To effectively build AI agents, ensure you have a way to measure results. Many developers build in a vacuum without feedback, which can lead to ineffective solutions.

To successfully implement AI agents, start small with a well-known task, design for limited downside, leverage natural language for building, iterate with compassion, and gradually increase the agent's scope and responsibility.

Most successful AI agents start with simple linear workflows rather than complex autonomous systems, which allows for easier implementation and scaling.

The five-question checklist for designing an AI agent includes understanding the task, starting small, keeping the downside low, providing enough context, and staying close to raw customer signals.

An iterative approach is essential in working with AI; continuously debugging and refining prompts can lead to more reliable outcomes.

To build your first AI agent with minimal risk, start by identifying low-risk, high-impact tasks that can be automated.

The iterative process of deploying AI systems, gathering feedback, and learning is crucial for building safe systems as we advance into more complex AI.

My advice for building AI agents is to start with something you love and find fun, as this will foster a deeper understanding and relationship with the model.

Companies are increasingly focused on the business value of AI agents rather than just technical benchmarks, emphasizing the importance of measurable impact.