AI cost management used to be relatively simple: understand your licenses, track adoption, and calculate cost per user. Agentic AI changes that equation.

With Microsoft 365 Copilot, Copilot Cowork, Autopilot experiences, Copilot Studio, and different agent harnesses, organizations are moving toward a world where licensing is only one part of the financial picture. Agents reason, retrieve knowledge, invoke tools, execute actions, call APIs, and increasingly perform work on behalf of users. Each of these activities can contribute to consumption, while the real value lies in the business outcome they produce.

Agent FinOps therefore isn’t about counting tokens. It’s about understanding the economics of autonomous work.

In this session, we will develop a practical FinOps for Microsoft Agents reference architecture that connects the agent experience, harness and runtime with consumption telemetry, cost allocation, governance, and ultimately business outcomes.

We will look across the Microsoft agent landscape, from Microsoft 365 Copilot and Copilot Cowork to Copilot Studio and other agent execution models, and explore how organizations can move from simply monitoring AI consumption toward understanding cost per successful task, cost per automated process, and value per agent outcome.

The goal is not simply to make agents cheaper. It is to establish an architecture and operating model that helps organizations make agentic AI observable, attributable, governable, and economically sustainable at scale.

Key Takeaways

Attendees will leave with:

* A FinOps model for the agentic enterprise
Understand why traditional license and cost-per-user models are no longer sufficient once agents start performing work autonomously.

* A FinOps for Microsoft Agents reference architecture
Connect Microsoft 365 Copilot, Copilot Cowork, Autopilot experiences, Copilot Studio and different agent harnesses to consumption, telemetry, allocation, governance and business outcomes.

* A practical approach to Agent Unit Economics
Move beyond tokens and raw consumption toward meaningful measures such as Cost per Successful Run, Cost per Business Outcome, Credits per Outcome, and Business Value per Agent Cost.

* A lifecycle for managing agent economics
Apply a repeatable model of Discover → Estimate → Observe → Allocate → Optimize → Govern from development through production operation.

* A different definition of AI optimization
FinOps for Agents should not ask “How do we use less AI?” but instead “How do we maximize business value for every unit of agent consumption?”