Delivery rate on your primary sending domain dropped from 97.8% to 91.4% over the last 5 days. Bounce rate spiked from 1.2% to 6.1%, concentrated on Gmail recipients. The shift started Thursday after the DNS change. Roughly 3,200 transactional emails per day are not reaching inboxes, and your suppression list grew by 840 addresses this week, well above the ~120/wk baseline.
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 Mailgun open, click, and delivery events to your product usage and revenue data, so you see which email flows actually produce activated users and downstream revenue, not just delivery counts. When a transactional flow that used to drive activation stops converting, you find out before the cohort churns.
When bounce rates, complaint rates, or failure rates move on a domain or recipient segment, it tells you which sending domain is affected and how many emails are failing. You find out the day it shifts, not when support tickets reveal half your users never got their password reset.
It watches your bounces, complaints, and unsubscribes over time. When your suppression list growth accelerates past its baseline, it flags which domain and which recipient segments are driving the increase, so you can clean your lists before sender reputation takes the hit.
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 Mailgun object, modeled and query-ready the moment you connect.
It runs on your real Mailgun account (bounced addresses, spam complaints, test-domain leakage 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 →