What's next for AI agentic workflows ft. Andrew Ng of AI Fund

Step-by-Step Tutorial: Understanding AI Agentic Workflows with Andrew Ng

Introduction:

• In this tutorial, we will delve into the concept of AI agentic workflows as discussed by Andrew Ng, a prominent figure in the field of artificial intelligence.
• We will explore the importance of agentic reasoning in building AI applications and how it can enhance productivity and performance.

1. Understanding the Current Workflow:

• Currently, most of us use Lish models in a non-agentic workflow where we provide a prompt and receive a response without iterative refinement.
• This traditional workflow involves typing a prompt and receiving a complete response without the opportunity for revision.

2. Transition to Agentic Workflow:

• An agentic workflow involves a more iterative process where the AI is guided through multiple steps to refine its output.
• This process includes tasks such as creating an outline, drafting content, self-revision, and iterative refinement.

3. Benefits of Agentic Workflow:

• The agentic workflow allows for more thoughtful and iterative processing, leading to improved results.
• By incorporating agentic reasoning, AI models can outperform traditional non-agentic approaches in tasks like coding and problem-solving.

4. Implementing Agentic Workflow:

• Start by transitioning from zero-shot prompting to a structured agentic workflow for tasks like coding.
• Compare the performance of models like GPT 3.5 and GPT 4 in both non-agentic and agentic workflows to observe the difference in results.

5. Design Patterns for Agentic Workflows:

• Explore different design patterns for agentic workflows, such as self-reflection, coder-agent interactions, multi-agent collaboration, and planning algorithms.
• These design patterns enhance the capabilities of AI models and improve their performance in various tasks.

6. Leveraging AI Agents for Productivity:

• Incorporate AI agents into your workflow to boost productivity and efficiency in tasks that involve coding, self-reflection, planning, and collaboration.
• Experiment with different agentic reasoning design patterns to find the most effective approach for your specific tasks.

7. Embracing the Future of AI:

• Emphasize the importance of agentic reasoning in shaping the future of AI and advancing towards Artificial General Intelligence (AGI).
• Stay updated on emerging trends in AI, such as fast token generation and multi-agent collaboration, to leverage the full potential of AI technologies.

Conclusion:

• By adopting agentic workflows and design patterns, individuals and organizations can enhance their AI applications' performance and productivity.
• Continuously explore and experiment with agentic reasoning approaches to stay at the forefront of AI innovation and contribute to the journey towards AGI.

Additional Resources:

• For further reading and references on agentic workflows and AI design patterns, refer to the recommended reading section provided in the video transcript.