AI cost management is entering a new era.

Traditional cloud FinOps taught us how to govern compute, storage, and infrastructure. Generative AI introduced token-based cost models. But autonomous agents fundamentally change the equation. A single request can trigger planning loops, model calls, memory retrieval, MCP servers, connected agents, workflows, evaluations, and tool execution across multiple systems before producing a result.

With the arrival of the GitHub Copilot harness in Microsoft Copilot Studio, organizations can now build sophisticated agentic solutions using advanced reasoning models, connected agents, memory, skills, and autonomous workflows. The same technology that powers GitHub Copilot's coding agent and Copilot Cowork is now available for enterprise business processes, bringing unprecedented capabilities but also new challenges in cost governance and operational visibility.

In this session, we introduce the concept of Agent FinOps and provide a practical framework for measuring, allocating, governing, and optimizing costs across agentic systems. We will explore how Copilot Credits are consumed, why traditional token-focused reporting is no longer sufficient, how to establish ownership and chargeback models, and which controls are available in Copilot Studio and the broader Microsoft Copilot ecosystem.

Using real-world enterprise scenarios, we will examine multi-agent architectures, connected agents, MCP integrations, evaluation pipelines, and autonomous workflows, highlighting where cost accumulates and how architects can prevent runaway spend without slowing innovation.

Whether you are deploying your first enterprise agent or scaling hundreds of autonomous copilots, this session will equip you with the governance patterns needed to balance innovation, accountability, and business value.

Key Takeaways

After attending this session, participants will be able to:
* Understand why Agent FinOps differs from traditional cloud FinOps and GenAI cost management.
* Identify the major cost drivers within Copilot Studio, Copilot Cowork, and agentic architectures.
* Understand how Copilot Credits are consumed across reasoning, grounding, tool usage, workflows, memory, and evaluations.
* Design chargeback and showback models for enterprise AI initiatives.
* Apply governance controls for autonomous agents, multi-agent systems, MCP servers, and connected agents.
* Establish KPIs that connect AI spending to measurable business outcomes.
* Build an Agent FinOps operating model that scales from pilot projects to enterprise-wide deployments.