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§ Agent · Kanbanize

The Kanbanize data agent that acts the way you would.

It keeps an eye on your Kanbanize boards and cards alongside the rest of your ops data, on a schedule you set or whenever fresh data lands. When something needs attention, it tells you, or handles it the way you'd want.

D
DefiniteAPP9:14 AM · #ops-alerts
⚠️ 12 cards blocked longer than 48 hours, 3 are upstream of this week's release

Blocker count jumped from a baseline of 2-3 to 12 since Monday. Three of those sit on the critical path for the Q3 launch epic, and average cycle time on the affected board is already 40% above its 30-day norm.

Review & approve Dismiss
Kanbanize Cards + Blockers + Workflow Stages · joined to deploy history and CRM milestones · 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

ReconcileACROSS YOUR SOURCES

Tie board throughput to what actually shipped and closed

It joins your Kanbanize cards and workflow stages to your deploy history and CRM data, so a card dragged to Done that never made it to production stops reading as delivered. You find out which work actually landed for customers, not just which cards moved columns.

Card (Task)Workflow StageBoard
Bottlenecks

Catch a workflow stage piling up before it stalls the team

When WIP in a column breaks its trend, it tells you which cards are accumulating, who owns them, and how far cycle time has drifted from your baseline. You hear it while there is still time to rebalance, not after the sprint retrospective.

Card (Task)Workflow StageAssignee
Blockers

Surface blocked work with the cost of waiting

It watches for cards sitting in a blocked state, tracks how long each one has been stuck, and flags the ones that sit on a critical path or affect downstream deliverables. It routes each blocker to the right owner with the context they need to unblock it.

Card (Task)BlockerTask Relationship
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 Kanbanize object, modeled and query-ready the moment you connect.

Card (Task)
customersales
Board
salesmarketing
Workflow Stage
salesoperations
Assignee
customermarketing
Tag
customermarketing
Blocker
support
Time Tracking
customermarketing
Task Relationship
salessupport
Custom Field
customermarketing

It runs on your real Kanbanize account (stale cards, inconsistent tags, half-filled custom fields 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 Kanbanize, 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 Kanbanize 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 report on Kanbanize in isolation, when you go look. This watches continuously, joins your board data to deploys, CRM, and support, and hands off to Fi to investigate why a workflow stage is piling up, so you find out before it stalls the team, not after.

Your answer engine
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