Google White Paper on AI Agents and how it compares to Zendesk's offering
This article compares Zendesk AI Agents, including Bots and Copilot, to the Google Generative AI Agents whitepaper. It explores their features, use cases, strengths, and limitations, highlighting Zendesk’s CX-focused approach versus the whitepaper’s multi-domain flexibility.
Early January Google released a white paper on AI Agents. It's a 40 page overview of AI Agents (or Agents in short) that explores how agents can extend the raw capabilities of large-language models to not only read, reason and react to input, but to perform actual actions on that data.
Where most of the world looks at AI and LLM through the window of ChatGPT, within the Customer Care and Zendesk world we also know AI from the point of view of chatbots and agent copilots within the Zendesk platform.
The white paper almost serves as a how-to and motivation to start building AI Agents and Copilots within Zendesk, being it not that Zendesk has already done this.
My thanks to babelforce for sponsoring this month's Internal Note
So for the focus of this article it seemed like a fun exercise to compare the approach of the white paper to Zendesk approach in building AI Agents both as a direct comparison on how Zendesk's approach differs, as well as from an alignment standpoint to see how close Zendesk's AI Agents adhere to Google's theoretical approach.
Intro
Reading through the white paper there's a few sentences I highlighted as being highly relevant for Zendesk.
Tools bridge this gap, empowering agents to interact with external data and services while unlocking a wider range of actions beyond that of the underlying model alone. (p7)
I read this in two ways: one way is that by providing the LLM with external sources like Help Center, website or tickets, it can give more contextual and better responses. Secondly, Zendesk's Flow Builder and Ultimate's Bot platform are tools we can use to turn a raw AI model into a tool and feature you can actually use without building the entire tech-stack ourselves.
I think this is key to Zendesk's AI Agents' success. Combining (in their case) OpenAI's model with their own Zendesk AI model for intents and the unique knowledge of each customers' tickets and knowledge base creates powerful solutions that give better responses to their end-users than a Google search or conversation with a world-model like ChatGPT would.
By integrating retrieval-augmented generation (RAG), agents can connect with fresh data sources to extract valuable insights and uncover hidden patterns. (p29)
Zendesk AI is not just about bots. Zendesk QA and its autoQA tool can surface data across all your tickets and detect trends in quality, churn, expertise. Similar Ultimate's reporting can detect knowledge gaps in your data sources or surface new use-cases.
At the heart of an agent’s operation is the orchestration layer, a cognitive architecture that structures reasoning, planning, decision-making and guides its actions. Various reasoning techniques such as ReAct, Chain-of-Thought, and Tree-of-Thoughts, provide a framework for the orchestration layer to take in information, perform internal reasoning, and generate informed decisions or responses. (p40)
For me this is the big difference between using a tool like Zendesk AI and Ultimate, or "just putting ChatGPT in front of your data". Zendesk has done all the work and thinking to make sure their AI Agents react in a way that is useful for CX and it forces those bots to use your data to respond. And it abstracts all that technical stuff behind a UI that makes it easy to manage your bot without worrying about the underlying models, bias-prevention, validation e.a.
Extensions provide a way for agents to perceive, interact, and influence the outside world in a myriad of ways. (p18)
Bot the Zendesk Bot as well as Agent Copilot offer ways to integrate with external tools. Zendesk Bots offer hybrid flows that combine generative replies with raw API data, and Agent Copilot offers actions that allow you to integrate with external tools to react to customers, or update those tools based on customer input.
