The Future of AI: Agentic Workflows

Andreu, a renowned computer science professor at Stanford and creator of Coursera and Google Brain, discusses the exciting trend of AI agents. Traditional AI models work in a non-agentic workflow, where a prompt generates an answer. This is akin to typing an essay without using backspace. In contrast, an agentic workflow involves iterative processes like outlining, researching, drafting, and revising, leading to remarkably better results.

For instance, using GPT-3.5 with zero-shot prompting achieves 48% accuracy, while GPT-4 reaches 67%. However, wrapping an agentic workflow around GPT-3.5 outperforms even GPT-4. This has significant implications for building applications.

Reflection is a tool where an AI agent reviews its own code for errors and improvements. This iterative process often results in better code. Multi-agent systems involve multiple AI agents collaborating, such as one acting as a coder and another as a critic. This approach can significantly boost performance.

Tool use expands what AI can do, such as using code to manipulate images. Planning algorithms allow AI to autonomously reroute around failures, making them highly effective for complex tasks. Multi-agent collaboration involves AI agents acting in different roles, like CEO or software engineer, to develop complex projects.

The agentic reasoning design patterns are crucial for productivity. As AI capabilities expand, we need to learn to delegate tasks to AI agents and patiently wait for responses. Fast token generation is essential for iterative workflows, allowing AI to generate results quickly.

In summary, agentic workflows are a significant trend in AI, offering a path towards more advanced AI capabilities and potentially AGI (Artificial General Intelligence).

The original article: https://m.youtube.com/watch?v=sal78ACtGTc