Call volume on deals closing this quarter dropped 31% week-over-week. Six deals worth $389K combined have had zero recorded calls in the last 10 days, against a baseline of at least one call per week at this stage.
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 Gong call data to your CRM opportunities so you can see which deals have real conversation momentum and which have gone quiet. When pipeline coverage slips or deal activity diverges from what the forecast assumes, it flags the gap before the commit call, not after.
When a rep's talk ratio, question rate, or monologue length drifts outside the team baseline, it flags the pattern with the specific calls that drove it. You get the signal before it shows up as a missed quarter.
It tracks call frequency and participant coverage on every open deal. When a deal that should be in active conversation goes silent, or when the right stakeholders stop showing up, it surfaces the risk with enough context to act on it.
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 Gong object, modeled and query-ready the moment you connect.
It runs on your real Gong account (internal calls, test recordings, partial transcripts 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 →