AI Pioneer Shows The Power of AI AGENTS - "The Future Is Agentic"

Dr. Andrew Ng, a prominent figure in AI and co-founder of Google Brain, shares his enthusiasm for AI agents during a talk at Sequoia Capital. He discusses the potential of models like GPT 3.5 to reason at the level of GPT 4 and emphasizes the importance of iterative, agentic workflows over non-agentic ones. Ng's background and his venture, Coursera, are highlighted, along with Sequoia's impressive portfolio, which includes major tech companies like Apple and Instagram. The talk delves into the concept of agents performing tasks through multiple iterations, similar to human planning and revision processes.

The paragraph explores the concept of agentic workflows, where tasks are accomplished through the collaboration of multiple agents, each with a specific role. This approach is contrasted with zero-shot prompting, where AI generates an answer without iteration. A case study is mentioned, where using an agentic workflow with GPT 3.5 outperforms GPT 4 in a coding benchmark. The potential of agentic workflows to improve AI applications is discussed, along with various design patterns observed in agents, such as reflection, tool use, planning, and multi-agent collaboration.

The focus shifts to specific agentic design patterns, starting with reflection, where an AI is prompted to improve its own output. The benefits of this approach are highlighted, with examples of how it can lead to better code and more accurate responses. Tool use is the next pattern discussed, where AI is equipped with custom-coded tools to enhance its capabilities, such as web scraping or stock information retrieval. The paragraph also touches on the historical development of tool use in AI, particularly in computer vision before the advent of large language models.

Planning algorithms and multi-agent collaboration are introduced as emerging design patterns in AI agents. The ability for AI to plan steps and think through each stage is emphasized, which often results in superior outcomes. Multi-agent collaboration, where different agents work together, is described as a powerful but sometimes finicky technology. Examples include using multiple agents to write code, review it, and iterate until a high-quality result is achieved. The potential for agents to recover from failures and the importance of patience when working with agentic systems are also discussed.

The final paragraph discusses the importance of fast token generation for agentic workflows, which rely on quick iterations. The potential of leveraging high inference speeds, such as those provided by GPT models, to enhance agent performance is highlighted. The paragraph also touches on the economic considerations of using these advanced models and the anticipation of future improvements. The presenter expresses excitement about the progress of AI agents and the impact they could have on the field of AI, suggesting that agentic workflows might bring us closer to achieving general AI.