Pendo for Agents equips AI agents with real-time context, allowing proactive resolution of customer issues at the moment they emerge. The platform analyzes user behavior data to supply each agent with actionable insights. Product teams receive reliable metrics on agent performance and ROI, while low-quality interactions are detected and reduced. Native integration with Fin, Decagon and Sierra plus analysis of millions of conversations streamlines support costs and improves user experience.
Table of Contents: What awaits you in this article
Toolkit empowers AI agents with real time behavior insights
The new Agent Toolkit supplies AI agents with real-time user behavior data streams, enabling proactive issue anticipation and resolution before users initiate requests. Integration within existing applications provides contextual insights that allow him to deliver personalized interactions at critical moments, enhancing engagement. Developers can embed the toolkit into custom platforms or integrate it with popular solutions, enhancing diagnostic accuracy and optimizing the overall user experience by leveraging precise behavioral signals.
Pendo Agent Analytics analyzes 47M conversations for roadmap improvements
Agent Analytics is Pendos established analytics tool designed to track rigorous performance metrics of AI agents in real time. During the last twelve months it processed over forty-seven million agent conversations across nearly five hundred organizations. The aggregated intelligence informs product managers, equipping him with actionable insights derived from user engagements. By quantifying recurring issues and interaction patterns, it enables strategic roadmap planning based on empirical evidence instead of assumptions.
Agent Toolkit and Analytics cut support costs, inform roadmaps
By combining Agent Toolkit with Agent Analytics, support teams reduce operational costs through proactive issue detection and automated resolution workflows. The solution prevents reactive escalations by identifying emerging user problems early and addressing them before they escalate. It also provides detailed insights to inform product roadmaps based on real customer behavior and interaction patterns. This data-driven strategy minimizes resource waste, improves development prioritization and elevates return on investment from AI.
Global AI agent software spend reaches $376.3B by 2027
According to Gartner, global expenditure on AI agent software is projected to reach 376.3 billion dollars by 2027, reflecting rapid market growth. At the same time, analysts anticipate that more than forty percent of initiatives will be terminated prematurely due to ambiguous business value and escalating operational costs. Pendo emphasizes that absence of real-time context results in so-called “zombie agentsâ that consume resources and budgets without delivering outputs or engagement.
Pendo for Agents integrates Fin, Decagon and Sierra seamlessly
Pendo for Agents integrates seamlessly with leading AI platforms such as Fin, Decagon, and Sierra alongside bespoke agent implementations. Since mid 2026, pilot client Teachable has leveraged Pendo for Agents to supply Fin with anticipatory user friction data before it would otherwise generate support tickets. This proactive approach enables Teachable to resolve 86 percent of interactions without human intervention, achieving a customer experience rating of four out of five stars.
Agents resolve issues instantly, freeing staff for complex support
For CX and IT teams, Pendo for Agents delivers proactivity by empowering support agents to detect and resolve issues as soon as they arise. This proactive intervention reduces ticket volumes and allows human agents to dedicate expertise to complex inquiries. Meanwhile, product teams gain insights into user expectations and problem clusters on an account level. Data-driven findings facilitate strategic roadmap planning, enabling precise prioritization of future agent feature enhancements.
By integrating real-time context with analytics, Pendo for Agents accelerates issue detection and resolution, enabling agents to anticipate user challenges before they emerge. The platform consolidates usage data into proactive insights, reducing support ticket volume and automating fixes. With strategic product roadmapping guidance, it eliminates low-value agents, prevents zombie deployments, optimizes investment decisions and maximizes return on AI initiatives. This solution drives sustainable cost savings and enhances end user satisfaction.
