AI + a16z

Can AI Agents Finally Fix Customer Support?

Dec 18, 2024

Jesse Zhang, Co-founder and CEO of Decagon, shares insights from his background, including a prior venture acquired by Niantic. He discusses how large language models are revolutionizing customer support, emphasizing customizable AI agents that adapt to specific business needs. The conversation shifts to innovative pricing models, like pay-per-conversation, enabling startups to disrupt traditional seat-based pricing. Zhang highlights the agility of younger companies in iterating quickly, providing them an edge in the competitive landscape of AI-driven customer service.

44:12

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Episode guests

Jesse Zhang

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Episode notes

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Quick takeaways

AI agents are evolving to mimic human cognition and improve customer support through natural language interactions and adaptability.

The shift to a per-conversation pricing model allows startups to disrupt incumbents by emphasizing work output over traditional seat-based pricing.

Effective implementation of AI agents requires careful regulatory compliance and transparency, ensuring safety while enhancing customer interaction quality.

Deep dives

The Evolution of AI Agents

AI agents are increasingly being built to think and operate in a manner akin to human cognition, prioritizing natural language interactions. This evolution allows agents to absorb new information and receive feedback similarly to how a competent human team member would learn and adapt over time. The complexity of decision trees previously used has diminished as language models (LMs) have improved, making way for more conversational interfaces that enhance user experience. As AI agents become sophisticated, their utilization is expected to become more integrated into natural communication, moving away from cumbersome programming models.

Intro

5min

Revolutionizing Customer Support with AI

12min

The Future of UI and UX in AI Agents

2min

Navigating AI-Driven Customer Support Challenges

10min

The Evolution of Multimodal AI Agents

2min

AI Agents in Enterprise: Insights and Challenges

10min

AI's Role in Workplace Dynamics

3min

In this episode of the AI + a16z podcast, Decagon cofounder/CEO Jesse Zhang and a16z partner Kimberly Tan discuss how LLMs are reshaping customer support, the strong market demand for AI agents, and how AI agents give startups a a new pricing model to help disrupt incumbents.

Here's an excerpt of Jesse explaining how conversation-based pricing can win over customers who are used to traditional seat-based pricing:

"Our view on this is that, in the past, software is based per seat because it's roughly scaled based on the number of people that can take advantage of the software.

"With most AI agents, the value . . . doesn't really scale in terms of the number of people that are maintaining it; it's just the amount of work output. . . . The pricing that you want to provide has to be a model where the more work you do, the more that gets paid.

"So for us, there's two obvious ways to do that: you can pay per conversation, or you can pay per resolution. One fun learning for us has been that most people have opted into the per-conversation model . . . It just creates a lot more simplicity and predictability.

. . .

"It's a little bit tricky for incumbents if they're trying to launch agents because it just cannibalizes their seat-based model. . . . Incumbents have less risk tolerance, naturally, because they have a ton of customers. And if they're iterating quickly and something doesn't go well, that's a big loss for them. Whereas, younger companies can always iterate a lot faster, and the iteration process just inherently leads to better product. . .

"We always want to pride ourselves on shipping speed, quality of the product, and just how hardcore our team is in terms of delivering things."

Learn more:

RIP to RPA: The Rise of Intelligent Automation

Big Ideas in Tech for 2025

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Jesse Zhang

Kimberly Tan

Derrick Harris

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