Deploying agents is table stakes in 2026. Plug and Play’s 2026 pulse of the world’s largest companies found roughly three-quarters now run at least one AI system in production, and nearly all are piloting or further along. But it also found that half of those same companies can’t say whether any of it worked.

The real battle is for user trust and loyalty

What separates the companies pulling ahead is whether users actually get value from what they’ve shipped. An agent can run flawlessly and still leave the user no better off.

Proof of value is hard for a specific reason: two gaps sit on either side of every agent you deploy. Going in, your agent doesn’t know your user. Where are they in their journey? What are they stuck on? What have they set out to do? Coming out, you can’t see how it’s being used, whether it answered well, or where it broke down.

Close neither gap and you get what’s filling enterprise tech stacks right now: slop. Agents that are built fast and cheap, without judgment, taste, or product context. Zombie agents that run loose, that nobody measures and nobody owns. It’s no surprise recent surveys have found 1 in every 4 dollars spent on AI is wasted. That number will only grow with every sprint you can’t measure.

Speed made building easy and proof hard

AI may have collapsed the cost of shipping software, but companies are still hard pressed to prove that software works. And the gap between what companies spend on AI and what people actually use keeps widening. Gartner puts the money lost from this at $116 billion.

Every measurement problem traces back to one missing input: product context. At Pendo, we’ve described this as the record of what your users and your agents actually do in-app: the quantitative and qualitative data, tracked from your product’s first use. Without it, every team invents its own definition of “working,” and none of them roll up into a number your CFO can compare or use to evaluate an agent’s actual impact. Enterprise buyers already feel this: they now weigh explainability and defensibility above raw performance when they choose a vendor.

How to build agents that actually support your users

Product context is what creates proactive, self-improving agents: user, agent, and business data in one place, so the teams building on AI can ship experiences that create value, not more slop. The operating model is straightforward: understand what’s happening in-app, empower your agents to act on it, and keep improving your product experience.

Agent Analytics: Prove it works

With Pendo Agent Analytics, you can see who’s using your agent and for what, catch failed interactions before they turn into churn, and measure whether it drives real outcomes like task completion. Ticketmaster built an internal agent and had no way to know if it was landing. With Agent Analytics, the team cut rage prompts by 53%, reached 82% user retention, and rolled out to more people with the metrics to back the decision.

Agent Toolkit: Make it act

Pendo’s Agent Toolkit lets you connect your product data to any agent, built or bought, so it steps in at the right moment, whether it’s surfacing the right guide, catching a churn risk early, nudging a user back to a workflow they abandoned. Teachable wired its support agent to Pendo context and made it proactive. Now 67% of Pendo-initiated tickets get resolved by the agent, with a 200% jump in reply rates.

Command Center: Govern the portfolio

With Pendo Command Center, enterprises have one place to oversee every agent they’re running, retire the zombies, and cut the low-value tools so you can reinvest in what works. When you can see the whole portfolio at once, slop stops compounding and the agents earning their keep get room to grow.

The future belongs to agents that drive value

The companies that win the battle for the user won’t be the ones who shipped the most agents, but the ones who can show, in numbers anyone trusts, that their agents left users and the business better off than before. That’s what Pendo is built for: providing you the product context behind every user and every agent, so you can build what people love and prove it paid off.

To learn more about how Pendo brings every agent the context to perform better, get a demo here.