Position: Build Agent Advocates, Not Platform Agents

Sayash Kapoor, Noam Kolt, Seth Lazar
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:81617-81633, 2025.

Abstract

Language model agents are poised to mediate how people navigate and act online. If the companies that already dominate internet search, communication, and commerce—or the firms trying to unseat them—control these agents, the resulting platform agents will likely deepen surveillance, tighten lock-in, and further entrench incumbents. To resist that trajectory, this position paper argues that we should promote agent advocates: user-controlled agents that safeguard individual autonomy and choice. Doing so demands three coordinated moves: broad public access to both compute and capable AI models that are not platform-owned, open interoperability and safety standards, and market regulation that prevents platforms from foreclosing competition.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-kapoor25a, title = {Position: Build Agent Advocates, Not Platform Agents}, author = {Kapoor, Sayash and Kolt, Noam and Lazar, Seth}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {81617--81633}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/kapoor25a/kapoor25a.pdf}, url = {https://proceedings.mlr.press/v267/kapoor25a.html}, abstract = {Language model agents are poised to mediate how people navigate and act online. If the companies that already dominate internet search, communication, and commerce—or the firms trying to unseat them—control these agents, the resulting platform agents will likely deepen surveillance, tighten lock-in, and further entrench incumbents. To resist that trajectory, this position paper argues that we should promote agent advocates: user-controlled agents that safeguard individual autonomy and choice. Doing so demands three coordinated moves: broad public access to both compute and capable AI models that are not platform-owned, open interoperability and safety standards, and market regulation that prevents platforms from foreclosing competition.} }
Endnote
%0 Conference Paper %T Position: Build Agent Advocates, Not Platform Agents %A Sayash Kapoor %A Noam Kolt %A Seth Lazar %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-kapoor25a %I PMLR %P 81617--81633 %U https://proceedings.mlr.press/v267/kapoor25a.html %V 267 %X Language model agents are poised to mediate how people navigate and act online. If the companies that already dominate internet search, communication, and commerce—or the firms trying to unseat them—control these agents, the resulting platform agents will likely deepen surveillance, tighten lock-in, and further entrench incumbents. To resist that trajectory, this position paper argues that we should promote agent advocates: user-controlled agents that safeguard individual autonomy and choice. Doing so demands three coordinated moves: broad public access to both compute and capable AI models that are not platform-owned, open interoperability and safety standards, and market regulation that prevents platforms from foreclosing competition.
APA
Kapoor, S., Kolt, N. & Lazar, S.. (2025). Position: Build Agent Advocates, Not Platform Agents. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:81617-81633 Available from https://proceedings.mlr.press/v267/kapoor25a.html.

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