Why the Best AI Agents Are Built Without Frameworks (Primitives over Frameworks) — Ahmad Awais, CHAI

Introduction and Overview 00:00

• Ahmad Awais introduces the concept of building AI agents using AI primitives instead of frameworks.
• The video demonstrates building an AI agent that can chat with a PDF using Chai, emphasizing the speed and efficiency of using primitives.

The Limitations of Frameworks 01:18

• Awais argues that existing AI frameworks are often bloated, slow, and filled with unnecessary abstractions.
• He advocates for using AI primitives, which are simpler and more effective for building scalable production-ready AI agents.

The Role of AI Primitives 02:32

• Examples of successful AI primitives include Amazon S3 for data storage, highlighting their efficiency.
• Awais shares his background in tech and experience with AI, indicating a strong foundation for his perspective on AI primitives.

Transition to AI Engineering 04:20

• Awais believes that many engineers will transition to AI roles, emphasizing the need for tools that streamline the development of AI agents.
• He promotes building on scalable primitives to avoid the complexities of frameworks.

Building AI Agents with Primitives 05:17

• The talk covers eight different AI agent architectures built using primitives, such as memory, threads, and parsing tools.
• A demonstration shows how to create a memory structure for storing PDF content and generating answers to queries.

Common Architectures for AI Agents 11:15

• Awais outlines various architectures, including augmented LLMs, prompt chaining, and agent routers.
• He explains how these architectures can utilize threads and memory to manage context and conversation.

Orchestrating Agents 17:25

• He discusses creating an orchestrator agent to manage multiple worker agents, coordinating tasks efficiently.
• A simple coding example illustrates how to implement this architecture without complex frameworks.

Evaluating and Optimizing Output 20:06

• Awais describes a feedback loop where generated content is evaluated by another agent for quality assurance.
• This process ensures that the output meets specific criteria based on the target audience.

Practical Applications of AI Primitives 22:32

• The talk presents various use cases for AI primitives, such as building a deep research agent or an OCR tool for document processing.
• Awais showcases real-time examples of how these agents can analyze queries and images effectively.

Conclusion and Encouragement 25:46

• Awais concludes by reinforcing the importance of using AI primitives for building scalable agents in a rapidly evolving technology landscape.
• He invites viewers to experiment with Chai and share their experiences, emphasizing the community aspect of AI development.