$100M raised: How Decagon is building better AI agents I Jesse Zhang

In this episode, Jesse Zhang joins Raza to discuss building cutting-edge AI agents for customer support. They explore how his early passion for LLMs led to creating a company that’s transforming the way businesses like Rippling, Duolingo, and Webflow interact with customers. Jesse breaks down the challenges of scaling AI systems, the importance of customer feedback, and his predictions for the future of AI.

Chapters:
00:00 - Introduction and Jesse Zhang's Background  
01:17 - First Exposure to LLMs and Building Early Projects  
04:32 - Decagon’s Rapid Growth and Differentiation in AI  
06:37 - Understanding Decagon’s AI Customer Support Product  
10:21 - Challenges in Building High-Performance AI Systems  
13:14 - Evolution from Simple RAG to Agent Architectures  
16:54 - Measuring Accuracy with Evals and Customer Feedback  
19:05 - Balancing Customization and Reusability Across Clients  
22:35 - Handling Customer Data and Incremental Deployment  
25:21 - Restructuring Support Teams for AI Integration  
27:03 - Team Composition and the Role of Domain Expertise  
29:19 - Advice for New AI Builders: Customer-Driven Development  
32:21 - Key Insights on AI Agents and Enterprise Adoption  
36:34 - Predictions for AI Advancements in 2025  
39:41 - Is AI Overhyped or Underhyped?  
41:07 - Closing Remarks and Final Thoughts

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