The path to AGI feels like a journey rather than a destination: Andrew Ng on AI Agentic Workflows

Despite how hard this is, LMs [Language Models] do it remarkably well…this workflow is much more iterative…and what not many people appreciate is this delivers remarkably better results. – Andrew Ng

In this riveting discussion, Andrew Ng, founder of DeepLearning.AI and AI Fund, explores the future of AI agentic workflows and their transformative potential in AI advancements.

The conversation delves into the shift from non-agentic to agentic workflows in AI, the importance of multi-agent collaboration, and how these developments could impact the journey towards Artificial General Intelligence (AGI).

Table of Contents

1. Transition to agentic workflows
2. Key design patterns in agentic workflows
3. The role of multiple agents
4. Promising results of agentic workflows
5. The impact of agentic workflows on language models
6. The resilience of AI systems with agentic loops
7. Surpassing the impact of foundational models
8. Impressive capabilities of AI agents
9. Integration of agentic loops in personal workflows
10. The importance of patience in AI interactions
11. Fast token generation in agented workflows
12. Agentic reasoning and the journey towards AGI

Transition to agentic workflows

Agentic workflows, characterized by iterative and collaborative processes, are becoming increasingly important in AI development.

These workflows involve tasks such as drafting, revising, and iterating through AI-generated content, leading to significantly improved results compared to traditional non-agentic approaches.

Key design patterns in agentic workflows

Reflective tools, self-reflection prompts, planning, and multi-agent collaboration are integral to agentic workflows.

These design patterns enhance productivity and performance in AI systems, paving the way for more sophisticated and effective AI models.