Beacon Health opened 21 messaging conversations in the last 10 days (your baseline is ~5/wk), average CSAT fell from 4.1 to 2.8, and their renewal lands in 38 days. They are not on this week's CSM call list yet.
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 joins your LivePerson conversations and outcomes to the account, ARR, and renewal date in your CRM, so a spike in support volume from an account renewing next quarter surfaces as a ranked call list, not a number nobody connected. You catch the slide while there is still time to act on it.
When wait times or backlog break their trend for a skill, it tells you which queues are affected, how far SLA attainment has drifted, and whether the pattern is a staffing gap or a volume spike. You see it the day it opens, not at the end of the quarter in a CSAT report.
It watches agent activity and occupancy by group, and flags it when one team's idle time climbs while another's backlog grows. You rebalance before response times slip and the accounts behind those conversations start to feel it.
Beyond alerts and write-backs, an agent can run arbitrary Python, so it can do whatever the task actually requires: call an API, push a call list to your CS tool, 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 LivePerson object, modeled and query-ready the moment you connect.
It runs on your real LivePerson account (bot sessions, transferred conversations, abandoned chats and all), 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 →