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.

Starting simple and having measurable results is crucial as you build complexity into your AI agents. This approach allows you to adapt and improve as you learn what works.

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

Instead of looking for a problem to solve with a technology like AI, you should fall in love with a real problem that people care about.

When creating an AI agent, focus on ongoing repetitive tasks that require some judgment but not full expertise, similar to delegating to a junior intern.

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

When testing AI agents, it's important to be forgiving and compassionate, as issues may arise from the inputs provided rather than the AI's capabilities.

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.

To optimize costs with AI agents, you can either choose a cheaper model or reduce the amount of data fed to it, but it's best to start with a good model and ample data first.