This is the question keeping tech leaders up at night. After spending millions on AI tools, product, and IT teams are now scratching their heads, trying to identify the business impact of these investments. 

This problem is not new: software builders and buyers have long tried to understand what users do in-app, how they feel, and whether those efforts pay off. Now, generative AI and agents are shifting how teams measure software’s success. It’s less about measuring clicks, drop-offs, and time spent on pages and more about what kind of outcomes these agents can drive. 

The only way to understand agent performance is by linking usage data to what users do next. Here are six questions you should be asking your AI agent data, and what they mean. 

1. Why are users engaging with my AI agent?

Understanding the “why” behind agent engagement helps you replicate success and fix failure points. If users only engage when they're frustrated with your traditional SaaS product, that's a UX problem, not an AI win. However, if they're opting for agents over human support because it's faster, this is a positive sign.

To understand intent, analyze top use cases, conversation volume, and user context. Track what users were doing before an agent conversation, what alternative paths they had available, and how their engagement patterns differ across user segments and use cases.

You’ll be able to identify: 

  • Whether users choose agents first, or fall back to them as a last resort
  • How engagement patterns differ between power users and newcomers
  • Which use cases connect to your most and least engaged users

2. Are users accomplishing their intended goals? 

AI agents report conversation volumes, but not whether users achieved their goals. To answer this question, you need insight into what users do after engaging with your AI agents across your app.

To find this, track the full journey, from pre-interaction to agent prompt to business outcome. What led users to engage with your support agent in the first place? See when conversations end successfully versus when users give up due to frustration. And make sure to measure downstream behavior: do they complete purchases, finish onboarding, or return to your product? 

Understanding downstream agent impact will also help you answer: 

  • Which use cases lead to successful outcomes vs. bad ones, like dead ends, additional support tickets, rage prompts, and negative feedback
  • How task completion varies by user segment, use case, or the complexity of a request
  • Whether agent interactions help or hurt retention, engagement, stickiness, and adoption

3. Which types of agent interactions drive business outcomes? 

To effectively invest in your AI roadmap, you need to know how agents are helping you increase revenue, cut costs, and reduce risk. That means you need visibility into its top use cases, plus the downstream impact, to do more of what’s working and fix what isn’t. 

To answer this question, analyze conversations by use case. Track which type of conversation correlates with conversions, feature adoption, user expansion, or churn. Which conversations have the highest retention and associated ARR? Which conversations are linked to higher churn and poor NPS scores? 

Asking your AI analytics helps you understand: 

  • Which use cases are linked to higher conversions and retention
  • How agent interactions affect a customer’s lifetime value (LTV) 
  • If agents are accidentally cannibalizing high-value human interactions
  • Which user segments get the most business value from agent help

4. Is your agent changing user behavior? 

In the short term, agents answer questions. In the long term, agents reshape workflows—how users navigate your product, meet their needs, and conduct their work. 

Employees and customers who use agents may discover features more quickly, require less onboarding, or follow a new, unexpected path. Understanding these patterns helps you optimize agent design and the overall product experience.  

To understand this, compare pre- and post-agent user journey patterns. Track how agent interactions influence feature discovery, time-to-value, and product adoption paths. Identify new user behaviors emerging from agent assistance.

Once you answer this question, you’ll be able to understand: 

  • Which new user paths emerge when agents guide navigation
  • How agent-assisted users differ in engagement and retention patterns
  • If your agent is improving overall user sentiment via NPS and CSAT

5. What should you invest in next?

Most teams build agent features based on gut instinct rather than data about what users actually need and use. 

AI roadmaps, guided by real behavioral data, help you reduce wasted R&D spend and roadmap risk. This requires knowing which capabilities are working and which aren’t, so you can properly prioritize, improve, or sunset pieces of your stack.

To determine this, analyze unsupported request patterns to identify high-demand, missing features. Track which existing capabilities drive the most successful outcomes, and spot emerging user needs before competitors do.

What you can finally answer:

  • Which missing capabilities would eliminate the most user friction
  • Which existing agent features deliver the highest business impact
  • What new use cases are emerging from user behavior patterns
  • Where to focus improvement efforts for maximum ROI

6. What kind of data are end-users sharing with agents? 

You can see that users are chatting with agents, but you don’t know if they’re sharing information that could create compliance issues (like customer data, internal processes, competitive intel, or personal details). 

If users are sharing highly sensitive information, you need proper safeguards to prevent future regulatory and compliance issues. If you’re rolling out a new AI agent to your workforce, you need to track: 

  • If users are sharing PII, financial data, or other regulated information
  • How data sharing behavior varies by user role or department
  • Which conversation types trigger users to share sensitive details
  • Whether users trust your agent enough to provide context for complex questions

Track what information users willingly share versus what they protect, and identify patterns in data sharing across different user roles, departments, or use cases.

When your software and AI data live together, you can understand the full impact of your AI investments. Pendo Agent Analytics is the first—and only—solution built to help you do just this. Get a demo to learn more about Agent Analytics.