SMS open and reply rates for these accounts dropped below 10% over the past 3 weeks, down from a 38% baseline. 9 of the 12 also have zero inbound support calls in the same window. All 12 renew in Q3.
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 Twilio call and message data to your CRM and product usage, so you see which accounts have gone quiet, which are escalating through support queues, and which stopped engaging with outreach entirely. The health signal updates with every sync, not once a quarter when someone rebuilds the spreadsheet.
When an account's inbound call volume drops, outbound messages go unanswered, or support queue interactions spike, it flags the account with the renewal date and ARR at stake. Your CSMs get a prioritized call list, not a dashboard they have to remember to check.
It watches your TaskRouter queues for wait times, abandonment rates, and worker occupancy. When a queue breaches your SLA threshold or abandonment spikes, it tells you which queue, how many customers are waiting, and which workers are available, so you rebalance before the service level collapses and accounts start calling their CSM.
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 Twilio object, modeled and query-ready the moment you connect.
It runs on your real Twilio account (subaccounts, test numbers, retry loops, and partial transcriptions), 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 →