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

------------------------------------------------------------------------------------------------------------------------------------------------
Humanloop is the LLM evals platform for enterprises. We give you the tools that top teams use to ship and scale AI with confidence. To find out more go to humanloop.com

$100M raised: How Decagon is building better AI agents I Jesse Zhang 41:45 Share

High Agency: The Podcast for AI Builders

Shownotes Transcript

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