Analyze cashflow variances
Analyze cashflow patterns across a rolling average to understand seasonality and surface major variances.
Copilot for Finance analyses variances across a 12-month rolling average; Researcher and Copilot in Teams assemble a pre-meeting report from the analysis and recent conversations.
Bounded single agent (rung 5 of 6). The model chooses some of the steps and tools, inside explicit limits.
An agent: the model chooses part of the sequence at runtime, so it can only be tested on its outcome and its bounds.
- Autonomy: T0 · Suggest
- Surfaces information, ranks, or recommends. Changes nothing. Human role: does all the work; may ignore the system entirely.
- How it is governed
- Outcome and bounds only. Loop limits, tool permissions, hooks, an authority matrix and per-step autonomy tiers.
- Anthropic (Claude Certified Architect – Foundations (CCAR-F))Agent: the model dynamically directs its own process and tool use. source
- OpenAI (OpenAI agent guidance (Agents SDK, Codex, OpenAI Academy))Agent: a model with instructions and tools, run in a loop to complete a task on your behalf. source
- Google Cloud (Generative AI Leader)Agents use tools to interact with their environment and achieve tasks (exam §2.5). source
- Microsoft (AI-901 (Azure AI Fundamentals) and AB-100 (Agentic AI Business Solutions Architect))Task agents and autonomous agents; generative orchestration (AB-100). source
How Koko classifies AI · the full ladder is taught in AI & Agentic Delivery Foundations.
Try it
Illustrative demoWorked example of "Analyze cashflow variances" on synthetic data — edit the inputs to tailor it. Edit the inputs to tailor the output — it's baked synthetic data, so nothing leaves your browser until you run it live.
Synthetic demo company (a ~$5B high-growth US AI SaaS) — swap in any name to tailor the output.
What this run should concentrate on.
Click any prompt to copy it and open Microsoft 365 Copilot in a new tab — paste it in (Ctrl/Cmd+V) and tailor it to your own data before you run it.