Now someone asks the question every product team building AI eventually faces: is it actually helpful?

Not "is it running," your infrastructure team can answer that. The harder question is whether users are engaging with it, whether it's handling what they throw at it, and whether the experience is creating value or quietly frustrating people into giving up.

Those answers don't live in your server logs.

That's the AI observability and adoption gap Pendo uniquely fills. It helps teams understand how users interact with AI agents, what they ask, where agents fail, which use cases drive adoption, and whether agent interactions improve workflows or create new friction. 

And it's a different discipline than what most "agent observability" content covers. Search the term, and you'll find guides on distributed tracing, span capture, token monitoring, and OpenTelemetry standards. 

That's valuable infrastructure for the engineers keeping the agent alive. But product teams need a different layer of visibility: what users are actually doing, if they’re successful , where things break down, and whether any of it impacts outcomes your business cares about.

This post unpacks that layer. The five signals worth tracking from day one, the two places teams get burned when they skip it, and what it looks like when the right visibility is in place.

 AI observability has two definitions

When engineers talk about “agent observability,” or “AI observability,” they mean something specific: tracing every model call, capturing tool invocations, monitoring latency, token cost, and failure rates at the infrastructure level. 

But when product teams talk about this, they mean something a little different: understanding how real users interact with the agent, what they're trying to accomplish, whether the experience is actually meeting their expectations, and whether this AI investment is paying off.

These separate questions for different audiences are complementary, and should live together. Infrastructure observability tells you how your agent is running, and AI agent usage observability tells you whether it's adding value.

The confusion between the two is part of why product teams underinvest in agent observability. They assume that monitoring their engineering counterparts has covered the full picture, but it doesn't. Trace data can tell you that a conversation was completed without an error, not that the user asked the same question four times, didn’t get the answer they needed, and churned from your agent. It can't tell you that a specific type of request consistently goes unanswered, or that one account cohort has never engaged at all.

Those are the signals that drive product decisions. And they require a different kind of observability entirely.

5 AI agent usage signals worth tracking, from the earliest beta phases

Without measurement, you're making roadmap and growth decisions without the data to back them up.

1) Prompt volume and trends

Is usage growing, flattening, or dropping after the launch spike? A retention curve is the most honest signal of whether users find the agent valuable enough to return to. Stagnation after the initial novelty window deserves investigation before it becomes a pattern.

2) Use case distribution

What are users actually asking? This is where most teams encounter their first real surprise. The use cases that get adopted aren't always the ones that were designed for. Understanding the distribution of intents tells you where to deepen coverage, where to improve responses, and occasionally reveals a high-value use case you never anticipated.

3) Failure and frustration signals

Where do users repeat the same prompt back-to-back, hit unsupported responses, or abandon a conversation halfway through? Some of them are swearing at your agent. Some are typing in all caps. They're not filing tickets—they're just expressing their frustration in a text box and hoping something changes.

These signals are more valuable than the occasional voluntary thumbs down. A thumbs down requires a user who's still engaged enough to give you feedback. Rage prompts and all-caps inputs come from users who are genuinely stuck, and they appear whether or not someone decides to rate the experience. They're not random noise either. They cluster around specific gaps in coverage or agent behavior.

4) Adoption by segment

Which accounts or user cohorts engage consistently? Which tried the agent once and disappeared? Account-level adoption data tells you whether you have a reach problem, a quality problem, or both. It also tells you which segments need attention before the next renewal conversation.

5) Agent impact on broader product behavior

Does agent usage correlate with better outcomes across the rest of the product? Does it correlate with faster task completion, lower support volume, and stronger retention? Or does agent activity exist in isolation, disconnected from the outcomes your business actually cares about? The answer to that question is what turns an AI feature into a business case.

Why agent observability matters

Scaling what works

When your agent gains traction with a segment of users, the instinct is to call it a win and move on. But adoption without visibility is fragile.

You need to know which use cases are driving engagement, which user segments are getting the most value, and how agent usage connects to the outcomes that matter (like retention, task completion, and revenue impact). That's how you make the case to invest more, expand coverage, and prioritize the next capability with confidence rather than gut feel.

Fixing what breaks before users give up

This is the harder problem (and it’s also the one most teams underestimate). 

AI isn't perfect. A user comes in, asks a reasonable question, gets a bad or incomplete response, and leaves. They don't file a ticket. They don't fill out a survey. They just don't come back.

That drop-off is the leaky bucket of agentic software, and it's exactly what agentic AI observability at the product level is designed to catch. By the time a complaint surfaces in a QBR, or someone raises their hand on a customer call, you've already lost users who hit the same wall and quietly moved on. The feedback loop that normally catches product problems doesn't work here. The users who have the worst experiences are also the least likely to tell you about them.

The teams that stay ahead of this aren't waiting for feedback to find the problems. They're watching the signals: repeated prompts, unsupported request rates, conversation abandonment patterns. When you can see that a specific type of question is failing consistently, you can act in days rather than quarters. That's not just a better user experience. It's a retention strategy.

What Agent Analytics surfaces as an AI observability platform

Pendo Agent Analytics is built specifically for product teams shipping agents inside their products. It captures how users interact with conversational AI: what they ask, how conversations unfold, where things go wrong, and how agent usage connects to everything else users do in your product.

Here's what it surfaces:

  • Conversations and prompts at scale. See the actual prompts users submit and the full conversation threads, segmented by use case, user type, and account. Not aggregates. The real thing.
  • Automatic use case detection. Agent Analytics groups prompts by semantic similarity to surface the use cases your users actually care about, including the ones you didn't build for. You don't have to manually tag or categorize intent. The patterns emerge on their own.
  • Issue detection and rage prompt tracking. Failures and frustration signals get flagged automatically. When users are repeating themselves, hitting dead ends, or abandoning conversations at the same point, you see it without having to go looking for it.
  • Experiments. Compare different agent configurations, prompts, or coverage changes head to head. Measure what actually improves outcomes rather than what you think might.
  • Cross-product context. Agent Analytics lives inside Pendo, which means you can connect agent behavior to everything else users do in your product. See what users do before and after agent interactions, how agent usage correlates with retention and task completion, and where agents fit into the broader product journey.

As Christopher Penney, a product leader at OSAIC, put it:

"Agent Analytics opens up an entirely new level of insight into customer needs. What they're struggling with, what they're asking about, without having to ask them directly."

Most teams find out too late that launch-day metrics don't hold because they didn't have the observability layer in place to catch the signals before they turned into churn.

See how Pendo Agent Analytics gives you the full picture, from first prompt to business outcome.