FinOps for AI Agents: Who Spent All the Tokens?
Official Schedule 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
Official 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.
Related YouTube Video
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).
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.
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Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.
Supporting Slides
- youtube Lc8zRh9muoY slides — extracted from the related public AI Engineer video.
Slide Evidence
- Slide-only cropped deck: youtube Lc8zRh9muoY dense slides (6 viable slide images).
- Related slide/OCR pages:
- youtube Lc8zRh9muoY dense slides
- youtube Lc8zRh9muoY reconstructed slides
- youtube Lc8zRh9muoY slides
- Slide-derived terms:
live,sell,boundary,place_order,tool,symbol,quantity,determinism,acme,dict,record,side,argmax,replay,output,place_order-1.jsonm,agent-1.jsonm,filled