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.

We need to keep AI on a leash, as over-reliance on AI agents can lead to issues like introducing bugs or security vulnerabilities.

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.

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 prevent AI from generating misleading information, it's important to give it an escape hatch, allowing it to indicate when it lacks sufficient information to make a decision.

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

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.

When using AI agents, it's crucial to evaluate the security risks associated with third-party services accessing company data, especially in light of existing automation practices.

Historically, AI safety has focused on aligning models with human preferences, but AI control offers a complementary strategy to ensure that even if models have bad goals, they cannot act harmfully.