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Keynote

When Everyone Has an Agent

From Better Decisions to Better Markets
GenAIECommerce 2026 workshop at ACM RecSys 2026 · Minneapolis · September 28, 2026
Title slide: When Everyone Has an Agent — From better decisions to better markets, with a cup of espresso.
View slides Explore Espresso Arena PDF (4 MB) References Workshop

Abstract

AI agents are rapidly becoming capable of searching, recommending, negotiating, and transacting on behalf of users and businesses. But something fundamental changes when these agents interact with other independently motivated agents: the problem shifts from making good decisions to making good decisions in a strategic environment.

This talk explores that transition through the lens of recommendation games. When a user agent solicits recommendations from multiple competing agents, each recommendation is no longer simply a prediction of what the user wants. It is also a strategic action, shaped by what other agents recommend and by the rules governing selection and reward.

More broadly, the talk considers a shift from decision theory to game theory and mechanism design for agentic commerce at scale. The central challenge is not simply to build smarter agents. It is to design and evaluate the environments in which they interact, so that useful behavior becomes strategically advantageous.

Outline

  1. A running example — one buyer, five espresso machines, an $800 budget
  2. Better decisions — preferences as vectors; recommendation as nearest-neighbor search
  3. Recommendation as a game — competing recommenders, facility-location games, and equilibria
  4. When the players are LLMs — language as strategy, and evidence that agents take the first offer
  5. Improving agents — empirical game-theoretic analysis, prompts as strategies, and the Espresso Arena demo
  6. Market design — a market agent, lessons from ad tech, and subsidized exploration
  7. The world we can build — an advocate for everyone; markets as design objects with three knobs: access, payment, mobility

References

Recommendation as a game

LLM agents as players

Empirical game-theoretic analysis

Market design

The opinions in this talk are my own and do not represent my employer. The espresso market is a stylized example.