Weekly active usage of your reporting feature dropped from 74% to 61% of enterprise accounts over the last 3 weeks. The drop is concentrated in accounts onboarded after March; pre-March accounts are stable. Guide completion on the reporting walkthrough is 22%, well below your 45% baseline, suggesting the onboarding path changed.
An agent watches one thing and acts on it. Not a workflow, just a standing watch that usually does nothing and acts the moment it should.
An agent does what you'd do, and only what you've authorized.
It acts on the same governed metrics as your dashboards, and every action is logged and traceable.
It alerts and recommends on its own; anything that changes data is yours to approve.
Point a new agent at a throwaway channel and watch its judgment before it touches anything real.
It remembers what it already flagged and waits before acting again, so it won't alert you about the same thing twice.
It reconciles your Pendo account and feature usage data against your billing and support data, so you see which accounts are going quiet on the features their plan is priced on. When adoption drops on a high-ARR account, you find out alongside the revenue at risk and the support context, not as an isolated product metric.
When weekly or monthly active usage of a key feature breaks its trend, it tells you which accounts and visitor cohorts are behind the shift, with enough context to decide whether it is an onboarding problem, a discoverability problem, or a product problem. You find out the week it moves, not the quarter it churns.
It watches guide impression and completion rates. When a guide's completion rate drops below baseline or a new guide is not getting the engagement you expected, it flags which guide, which pages it appears on, and which visitor segments are falling off, so you fix the onboarding path before adoption suffers.
Beyond alerts and write-backs, an agent can run arbitrary Python, so it can do whatever the task actually requires: call an API, kick off a job, reshape the data, or wire into your own tooling. The action space is yours to define.
You could rig one of these with a cron job and a Slack webhook in an afternoon. The watching is the easy part. Here's what you'd own forever, and don't, here:
Every Pendo object, modeled and query-ready the moment you connect.
It runs on your real Pendo data (untagged features, guides nobody reviewed, accounts with sparse event data), not a tidy demo.
A message in the channel you choose, with the context and a button to act on it.
A summary in the inbox of the people who need to see it.
A payload to your own systems, to wire the agent into whatever you already run.
A flag written back to your warehouse for everything downstream to pick up.
Kick the question to Fi to investigate the why and propose the fix.
Expose it to your own agents and tools over MCP, and drive it from your stack.
Run it in your own VPC or fully self-hosted. Everything it does is pure SQL and Python you can inspect.
Fi is your AI analyst. It helps you build and customize everything in Definite, including the agents that watch and act.
Your AI analyst. Ask questions in plain English, and let it help you build and customize everything in Definite, including your agents.
Meet Fi →The watchers and actors. Once you've built one, it runs on its own, keeping an eye on what matters and acting the way you would.
Autonomous agents →