Breach rate was steady at ~7% over the prior four weeks. The spike is concentrated in the Enterprise tier, mostly conversations tagged billing-dispute routed to a team running at 140% capacity.
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 Kustomer conversations and customer records to your billing and CRM data, so you see which open tickets belong to your highest-value accounts, how resolution time correlates with churn, and where the gaps are before leadership asks.
When your breach rate breaks its trend, it tells you which teams and tags are affected, how far past target the conversations have drifted, and what changed (staffing, volume, routing) so you can fix the root cause, not just the symptom.
It tracks first-response time, resolution count, and message volume per agent and team, flags when someone is carrying twice the load or when a queue is backing up, and gives you the data to rebalance before service quality drops.
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 Kustomer object, modeled and query-ready the moment you connect.
It runs on your real Kustomer account (re-opened tickets, SLA policy changes, routing overrides 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 →