# Agentic AI: User Empowerment or Foreclosure?

arXiv: https://arxiv.org/abs/2608.06510 · HTML: https://mlsanity.com/paper/2608.06510/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 5 of its 64 references that are in the map.
- First posted on arXiv 6 Aug 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 5 of 5)

- [Building Human Values into Recommender Systems: An Interdisciplinary Synthesis](https://mlsanity.com/paper/2207.10192/) (2022)
- [The History of Digital Spam](https://mlsanity.com/paper/1908.06173/) (2019)
- [Ad-Blocking and Counter Blocking: A Slice of the Arms Race](https://mlsanity.com/paper/1605.05077/) (2016)
- [The Future of Ad Blocking: An Analytical Framework and New Techniques](https://mlsanity.com/paper/1705.08568/) (2017)
- [To be Tough or Soft: Measuring the Impact of Counter-Ad-blocking Strategies on User Engagement](https://mlsanity.com/paper/2002.06620/) (2020)

## Abstract (arXiv)

Agentic AI promises systems that can act on users' behalf, from filtering content to negotiating prices to selecting services. Whether it will empower users is an open question, and one that depends on more than the technology. We conduct a comparative case analysis of four earlier, more mature domains in which similar forms of agency emerged: browser-based ad blockers, platform recommender systems, financial robo-advisors, and email spam filtering. Across the cases, questions about whose interests agents would serve were resolved through technical arrangements: API choices, protocol governance, industry standards, and default configurations. Beyond their technical form, these were political decisions. We identify this settling of contestable questions in a technical form as depoliticization, a concept from political theory, here at work in technological systems. Its most consequential effect is that individual outcomes and collective contestation capacity can move in opposite directions: spam inbox quality improved substantially while the organized capacity to contest spam governance collapsed. Where intermediary institutions sustained formal channels for challenge, user-aligned agency proved more durable; where proprietary infrastructure and closed standard-setting absorbed contestation, the material basis for user-aligned alternatives was dismantled, and the loss proved hard to reverse. Applying this lens to agentic AI, we find a similar pattern forming: governance is consolidating around the Model Context Protocol and the Agentic AI Foundation, an industry-governed venue already deciding what agents will be able to do. Unlike in the completed trajectories, these decisions have not yet hardened, and remain open to challenge by users and the public.