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§ Agent · Instagram

The Instagram data agent that acts the way you would.

It watches your Instagram content performance alongside your CRM and revenue data, on a schedule you set or whenever fresh data lands. When engagement drops or a content format stops converting, it tells you, or handles it the way you would.

D
DefiniteAPP9:14 AM · #revops-alerts
⚠️ Reel reach down 34% this month; pipeline from Instagram referrals flat at $0

Reel impressions dropped from ~82k to ~54k over the last 30 days while follower growth stalled at +120/wk (your baseline is ~310/wk). Meanwhile, UTM-tagged Instagram traffic has generated zero pipeline in the CRM this quarter, down from 8 opps last quarter.

Review & approve Dismiss
Instagram Media Insights + Account Insights · joined to HubSpot pipeline · 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

AttributionACROSS YOUR SOURCES

Tie Instagram engagement to actual pipeline and revenue

It joins your Instagram reach and engagement data to your CRM and revenue source, so you see which content formats and posting cadences actually produce pipeline, not just likes. When a content type that used to drive traffic stops generating downstream conversions, you find out before the quarter closes.

MediaAccount
Content Performance

Catch a reach or engagement drop before it compounds

When impressions, reach, or saves break their trend on posts or reels, it tells you which content types moved, by how much, and when the shift started. You find out in days, not when someone asks why the social numbers look off in the monthly deck.

Media
Story Engagement

Flag story performance decay before you lose the audience

It watches story impressions, reach, taps, and exits over time. When exit rates climb or reply rates fall below your baseline, it flags which story sets are underperforming and how the trend compares to your last 30 days, so you adjust the format before the algorithm deprioritizes you.

Story
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 Instagram object, modeled and query-ready the moment you connect.

Account
customersales
Media
revenue_financemarketing
Story
marketingengagement

It runs on your real Instagram account (carousel orphans, stories that expired before anyone checked, engagement dips from algorithm changes 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 Instagram, 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 Instagram 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.
Insights shows you engagement numbers inside the app, when you go look. This watches continuously, joins Instagram performance to the CRM and revenue data Instagram never sees, and hands off to Fi to investigate why reach dropped, so you find out the day it shifts, not when someone asks at the monthly review.

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