June invoice is projecting $8,400 over your $12,200/mo baseline, driven by cache-miss rates climbing on api-prod and static-assets since the June 9 deploy. Bandwidth line items are 3.2x their 30-day average.
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 Fastly invoice line items to service-level traffic metrics and your infrastructure data, so you can see which services, endpoints, or deploys drove the bill. You stop guessing why the invoice jumped and start tracing it to the commit.
When your cache-hit ratio drops or miss rate breaks its trend on any service, it tells you which service moved, when the shift started, and how much extra origin traffic it is generating. You find out in hours, not when the invoice arrives.
When error-rate status codes climb on a service, it surfaces which service, the error distribution, and when it started, and routes it to the right person before users start filing tickets.
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 Fastly object, modeled and query-ready the moment you connect.
It runs on your real Fastly account (mid-cycle invoices, WAF noise, test services 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 →