No Priors: AI Agents Talking to AI Agents – Reinventing Commerce with Decagon CEO Jesse Zhang

Date: September 18, 2025
Hosts: Elad Gil & Sarah Guo
Guest: Jesse Zhang (CEO & Co-founder, Decagon)

Episode Overview

This episode explores the transformative impact of AI-powered agents on enterprise customer service and commerce. Jesse Zhang, CEO and Co-founder of Decagon, joins hosts Elad Gil and Sarah Guo to discuss how Decagon is reinventing the way large organizations engage with customers through AI, the evolution of AI adoption in enterprises, strategies for building commercial products, scaling startups, and glimpses into a near future where AI agents interact directly with one another.

Key Discussion Points & Insights

Decagon’s Mission & Evolution

• What Decagon Does

  • Decagon creates AI customer service agents, acting as a conversational UI across major enterprises (banks, telecoms, airlines, tech).
  • Goal: To make customer interactions more engaging and efficient, reduce costs, and increase future revenue via deeper engagement.
  • "You can kind of think of us... as a conversational UI for the brand... a concierge." — Jesse Zhang [00:34]

• Early Go-to-Market & Upmarket Shift

  • Started working with digital-native companies, then was quickly pulled upmarket due to enterprise demand.
  • "We were kind of pulled up market just because of the demand... enterprises moved a lot faster than we expected." — Jesse Zhang [01:32]
  • AI adoption has become a board-level, top-down priority in large enterprises, accelerating vendor selection and transformation.

• Tangible Impact

  • Decagon’s agents have enabled cost reductions by up to 60-70% in some customer service organizations, with a key focus on both efficiency and customer satisfaction.
  • "We’ve done case studies now where folks have been able to cut that down by 60, 70%." — Jesse Zhang [02:52]

Building and Scaling Decagon

• Founding Philosophy

  • As second-time founders, Jesse and co-founder Ashwin prioritized “commerciality”—valuing commercial problem-solving alongside technical product development.
  • "There’s a lot of untapped potential in making strong technical folks a bit more commercial..." — Jesse Zhang [04:47]

• Hiring & Team Culture

  • Optimization for raw intelligence and motivation over direct experience, especially early hires.
  • Strong in-office culture (five days a week), fostering intensity and career-defining opportunities.
  • "We're generally just selecting for very smart people... even on sales and marketing." — Jesse Zhang [05:52]
  • "Pretty much all the AI companies that are doing well have pretty heavy in-office cultures. You get way more done, especially in the early stage." — Jesse Zhang [07:52]

• Scaling Challenges

  • Transitioning from nimble, short-term focus to medium/long-term thinking in org design and product investment.
  • Need for evolving structures: People functions, operating cadences, international expansion.

• Learning from Other Startups

  • Admires companies like Ramp and Databricks for their scaling execution and technical-commercial blend.
  • "Ali at Databricks... probably go so far as to say he's my favorite CEO." — Jesse Zhang [10:59]

Building Defensibility & Differentiation

• Competitive Moat

  • Focused on building deeply integrated enterprise software layers, with observability, simulation, and insights.
  • Emphasis on empowering non-technical business users to configure AI agents, contrasting with the legacy, engineering-heavy SaaS customization model.
  • "One of the things that LLMs unlock is that you can really empower the non-technical business users..." — Jesse Zhang [19:49]

• Position Relative to Foundation Model Providers

  • Not particularly anxious about OpenAI/Anthropic forward-integrating into enterprise apps, since deep enterprise software is structurally defensible.
  • Partnership, rather than competition, remains possible with leading AI labs.

• Execution as Differentiator

  • Speed and adaptability from a young, intense team; productization for easier usability; sharp focus on execution.

Pricing, TAM, and the Changing Nature of Commerce

• Labor as a Service

  • Shift from SaaS (seat-based) pricing to labor/cognition-as-a-service models; Decagon charges per successful customer conversation.
  • "You generally price per the output that it's doing... very clearly the right pricing model for our space." — Jesse Zhang [22:38]

• Expanded Markets

  • AI agent pricing directly unlocks massive services TAM: automating entire classes of enterprise labor, not just selling software seats.
  • "Even us, plus all of our competitors...is probably still like a grain of sand in the overall market right now. That's exciting because there's a lot to do." — Jesse Zhang [24:21]

The Future: AI Agents Talking to AI Agents

• Agent-to-Agent Commerce

  • The vision: personal consumer agents acting on a user’s behalf, negotiating and transacting directly with enterprise agents (like Decagon).
  • "At some point maybe they'll call into an airline to reschedule your flight or something and then maybe they'll talk to our agent and then you'll have agents talking to each other..." — Jesse Zhang [28:03]
  • For now, natural language remains the agent protocol for compatibility but more efficient communication is likely to emerge.

• Commerce Redefined

  • The move from reactive support to proactive, agent-powered, concierge-style engagement; the customer journey starts the moment a user visits a site or app.

• A World Where Everyone Has an AI Assistant

  • Hosts speculate that personal digital assistants will become as ubiquitous as baths in modern homes—a luxury of the few democratized by technology.
  • "It seems like a similar thing. If you look at Bill Gates...he probably has a staff of people who buy clothes for him and go and do things for him and book flights for him. And so therefore, everybody will have this at some point, it'll just be agents." — Host [29:53]

Notable Quotes & Memorable Moments

• On the Start of Decagon:
  "We just saw a lot of folks that were willing to pay us like, you know, six figure contracts... it was the only idea we really explored that really had that property."
  — Jesse Zhang [16:17]

• Practical Advice for Aspiring Founders:
  "If you join a pre PMF team and you never actually get to see the commercials in action, you're not really learning much. You're just kind of learning essentially what not to do."
  — Jesse Zhang [11:28]

• Defining Long-Term Company Success:
  "We want to be the winner in this exciting market... reinvent the way that most consumers interact... and just go to market execution in the same way that I'm currently talking about Databricks and Ramp."
  — Jesse Zhang [26:01]

• Looking Ahead to Agentic Commerce:
  "You have all these consumer agents... at some point maybe they'll call into an airline to reschedule your flight... maybe they'll talk to our agent and then you’ll have agents talking to each other."
  — Jesse Zhang [28:03]

Timestamps for Key Segments

• Decagon Overview & Market Entry – [00:34–02:41]
• Enterprise AI Adoption & Impact – [02:41–03:48]
• Team Building & Culture – [04:47–07:52]
• Scaling Up & Org Design – [08:31–09:40]
• Learning from Startup Successes – [10:41–11:19]
• Advice to Engineers and Founders – [11:19–13:12]
• International Expansion & Culture – [13:12–14:49]
• Medium vs. Short Term Thinking – [14:49–15:37]
• Choosing Customer Service as Focus – [15:37–16:57]
• Platform Providers: Risk & Defensibility – [16:57–18:36]
• Differentiation: Product & Team Approach – [19:45–22:05]
• Commerce, Pricing, TAM – [22:05–24:46]
• AI Agents Talking to AI Agents – [28:03–30:36]
• Vision for the Future & Wrap Up – [30:36–31:00]

Episode Tone

The discussion is candid, practical, and fast-paced, with a clear focus on actionable learning for founders, the realities of scaling AI startups, and genuine excitement for a paradigm shift driven by agentic AI. Jesse Zhang’s insights are direct, often peppered with concrete evidence and thoughtful reflection on startup life cycles, market trends, and leadership.

Summary prepared for listeners seeking actionable insights on building and scaling AI-driven enterprises, and those looking to understand the future of AI-powered commerce.