Markdown source

FinOps for AI Agents: Who Spent All the Tokens?

Conference Context

Session Description

When an autonomous agent finishes a task successfully but costs ten times more than it did the previous day, traditional application monitoring fails. A recursive tool loop that retries silently, an oversized context window that quietly expands, or an unflagged model upgrade can burn through an entire budget long before a human notices. The execution appears successful on functional dashboards, meaning the only clear signal of failure is the cloud invoice at the end of the month. As AI systems move into production, tokens have become a primary operational resource alongside CPU, memory, and storage, yet few teams manage them with equivalent systems rigor. Most architectures lack the granular visibility required to attribute token spend to specific users, agents, or workflows, and they lack mechanisms to terminate a runaway loop before it triggers a financial incident. This session treats token consumption as a first class systems problem, demonstrating how to make it observable, attributable, and enforceable across complex agent workflows. The presentation covers practical engineering patterns for instrumenting token usage at every model call and tool invocation, attributing costs down to specific users or business operations, surfacing expensive execution paths, and enforcing runtime budgets, quotas, and circuit breakers to halt runaway behavior in real time. Attendees will leave with a practical framework for governing agent spend deliberately, transforming tokens into a managed operational resource rather than a surprise line item on the cloud bill.

Media Evidence

Your Agent Failed in Prod. Good Luck Reproducing It. - Tisha Chawla & Susheem Koul, Microsoft (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

Evidence Graph

This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

Media Signals

Agent Reading Notes

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Transcript Status

Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Not fetched yet.

People

Supporting Slides

Slide Evidence

Attendance Visibility

No high-confidence attendance icon signal is shown for this talk. The sampled video evidence was either low confidence, source-proxy-only, or did not expose a clear audience view.

Synthesis

Synthesized Breakdown

FinOps for AI Agents: Who Spent All the Tokens? ## Conference Context - Date/time: 2026-07-01 · 11:10am-11:30am - Track/room: AI Architects: AI Factories · Leadership 2 - Speaker(s): Tisha Chawla, Susheem Koul - Session type/status: session · confirmed - Track: AI Architects: AI Factories - Room: Leadership 2 - Session type: session - Status: confirmed ## Session Description When an autonomous agent finishes a task successfully but costs ten times more than it did the previous day, traditional application monitoring fails. A recursive tool loop that retries silently, an oversized context window that quietly expands, or an unflagged model upgrade can burn through an entire budget long before a human notices. The execution appears successful on functional dashboards, meaning the only clear signal of failure is the cloud invoice at the end of the month.

Speaker And Company Context

Topics Covered

Derived Links And Source Material

Novel Concepts / Clever Methods

Evidence Boundary

This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.