4 AI agent design patterns recommended by Andrew Ng
What are the 4 most popular AI agent design patterns from Andrew Ng?
1. Reflection Mode
- Meaning: Allows the agent to examine and correct its own generated outputs.
- Background: Large models sometimes generate outputs lazily, possibly only partially executing prompts, leading to limited effectiveness. Reflection mode enables the agent to review and refine its generated outputs, iteratively improving the quality.
- Scenario: Writing an industry short review. After generating the first draft, the agent reads it, identifies areas for modification, and then iteratively optimizes the generation.
2. Tool Use Mode
- Meaning: The agent possesses functionalities such as web searching, code execution, or any other tool to help gather information, take actions, or process data.
- Background: In many cases, large models generate content based solely on training data, limiting their utility.
- Tool Use Mode empowers the agent with functionalities it can request, such as connecting to databases or internal knowledge repositories, as well as accessing information through internet searches. It's the most widely used and recognized design pattern.
- Scenario: Generating a travel guide for a trip on a platform like Instagram. The agent can search for real-time textual and visual information about local weather, transportation routes, and operating hours of attractions, while also editing and formatting the content according to the author's style and platform standards, thus producing high-quality content on the fly
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3. Planning Mode
- Meaning: Enables the agent to decompose complex tasks and execute them according to a plan.
- Background: Generation by large models depends on the effectiveness of training data and can sometimes produce suboptimal results due to illusions.
- Planning Mode allows the agent to iteratively refine and process generated content based on planned task steps, leading to higher quality outputs.
- Scenario: Writing an academic paper. The agent first learns from the knowledge repository, then searches the internet for useful references, filters out relevant content, summarizes and organizes important information, engages in deeper-level thinking and refinement, and finally produces a high-quality paper.
4. Multiagent Collaboration Mode
- Meaning: Multiple AI agents work together, allocating tasks and discussing and debating ideas to propose better solutions than a single agent.
- Background: Large models occasionally encounter systemic tasks that require teamwork to complete, but individual agents typically focus on specific abilities.
- Scenario: Users can easily define multi-agent systems for various functionalities and use cases by simply filling out configuration documents in natural language, particularly in content creation studios involving various roles (such as scriptwriting/novel writing).
For example, we can use Coze's Multi-agent functionality to implement high-quality travel planning:
Step 1: Define 3 expert agents for the travel planning scenario:
Step 2: Arrange the 3 expert agents on the canvas and set up the conditions for task handover between them.
Generative / AI | Azure / Solutions Architect | Analytics Engineer | Data Quality | Python Coder
4 个月Yiman Huang Thanks for your article. Possible to share sources where Andrew Ng recommends these AI Agent design patterns please? :-)
Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
10 个月Your exploration of AI agent design patterns, as recommended by Andrew Ng, highlights the importance of understanding different approaches to building intelligent systems. This aligns with historical trends in AI research, where diverse methodologies have been employed to tackle complex problems. Considering the evolving nature of AI agents, how can researchers effectively balance between the trade-offs of different design patterns, such as efficiency versus flexibility, to ensure optimal performance in various applications and contexts?