise tool descriptions and structured architectures in maximizing agent effectiveness, highlighting that while agents appear powerful, they require thoughtful design and clear information to perform optimally. Langflow, a visual IDE, is noted for aiding in developing generative and agentic AI workflows, allowing for quick iterations and seamless integration with applications.