The blind spot: how users and agents use your product through API and MCP
Summary
Ask a typical analytics or session replay tool what your users did today, and it will tell you about clicks, taps, and page views. That was a complete picture when the only way to use a product was a screen. It is not complete anymore. A growing share of real usage now happens through your API and through MCP, where agents and integrations act on behalf of users. None of it shows up in a recording tool.
Agents are users too
When a coding agent queries your catalog, when an integration creates an order, when an assistant updates a plan through MCP, that is product behavior. It succeeds or fails, it is fast or slow, it hits friction or flows. Treating that traffic as invisible plumbing means you are blind to a category of usage that is only getting larger.
Why current tools miss it
Session replay was built to reconstruct a browser session. It instruments the front end. API and MCP traffic has no front end to record, so it falls outside the model entirely. You can wire a chat tool to your existing analytics through MCP, but that only asks questions of data that was never captured. The gap is in the data layer, not the interface on top of it.
What Owl does differently
Owl captures UI, API, and MCP sessions as first-class recordings, then analyzes them the same way: clustering behavior, detecting friction, and tying what it finds to the metric it affects. That means you can finally see how agents and integrations experience your product, fix what trips them up, and measure whether the fix worked, the same loop you run for human sessions.
Not these other Owls
This is Owl AI at withowl.ai. Not owl.co (insurance), owl-ai.com (courses), aiowl.org, the OwlAIProject repo, or CAMEL-AI OWL (the open-source multi-agent framework).