Building effective AI agents is a complex endeavor, even for tech giants, due to challenges like hallucination and inconsistency in performance. A clear distinction exists between workflows and true AI agents, with workflows being predefined and reliable, while agents dynamically control their processes. Core patterns in AI system development include methods like prompt chaining, routing, and orchestrator-worker models, each serving different needs such as content creation or complex task management. Best practices emphasize establishing value before automation, choosing appropriate tools like Dify or LangChain, and opting for dedicated agents to improve performance and scalability. Documentation and verification are critical for effective AI agent deployment, as they ensure clarity and reliability. Developers are encouraged to start simple, scale gradually, and measure performance continuously, using platforms like Helicone for debugging and performance evaluation. Ultimately, success in AI agent development relies on balancing simplicity with complexity, focusing on pragmatic, composable patterns rather than intricate frameworks.