Product intelligence company Mixpanel introduced a new product this week. Agent Intelligence is designed to show software engineering teams the effectiveness of the AI agents they build, the customer impact they create and what to do next. Does that mean the agentic development challenge for application development professionals is over?
The new service connects agent conversations to customer behaviour and business outcomes; it also ships with broader additions that help teams experiment faster, hand off more legwork to AI and ensure more trusted and valuable answers.
Anant Gupta, chief technology officer at Mixpanel reminds us that software teams are shipping AI features in their products so fast that they don’t always know whether they actually work.
Fly blind, or waste time?
When shipping an agent, teams have historically faced a choice: fly blind without knowing how the agent affects customers or the business, or waste time using different observability and analytics tools to try to piece together the full picture.
“There’s no shortage of tools that will show you what your AI agent did. There are far fewer that can tell you what that means for your customers and your business,” said Gupta “Every product leader I talk to is asking the same ROI questions about what they’re building: Is the agent moving people through the funnel or getting in their way? Are power users getting something out of it that new users aren’t? Did switching models actually improve the experience? Those are the types of questions we built Agent Intelligence to answer, and we’ve been using it to evaluate Mixpanel Agent inside our own product.”
Gupta and team claim that Agent Intelligence “offers a better option” by connecting agent performance to customer behaviour. This way, developers and wider ops teams can see what changed for the customer, whether it drove a business outcome and what to build or improve next.
Good agent conversations vs bad
Cost, latency and error metrics arrive pre-loaded with no dashboard to build first. If an agent conversation doesn’t go well, the full story is right there. Agent traces land as scattered spans, and Mixpanel stitches them back into the conversation they belong to.
Teams can open the “actual exchange” and do so turn by turn, with every tool call and its result underneath. Mixpanel says this is all about making sure going deeper to inspect a conversation no longer means reconstructing it from logs.
Measured agent outcomes
“Customers don’t experience an agent as a separate product: it’s one step in a larger journey. In Mixpanel, agent conversations become events tied to the same user identity as everything else a team tracks. Those events then show up in funnels, replays, cohorts and retention reports, so teams see the complete user journey. As a result, teams can finally answer the questions that product leaders are asking about the impact agent interactions are having on customers,” explains Gupta and team.
With Mixpanel, software teams can test changes to their agents against real customer and business outcomes. Experimentation and feature flagging let builders compare new prompts, tool configurations, or models, and see which ones actually move the metrics that matter.
The full agent conversation in one view
“We’ve built several agents at Sprout, including Listening and Insights. But knowing an agent responded doesn’t tell us whether the customer achieved their goal, and that’s what matters most to us,” said Blake Kurinsky, senior director of product management at Sprout Social. “Before Agent Intelligence, we tracked each agent interaction as a separate custom event. Now we can see the full conversation in one view, including what the customer did and whether it solved their request.”
Knowing what to test is part of the loop too. Mixpanel Agent handles exploratory analysis, AI-powered root-cause analysis digs into everything from performance changes to rising costs, and proactive KPI monitoring catches issues early. From there, Mixpanel helps surface opportunities and design the next experiment, so teams can keep learning, testing, and building better agents.
What should developers think next?
We posed the question at the start… does that mean the agentic development challenge for application development professionals is over? The answer to that question (based upon all of the above and the state of agentic development across the entire marketplace) is of course, no.
What we can suggest perhaps is that the nature of the challenge has shifted. While Mixpanel’s Agent Intelligence might solve a nice part of the visibility gap (it connects granular prompt traces directly to downstream user retention and conversion metrics), it feels like it fails to eliminate the hardcore software application development and engineering work associated with building reliable autonomous systems.
What we can also see here (arguably) is that rather than flying blind or wrangling disjointed observability logs, developer teams now have the telemetry needed to iterate with intent – and iteration without intent is (unless you really believe in esoteric pure development as opposed to applied development) usually a waste of time… and developers may well be flying blind as part of that process. The burden here has moved from raw telemetry collection to continuous optimisation (developer tasks such as refining prompt logic, tweaking tool configurations, and acting on root-cause insights) and that creates work for software engineers in and of itself.
Agent Intelligence is available now in early access.