Presented by Zendesk
Agentic AI is currently transforming three key areas of work — creative, coding, and support — says Shashi Upadhyay, president of engineering, AI, and product at Zendesk. But he notes that support presents a distinct challenge.
“Support is special because you’re putting an autonomous AI agent right in front of your customer,” Upadhyay says. “You have to be confident that it’s going to do the right thing for the customer and by the customer. Every step forward in AI should make service more dependable for both customers and human agents.”
Zendesk, recently named a Leader in the 2025 Gartner Magic Quadrant for the CRM Customer Engagement Center, started implementing AI agents about a year and a half ago. Since then, they’ve seen that AI agents can solve almost 80% of all incoming customer requests on their own. For the remaining 20%, the AI agent can hand it over to a human to help solve the more complex problems.
“Autonomous AI agents work 24/7, with no wait or queue time. You have a problem; they provide an answer right away. All of that adds up,” he says. “Not only do you get higher resolutions, higher automation, but you can also improve the CSAT at the same time. Because 80% is such a promising number, and the results are so solid, we believe it’s only a matter of time before everyone adopts this technology. We already see that across the board.”
The company’s efforts to advance its standard of usability, depth of insight, and time to value for organizations of all sizes require continuous testing, integration of advanced models like ChatGPT-5, and a major upgrade of its analytics capabilities and real-time, gen AI–powered insights with the acquisition of HyperArc, an AI-native analytics platform.
Designing, testing, and deploying a better agent
“In a support context especially, it’s important AI agents behave consistently with the brand of the company, policies, and regulatory requirements you may have,” Upadhyay says. “We test every agent, every model continuously across all our customers. We do it before we release it and we do it after we release it, across five categories.”
Those categories — automation rate, execution, precision, latency, and safety — form the foundation of Zendesk’s ongoing benchmarking program. Each model is scored on how accurately it resolves issues, how well it follows instructions, how fast it responds, and whether it stays within clearly defined guardrails. The goal isn’t just to make AI faster — it’s to make it dependable, accountable, and aligned with the standards that define great customer service.
That testing is reinforced by Zendesk’s QA agent — an automated monitor that keeps a constant eye on every conversation. If an exchange starts to drift off course, whether in tone or accuracy, the system immediately flags it and alerts a human agent to step in. It’s an added layer of assurance that keeps the customer experience on track, even when AI is running the first line of support.
GPT-5 for next-level agents
In the world of support and service, the move from simple chatbots that answer basic queries or solve uncomplicated problems, to agents that actually take action, is groundbreaking. An agent that can understand that a customer wants to return an item, confirm whether it’s eligible for a return, process the return, and issue a refund, is a powerful upgrade. With the introduction of ChatGPT-5, Zendesk recognized an opportunity to integrate that ability into its Resolution Platform.
“We worked very closely with OpenAI because GPT-5 was a pretty big improvement in model capabilities, going from being able to answer questions, to being able to reason and take action,” Upadhyay says. “First, it does a much better job at solving problems autonomously. Secondly, it’s much better at understanding your intent, which improves the customer experience because you feel understood. Last but not least, it has 95%-plus reliability on executing correctly.”
Those gains ripple across Zendesk’s AI agents, Copilot, and App Builder. GPT-5 cuts workflow failures by 30%, thanks to its ability to adapt to unexpected complexity without losing context, and reduces fallback escalations by more than 20%, with more complete and accurate responses. The result: faster resolutions, fewer hand-offs, and AI that behaves more like a seasoned support professional than a scripted assistant.
Plus, GPT-5 is better at handling ambiguity, and able to clarify vague customer input, which improves routing and increases automated workflows in over 65% of conversations. It has greater accuracy across five languages, and makes agents more productive with more concise, contextually relevant answers that align with tone guidelines.
And in App Builder, GPT-5 delivered 25% to 30% faster overall performance, with more prompt iterations per minute, speeding app builder development workflows.
Filling in the analytics gap
Traditionally, support analytics has focused on structured data — the kind that fits neatly into a table: when a ticket was opened, who handled it, how long it took to resolve, and when it was closed. But the most valuable insights often live in unstructured data — the conversations themselves, spread across email, chat, voice, and messaging apps like WhatsApp.
“Customers often don’t realize how much intelligence sits in their support interactions,” Upadhyay says. “What we’re pushing for with analytics is ways in which we can improve the entire company with the insights that are sitting in support data.”
To surface those deeper insights, Zendesk turned to HyperArc, an AI-native analytics company known for its proprietary HyperGraph engine and generative-AI-powered insights. The acquisition gave new life to Explore, Zendesk’s analytics platform, transforming it into a modern solution capable of merging structured and unstructured data, supporting conversational interfaces, and drawing on persistent memory to use past interactions as context for new queries.
“Your support interactions are telling you everything that’s not working in your business today, all that information is sitting in these millions of tickets that you’ve collected over time,” Upadhyay says. “We wanted to make that completely visible. Now we have this genius AI agent that can analyze it all and come back with explicit recommendations. That doesn’t just improve support. It improves the entire company.”
That visibility now translates into actionable intelligence. The system can pinpoint where issues are most persistent, identify the patterns behind them, and suggest ways to resolve them. It can even anticipate problems before they happen. During high-pressure events like Black Friday, for example, it can analyze historical data to flag recurring issues, predict where new bottlenecks might appear, and recommend preventive measures — turning reactive support into proactive strategy.
“That’s where HyperArc shines,” Upadhyay says. It doesn’t just help you understand the past — it helps you plan better for the future.”
By integrating HyperArc’s AI-native intelligence, Zendesk is moving customer service toward continuous learning — where every interaction builds trust and sharpens performance, setting the stage for AI that can see what’s coming next.
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