Shipping an AI agent is no longer the hard part. The real test comes after launch, when someone asks whether it improved an outcome the business cares about.
That scrutiny is already changing enterprise deployment plans. In KPMG’s Q2 2026 Global AI Pulse, 49% of organizations said they had delayed or scaled back AI agent deployments when costs began to outweigh the benefits.
A fluent answer alone isn’t proof of value. Teams need to know what the agent understood, what it did, and whether the user ended up better off. That’s where most deployments run into trouble. Agents may know the company’s documentation and the customer’s account history, yet still miss the live experience unfolding inside the product.
The missing layer is product context: the behavioral record of what a user did, what they tried, where they got stuck, and what happened after the agent stepped in.
Product context changes the starting point
Without product context, every conversation starts from zero. The agent waits for the user to describe the problem, asks for background, and searches for the closest answer in its knowledge base. That puts the burden on the person who’s already struggling.
With product context, the agent can see the path into the problem. It knows which page the user visited, which workflow they abandoned, whether they hit an error, and whether they repeatedly tried the same action. Combine those signals with CRM and account data, and the agent can respond to this user, in this account, at this moment—not a generic version of them.
Pendo for Agents makes that behavioral context available to agents teams build or buy, then measures whether the agent’s intervention changed the outcome.
That creates practical gains across support, customer success, and employee workflows.
Resolve the issue before it becomes a ticket
Most support agents are reactive. They help users who know how to ask a clear question, but they miss the person who clicks in circles, types an angry prompt, and closes the tab.
Product context gives the agent a chance to reach out first. Signals such as rage clicks, error clicks, U-turns, page history, and previous attempts can trigger a timely message or walkthrough while the user is still working.
Teachable put this approach into practice by connecting Pendo data to its Fin support agent, Sunny. Pendo identified moments of struggle and passed the relevant context to Sunny, which could open with a specific offer of help. Pendo-initiated conversations achieved a 67% resolution rate, and 86% of proactive conversations were resolved without a ticket. The support team met users in the moment instead of waiting for frustration to spill into the queue.
See churn risk early enough to act
Customer success teams face a related problem. CRM data can confirm that an account is at risk, but often after the behavior has already changed. The earlier signals live in the product: a core workflow goes unused, adoption narrows to one person, or a team stops using the feature that drove the purchase.
When product usage and CRM data work together, teams can detect risk earlier and understand why it’s rising. Pendo can surface those signals inside the tools customer-facing teams already use, then connect the prediction to a human intervention, targeted message, or in-app guide.
Emburse built an early-warning churn system on that principle. The company connected data that had been scattered across four systems, expanded its model from roughly 20 signals to about 700 predictors, and increased coverage without adding CSMs. The value came from turning a risk score into a clear next action.
Put agents where employee work gets stuck
The same pattern applies inside the enterprise. Internal agents may log conversations, but logs alone don’t show whether employees completed the task, abandoned it, or kept repeating the same failed prompt.
By connecting agent interactions to user behavior and business outcomes, teams can identify high-value use cases, detect breakdowns, and improve the experience. Ticketmaster used this approach for an internal agent and reduced rage prompts by 53%, while user retention reached more than 80%. Agent Analytics turns those otherwise silent signals into evidence teams can use to decide where to improve and where to scale.
Measure the agent, then make it act
Across all three use cases, the playbook comes down to two moves. First, measure the agent like a product: who uses it, what they ask it to do, where it fails, and whether it changes the outcome. Then give it enough context to act at the right moment.
An agent’s model determines how it reasons. Product context determines whether that reasoning is relevant to the person in front of it. If you want an agent to survive the ROI review, start there.
Pendo for Agents connects product context to any agent you build or buy, then helps you see what’s working and where it needs improvement. Explore Pendo for Agents.
