As a kid, I loved going to football games with my dad. The huge stadium, the noise, the giant sodas, the Astrodome. (Okay, maybe it was mostly the sodas). One Sunday I watched the Oilers play for the last time. I just didn’t know it yet. The signs were there: a half-empty stadium, fans booing. But I just wanted to watch Eddie George run. After the season, the team packed up and moved to Nashville. See ya, Houston Oilers. The Astros became my new muse, and they won far more than the team that left ever did.
Some of you may have done something for the last time without seeing the signs. Personal AI agents that can search, compare, and check out on your behalf mean a permanently new way to shop. You still need groceries. You still want that vacation. You’ve just stopped doing the parts of shopping that don’t serve you.
The same thing is happening at your company in your day to day work. The people who build your digital experience are doing manual tasks for the last time, handing off the parts of their job that don’t serve them either. Some teams will use that time to move faster than they ever have. Others will spend it double-checking agents that are confidently wrong.
What the work looks like now
For 20 years, digital teams improved their experience through different tasks: build the segment, watch the sessions, write the summary. Agents now do those parts better and faster. What’s left is the work that was always most valuable: judgment. Deciding what matters, asking the next question, and judging whether the answer is right.
Product managers and UX designers used to watch sessions for hours, hoping to spot a pattern. Now agents do the watching. With Agentic Session Review, AI reviews the sessions and surfaces what’s happening across them. The PM’s job is knowing when that pattern doesn’t match what they know about their customers.
Engineers used to get a bug report, reproduce it, size it, and then argue about whether it made the sprint. Now many engineers start in Claude or Cursor. Fullstory MCP takes them from the error to every affected session to a ticket in minutes, and some teams feed that straight into a draft pull request. The engineer’s job shifts to deciding what ships and when.
In both cases, the agent does the legwork and the expert makes the call. The goal isn’t more agents. It’s agents and people working from what actually happened.
Fast and wrong is still wrong
Of course, it’s not that simple. You don’t just hit the AI button and solve all your problems (yet?). An agent never says “I’m not sure.” It answers a question it can’t fully see with the same confidence as one it gets exactly right.
It’s already showing up in the numbers. In our own research, 80% of digital experience pros said AI has increased their review workload. The tool meant to save time created a second job: checking the tool. And 65% of teams said they’d misdiagnosed digital friction and shipped the wrong fix. Agents can now make that same mistake at scale.
The model usually isn’t what broke. The context is. Agents fill gaps with guesses, especially when they can only see what someone thought to tag. Good agents need complete context going in and evidence coming out: the sessions behind the answer, not just the answer.
Rewiring Fullstory to help teams move faster
This year, we introduced two ways to give agents that context that our costumers have adopted in spades:
Agents that work inside Fullstory. StoryAI Agents take on the jobs nobody should do by hand: checking every release, watching key funnels, and finding the exact sessions behind an issue. They run without a prompt and post what they find to Slack or Teams. Because they work from Fullcapture, they start with everything that happened, not just what someone remembered to tag. Every finding links back to its sessions, so your team can check it in a click. As one early customer put it, “That was like two minutes there and saved me a lot of time.”
Agents you bring with you. When your engineers are already working in Claude or Cursor, Fullstory MCP gives their agents the same ground truth. With Agentic Session Review, the agent doesn’t just count events. It watches sessions.
The two work together. The MCP brings your AI to Fullstory, and StoryAI Agents bring Fullstory’s own agents to the job. “Even our MCP was able to use StoryAI and get a better fuller picture,” one customer told us.
Your customers sent someone else
Now consider that shopping trip again. Our data science team sent a personal AI agent on a grocery run on a test site to see how an agent shops compared to a person. To the retailer, it looked like any other visit. But the signs were there: a pointer that never curved, and none of the pauses a person takes to think.
Your customers aren’t leaving your site. Some of them are just sending someone else. Their agents are getting better at shopping. Your team’s agents are getting better at fixing. Both are only as good as what they can actually see. The same behavioral data is how we’re learning to spot when the visitor on your site is an agent, not a person.
When the Oilers left town, I didn’t stop loving sports, I just found a new game to love. Your team’s work isn’t ending either. It’s becoming something better.
The companies that win won’t have the most agents. They’ll have the clearest view of what’s really happening.
Fullstory customers will show you what that looks like at #Bash_, our virtual customer event, on November 10. You’ll see StoryAI Agents and Fullstory MCP at work, hear from the teams already using them, and get a first look at what it means to build for visitors who aren’t human.
