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§ Agent · Totango

The Totango data agent that acts the way you would.

It watches your Totango data alongside your product usage and billing, on a schedule you set or whenever fresh data lands. When an account needs attention, it tells you, or handles it the way you'd want.

D
DefiniteAPP9:14 AM · #cs-alerts
⚠️ 12 accounts dropped below health threshold this week, $87k ARR at risk

Usage events for these accounts fell 40%+ over the past 14 days while their renewal dates are within 45 days. Three are enterprise tier. Historically, accounts with this pattern churn at 3x your baseline rate.

Review & approve outreach Dismiss
Totango Accounts + Events + Person activity, reconciled to billing data, audit log

How an agent works

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.

◄ repeats on the schedule you set ►

You stay in control

An agent does what you'd do, and only what you've authorized.

The same trusted numbers

It acts on the same governed metrics as your dashboards, and every action is logged and traceable.

You approve anything that writes

It alerts and recommends on its own; anything that changes data is yours to approve.

Try it on a test channel first

Point a new agent at a throwaway channel and watch its judgment before it touches anything real.

No false alarms

It remembers what it already flagged and waits before acting again, so it won't alert you about the same thing twice.

What you can put an agent on

At-riskACROSS YOUR SOURCES

Spot churn risk before the renewal conversation

It joins Totango health scores and usage events against your billing and product data, ranks accounts by risk and revenue exposure, and pushes a prioritized call list to your team each week, so CSMs know which accounts to reach out to before the renewal window opens.

AccountEvent
Engagement

Flag accounts going quiet before they go dark

When a customer's event activity drops below its own baseline, the agent surfaces who stopped logging in, which features they abandoned, and how far they are from renewal, so your team can intervene while there is still time to re-engage.

PersonEvent
Expansion

Surface expansion signal from usage patterns

It watches for accounts whose person count or event volume is outgrowing their current tier, correlates that against contract terms from your billing system, and flags the ones worth an expansion conversation this quarter.

AccountPersonEvent
Custom

Run any Python it needs to get the job done

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.

Why not just build it yourself?

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:

  • The cross-source join: not one tool's data, but it reconciled against the rest of your stack
  • A trusted, consistent metric: the same number your dashboards use
  • The investigation into why, when something fires
  • A full audit trail of everything it did
  • The upkeep, when the schema drifts or the script breaks at 2am

The data it works from

Every Totango object, modeled and query-ready the moment you connect.

Account
customersales
Person
customermarketing
Event
customersales

It runs on your real Totango instance (stale health scores, orphaned accounts, duplicate contacts and all), not a tidy demo.

Where it acts

Slack

A message in the channel you choose, with the context and a button to act on it.

Email

A summary in the inbox of the people who need to see it.

Webhook

A payload to your own systems, to wire the agent into whatever you already run.

Warehouse write-back

A flag written back to your warehouse for everything downstream to pick up.

Hand off to Fi

Kick the question to Fi to investigate the why and propose the fix.

MCP

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.

Build your agents with Fi

Fi is your AI analyst. It helps you build and customize everything in Definite, including the agents that watch and act.

Fi

Your AI analyst. Ask questions in plain English, and let it help you build and customize everything in Definite, including your agents.

Meet Fi →

Agents

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 →

Get started

  1. 1Connect Totango, and the sources it needs to reconcile against. Synced and modeled in an afternoon.
  2. 2See the numbers tie out to what you already trust.
  3. 3Put an agent on one thing you can't afford to miss. Fi helps you build it.
§ FAQ

Common questions

You set the schedule, and it also re-checks whenever fresh Totango data lands. Each agent watches the one thing you point it at, nothing else.
It alerts and recommends on its own. Anything that writes, whether to a tool, your warehouse, or a customer, is yours to approve. You can also point a new agent at a test channel first and watch its judgment before it touches anything real.
When something fires, it can hand off to Fi to investigate, drilling into the data it has across your connected sources to find what's behind the move, and showing its work.
Those score accounts inside Totango based on rules you configure. This watches continuously, reasons across Totango plus your billing and product usage data, and hands off to Fi to investigate why a score dropped, so your team finds out before the renewal, not after.

Your answer engine
is one afternoon away.

Book a 30-minute call and watch us build your first dashboard live, with your own data.