Building an effective AI agent requires a nuanced approach, starting with manually simulating the task using real inputs to understand its complexity and identify areas for automation. Once a viable simulation is established, building code that automates the loop of collecting inputs, performing deterministic computation, calling the model when necessary, evaluating results, and deciding on next steps can be done. The key to success lies in focusing on reliability through refinement of prompts, precise tool calls, and iterative testing, ensuring quality holds up across a range of real-world inputs and edge cases. By leveraging regular programming fundamentals with judicious use of LLMs for parts that require judgment, developers can create powerful AI agents that feel like magic but are grounded in smart systems with reasoning built in.