Here’s a concise summary of the four agentic AI design patterns described by Andrew Ng.
Reflection
The AI critiques and improves its own output iteratively.
Example: Generating code, reviewing it for errors, and revising based on feedback.
This pattern boosts performance by using self-assessment and refinement.
Tool Use
The AI determines when external actions are needed, such as making API calls or performing specific tasks.
Example: Searching the web, sending emails, or executing a function to solve a problem.
This pattern extends the AI’s capabilities by integrating tools and external systems.
Planning
The AI breaks down complex tasks into sequential steps and executes them in order.
Example: Processing an image, analyzing poses, and generating a description step-by-step.
This enables the AI to handle multifaceted problems systematically.
Multi-Agent Collaboration
The AI simulates multiple roles or agents that interact to solve a problem.
Example: One agent writes code while another critiques it, mimicking teamwork.
This approach enhances problem-solving by leveraging specialization and collaboration.
Each of these patterns reflects a structured approach to using AI for solving sophisticated, multi-step tasks effectively. Let me know if you'd like a deeper analysis of any of these!