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Zendesk Launches AI Agent Builder

Zendesk Launches AI Agent Builder

Zendesk Launches AI Agent Builder and Enhances Agent Copilot Zendesk has unveiled its AI Agent Builder, a key feature in a series of significant updates across its platform. This new tool enables customer service teams to create bots—now referred to as “AI Agents”—using natural language descriptions. For example, a user might input: “A customer wants to return a product.” The AI Agent Builder will recognize the scenario and automatically create a framework for the AI Agent, which can then be reviewed, tested, and deployed. This framework might include essential steps like checking the order number, verifying the items for return, and cross-referencing the return policy. Matthias Goehler, CTO for EMEA at Zendesk, explains, “You can define any number of workflows in the same straightforward manner. The best part is that business users can do this without needing to design complex flowcharts or decision trees.” However, developers may still need to consult an API when creating AI Agents that interact with multiple third-party applications. Other Enhancements to Zendesk’s AI Agents The AI Agent Builder simplifies the automation of customer interactions that involve multiple steps. For more straightforward queries, Zendesk can connect a single AI Agent to trusted knowledge sources, allowing it to autonomously provide answers. Recently, the vendor has expanded this capability to email and strengthened its partnership with Poly.AI to integrate conversational AI capabilities into the voice channel. Goehler remarked, “When I first heard a Poly bot, I thought it was a human; it even had subtle dialects and varied pacing.” This natural-sounding voice, combined with real-time data processing, enables the bot to understand customer intent and guide them through various processes. Zendesk aims to help customers automate up to 80 percent of their service inquiries. However, Goehler acknowledges that some situations will always require human intervention, whether due to case complexity or customer preferences. Therefore, the company continues to enhance its Agent Copilot, which now includes several new features. The “Enhanced” Zendesk Agent Copilot One of the most exciting new features in Agent Copilot is its “Procedure” capability. This allows contact centers to define specific procedures for the Copilot to execute on behalf of live agents. Users can specify these procedures in natural language, such as: “Do this first, then this, and finally this.” During live interactions, agents can request the Copilot to carry out tasks like scheduling appointments or sending shipping labels. The Copilot can also proactively suggest procedures, share recommended responses, and offer guidance through its new “auto-assist” mode. While the live agent remains in control, they can approve the Copilot’s suggestions, allowing it to handle much of the workload. Goehler noted, “If the agent wants to adjust something, they can do that, too. The AI continues to suggest steps and solutions.” This feature is particularly beneficial for companies facing high staff turnover, as it allows new agents to quickly adapt with consistent, high-quality guidance. Zendesk has also introduced Agent Copilot for Voice, making many of its capabilities accessible during customer calls. Agents will receive live call insights and relevant knowledge base content to enhance their interactions. Elsewhere at Zendesk 2024 has been a transformative year for Zendesk. The company has entered the workforce engagement management (WEM) market with acquisitions of Klaus and Tymeshift. This follows the integration of Ultimate, which laid the groundwork for the new Zendesk AI Agents and significantly enhanced the vendor’s conversational AI expertise. Additionally, Zendesk has developed a customer messaging app in collaboration with Meta, established a venture arm for AI startups, and announced new partnerships with AWS and Anthropic. Notably, Zendesk has gained attention for introducing an “industry-first” outcome-based pricing model. This move is significant as many CCaaS and CRM vendors, facing pressure from AI solutions that reduce headcounts, have traditionally relied on seat-based pricing models. By adopting outcome-based pricing, Zendesk ensures that customers only pay more when they achieve desired outcomes, addressing a key challenge in the industry. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. 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FigJam AI

FigJam AI

Recently Edward Chechique explained FigJam AI, an AI flowchart generator. Below is a summary of this article originally published at uxdesign.cc Over the past three years, the author has extensively researched and tested how product designers can use generative AI technology to improve efficiency and accuracy in their design workflows. Although this journey is still in its early stages, designers must integrate AI into their design processes today. This integration will enable them to work more efficiently and with greater precision. Common AI tools on the market include ChatGPT, Gemini, and Midjourney, showcasing key features of artificial intelligence technology. Additionally, many new tools are launched daily. Apart from the new AI tools, there are tools not originally AI-based, like Figma and Miro. These tools have started to add AI capabilities, enhancing their utility. This article aims to show how designers can use FigJam AI, an efficient AI flowchart maker, to create flowcharts. In the words of designers: “Stop moving rectangles and invest in creativity.” This encapsulates the author’s goal. What is FigJam AI? FigJam AI is an AI tool that Figma added to FigJam. It helps cluster information, generate design thinking workshops, organize information, summarize information, and more. For a more detailed look at this AI feature, check out the author’s article about FigJam AI. Step-by-Step Introduction to Figjam AI for Better Design Collaboration Enhancing Design Team Workflows with Figjam AI The Importance of Detailed Prompts When teaching students how to use AI, the author emphasizes the importance of providing the AI with exact instructions. Without this, the desired results may not be achieved. This highlights a significant difference from traditional work processes. Often, traditional methods don’t start with a clear end solution but involve experimenting, adjusting designs, and refining the vision over time. In contrast, AI requires precise instructions from the beginning. The examples provided illustrate this point. During the process, small changes in the prompt can significantly affect the results, demonstrating the importance of clear and specific instructions. Generate Flowcharts from Text The author conducted several tests with FigJam AI to illustrate the process. Starting with simple prompts and gradually moving to more sophisticated approaches, here are three key tests: Test 1: Happy Flow The author began with a simple flowchart for buying a T-shirt in an online store to see if the AI could generate a complete flow from it. Prompt: Act like a product designer and create a Flow chart for the process of buying a T-shirt in an e-commerce store: Create only this flow. Do not add anything else. Result: The AI created exactly what was asked, making the creation process more efficient by eliminating the need to manually adjust “rectangles,” streamlining the diagram creation. Test 2: Flow with One Error The second test aimed to see if the AI could add a condition to the flow for cases where something does not work correctly. Prompt: Act like a product designer and create a Flow chart for the process of buying a T-shirt in an e-commerce store: Take into account the error case: Promo Code does not work. Create only this flow. Do not add anything else. Result: The AI accomplished the task exactly as requested and added the error case to the flowchart creation. Test 3: Flowchart with Three Errors The next step was to see if the AI could handle three error cases. Prompt: Act like a product designer and create a Flow chart for the process of buying a T-shirt in an e-commerce store: Take into account these error cases: Create only this flow. Do not add anything else. Result: The AI added all error cases but with issues: To address these issues, the author revised the prompt: Revised Prompt: Act like a product designer and create a Flow chart for the process of buying a T-shirt in an e-commerce store: Create only this flow. Do not add anything else. Result: The AI added all error cases correctly, creating a more legible and easy-to-understand flow. Key Insights from the Tests: To Summarize: This insight demonstrated how to use FigJam AI as an AI flowchart tool. The author shared three tests: a happy path flowchart, a flowchart with one error case, and a more complex flowchart with three error cases. After refining the prompts, the desired results were achieved. Key insights from the process were also discussed. Like Related Posts Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. 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