KPI Tree

For agents

Agents that already know your business.

Canopy is what external agents connect to. Canopy Agents are the ones KPI Tree runs for you: platform agents working out of the box on your metrics, with your permissions, plus your own agents deployed on the same context.

Canopy
Canopy Agents
Canopy Agent Workflows

Working from day one. Governed from day one.

Four agents, out of the box. Platform agents

Personalised action plans, RACI assignment, canvas edits and Slack answers, with no setup beyond connecting your data.

Grounded, governed, gated. Trust built in

Your permissions on every run, approval gates where you want them, and escalation up your real org chart. Agents know when to act and when to ask.

Any model. Your keys. Your caps. Cost under control

Claude, OpenAI or Gemini per run, bring your own keys, workspace spend caps and per-agent cost tracking.

Platform agents, working on day one

Personalised Action Plan identifies declining metrics, their drivers, and the specific actions that fall to you under your RACI. Update RACI Assignments finds unowned metrics and proposes Responsible, Accountable, Consulted and Informed owners with reasoning. Canvas Assistant shapes the metric tree itself. And the Slack Assistant answers and acts on metric questions where your team already talks.

Personalised Action Plan

Declining metrics, their drivers, and the actions that fall to you.

Run

Update RACI Assignments

Finds unowned metrics and proposes owners, with reasoning.

Run

Canvas Assistant

Shapes the metric tree: search, add, connect, rename.

Run

Slack Assistant

Answers and acts on metric questions in Slack.

Run

Trained on your context, not the internet

Every agent reads Canopy: the statistically tested causal model, the live org chart and RACI, verified outcome history, budgets and plans. So an agent's recommendation is the same plan a well-briefed person would produce, grounded in which levers have actually moved which numbers in this business.

Ecosystem diagram loading

Knows when to act, and when to ask

Actions can sit behind approval gates: the agent proposes, a named person approves, rejections branch to their own path. When nobody responds, escalation follows your real reporting lines. You choose where the line sits between suggest, approve and act.

Run #482 · Revenue target missedtoday, 09:00
Trigger
Completed
On target missedMetrics
Completed
Run action-plan agentAgents
Pending
Wait for Sarah's approvalUtilities
Waiting on Sarah. If nobody responds, this escalates to her manager automatically.

Your permissions, honoured on every run

Agents act on your behalf with your permissions, so what an agent sees is exactly what you see, metric by metric. There is no separate service account with god-mode access, and nothing to audit beyond the access model you already govern.

Conversion rate

Marketing · daily

R
A
C
+4
I

Any model. Your keys. Your caps.

Run agents on Claude, OpenAI or Gemini and switch models per run. Bring your own API keys if you prefer to bill LLM usage to your own account. Set a daily usage cap per workspace, track spend per agent and per user in the AI Usage view, and turn on content moderation when your industry requires it.

Weekly revenue review

scheduled · Mondays
ModelYour key · Opus
Spend this month£4.20 of £25 cap
Actions require approvalon

Scheduled, triggered, or on demand

Schedule an agent on a cron-like frequency in your timezone, run it on demand, or let the metrics themselves start it through Canopy Agent Workflows: a missed target, a threshold crossing, a metric gone silent. The morning action plan that lands before you sit down is the same agent, on a schedule.

KPI Tree

KPI Tree app · 08:00

Good morning, Sarah. Tuesday briefing: revenue is tracking £31.5k/day behind target, down 13.1% MTD. Three things need you.

Conversion is the driver, not traffic. The £31.5k/day gap traces to checkout conversion. Sessions are flat.
Liverpool Street down 40.9% MTD. £26.9k against £45.5k in February, your single biggest drag. You are Accountable.
Checkout fix verified: +£32k. Impact confirmed against the causal baseline, 14 days on.
Open briefingView my metrics

Bring your own agents to the same context

Custom agents built on your Canopy context get everything the platform agents get: the causal model, RACI, verified impact and plan context over MCP, with the same permission model and run history. Shared run history means the whole team can see what has run, what it did, and what it cost.

AI query examples loading

Common questions

Can an agent act without a human approving?
Only where you allow it. Actions can sit behind wait-for-approval steps with named reviewers; rejections branch explicitly, and escalation follows your org chart when nobody responds. Where you want full autonomy, you can grant it deliberately.
What data does an agent see?
Exactly what the user it runs as can see. Agents inherit your permissions and RACI scope; there is no separate all-access service account.
Which models can we use?
Anthropic (Claude), OpenAI and Google Gemini out of the box, switchable per run. Bring your own keys for Anthropic, OpenAI or AWS Bedrock if you prefer direct billing.
How do we control spend?
A daily usage cap per workspace, plus the AI Usage view: total spend, cost per agent, cost per user, and the most expensive recent runs.
Can we see what an agent did?
Every run keeps its history: what ran, what it read, what it proposed or did, and what it cost. Agent and workflow management sits under admin control with a shared run history.

Canopy

The business context layer every agent runs on.

Canopy Agent Workflows

Chain agents, approvals and actions into automated processes.

Metric Ownership

The RACI and escalation model agents respect.

See it on your own metrics.

Book a demo