Sunday, October 13, 2024

AI agentic workflows

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

Andrew Ng, founder of DeepLearning.AI and AI Fund, speaks at Sequoia Capital's AI Ascent about what's next for AI agentic workflows and their potential to significantly propel AI advancements—perhaps even surpassing the impact of the forthcoming generation of foundational models.



An AI agentic workflow is a multi-step process that uses AI agents to complete tasks in a more efficient and accurate way. Agentic workflows are a departure from traditional automated processes and can allow for a higher level of autonomy and intelligence in business operations.

What Is an Agentic Workflow?

At its core, an agentic workflow is a process in which an LLM acts on behalf of users to perform tasks or provide assistance. These workflows leverage a model's capabilities to act as an intelligent intermediary between users and the information or services they require, enhancing productivity, efficiency and user experience. Agentic workflows have four types of design patterns:

  • Reflection: LLM examines its own work for areas for improvement.
  • Tool Use: LLM uses tools for certain tasks like gathering information, decision-making, processing information or taking action.
  • Planning: LLM creates and executes a dynamic multi-step plan to reach a goal. This can be helpful with complex tasks that cannot be decomposed ahead of time.
  • Multi-Agent Collaboration: Multiple agents work together on separate tasks to create a better solution.


@deeplearning.ai

"Agentic Design Patterns Part 1: Four AI agent strategies that improve GPT-4 and GPT-3.5 performance"

"Agentic Design Patterns Part 2: Reflection"

Agentic Design Patterns Part 3: Tool Use

"Agentic Design Patterns Part 4: Planning"

"Agentic Design Patterns Part 5: Multi-Agent Collaboration"





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