Using larger AI models to teach smaller models allows for the creation of specialized models that fit specific operating domains or cost requirements.

Most AI applications today, particularly those labeled as AI agents, often have low operational costs due to their reliance on software and LLMs, which are becoming cheaper over time.

As AI becomes more integrated into various applications, we need to optimize for specific internal use cases, which may lead us to use other models if they better serve those needs.

To keep the downside low when using AI agents, design them to provide recommendations rather than making decisions, and restrict their access to minimize risks.

Building a system that effectively utilizes AI agents requires careful orchestration and cannot be achieved merely by increasing model size.

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.

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

Different AI models have varying trade-offs, costs, and capabilities, leading enterprises to choose the right tool for specific tasks.

Companies are increasingly using multiple AI models for different tasks, optimizing performance by selecting the best model for each specific application.