Training Data

Sierra Co-Founder Clay Bavor on Making Customer-Facing AI Agents Delightful


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Customer service is hands down the first killer app of generative AI for businesses. The reasons are simple: the costs of existing solutions are so high, the satisfaction so low and the margin for ROI so wide. But trusting your interactions with customers to hallucination-prone LLMs can be daunting.

Enter Sierra. Co-founder Clay Bavor walks us through the sophisticated engineering challenges his team solved along the way to delivering AI agents for all aspects of the customer experience that are delightful, safe and reliable—and being deployed widely by Sierra’s customers. The Company’s AgentOS enables businesses to create branded AI agents to interact with customers, follow nuanced policies and even handle customer retention and upsell. Clay describes how companies can capture their brand voice, values and internal processes to create AI agents that truly represent the business.

Hosted by: Ravi Gupta and Pat Grady, Sequoia Capital


Mentioned in this episode:

  • Bret Taylor: co-founder of Sierra
  • Towards a Human-like Open-Domain Chatbot: 2020 Google paper that introduced Meena, a predecessor of ChatGPT (followed by LaMDA in 2021)
  • PaLM: Scaling Language Modeling with Pathways: 2022 Google paper about their unreleased 540B parameter transformer model (GPT-3, at the time, had 175B) 
  • Avocado chair: Images generated by OpenAI’s DALL·E model in 2022
  • Large Language Models Understand and Can be Enhanced by Emotional Stimuli: 2023 Microsoft paper on how models like GPT-4 can be manipulated into providing better results
  • 𝛕-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains: 2024 paper authored by Sierra research team, led by Karthik Narasimhan (co-author of the 2022 ReACT paper and the 2023 Reflexion paper)


    00:00:00 Introduction

    00:01:21 Clay’s background

    00:03:20 Google before the ChatGPT moment

    00:07:31 What is Sierra?

    00:12:03 What’s possible now that wasn’t possible 18 months ago?

    00:17:11 AgentOS

    00:23:45 The solution to many problems with AI is more AI

    00:28:37 𝛕-bench

    00:33:19 Engineering task vs research task

    00:37:27 What tasks can you trust an agent with now?

    00:43:21 What metrics will move?

    00:46:22 The reality of deploying AI to customers today

    00:53:33 The experience manager

    01:03:54 Outcome-based pricing

    01:05:55 Lightning Round

    ...more
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    Training DataBy Sequoia Capital

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