Missing Mechanisms for the Agentic Economy

Source: Tim O'Reilly, with Ilan Strauss and Sruly Rosenblat; Asimov's Addendum (Substack) · 2026-04-01

This essay traces our path from disclosures through protocols to markets and mechanism design (read more on our new website: here). Rather than simply stating our conclusions, I’m sharing our thought process and some of the conversations and historical examples that have shaped it.

Ilan Strauss and I started the AI Disclosures Project in early 2024 with a conviction that most regulators had little idea how AI worked or where it was going. The field was so young that many of the early regulatory proposals were misguided. We thought that regulators and industry should start by agreeing on standards for disclosure, so that we could all learn together as the technology develops. You can’t regulate what you don’t understand.

One of our first insights was that focusing solely on model safety was a mistake, much as if regulators inspected automobiles at the factory but completely ignored their use on the roads. We believed (and still do) that the focus should be on AI as deployed. And we believe that disclosures shouldn’t focus just on capabilities but on business models and the operating metrics that AI companies use to shape how their products operate.

The first turn in our thinking came when we realized that disclosures aren’t just informational. The most important disclosures are functional. We came to see the parallels between disclosures and communications protocols, the agreed-on methods by which networked systems share information. For example, the HTTP protocol that underlies the World Wide Web specifies how a web browser and web server communicate in order to display a web page.

Once you see protocols as a system of functional disclosures, you start noticing that every regulatory system has a kind of communications and control protocol at its heart. Generally Accepted Accounting Principles (GAAP) or IFRS, the European equivalent, are protocols for communication between companies and their accountants, auditors, banks, investors, and tax authorities. Even road markings and road signs are a communications protocol, giving information to drivers about local conditions, laws, and the proper use of the road. These are slow, analog protocols, but they are protocols nonetheless.

Protocols can be inspected. Observability is the key to governance. Police observe speeders on the road; credit card processors and banks watch for credit card fraud on their payment networks; email processors filter spam as it passes through nodes on the network. The observability points for AI are still emerging, but that’s where regulators should be focused.

Think about the early shape of the AI chatbot market. It was a winner-takes-all race to be the dominant platform for AI in the way Windows became the platform for PCs, or iOS and Android for phones. Whoever wins controls the market. Then Anthropic introduced MCP, the Model Context Protocol. All of a sudden, the landscape looked more like a web. There could be many winners. It didn’t matter what model you were running or whose APIs you were calling as long as you followed the protocol. And as the agentic AI market unfolded, the protocol wasn’t just MCP. An AI agent could be a user of the existing internet protocol stacks. Whether MCP itself survives or is superseded by other protocols, the shape of the market was transformed.

This insight reframed our whole project. Protocols are not just technical infrastructure. They are market-shaping mechanisms.

In his introduction to the Summer of Protocols’ Protocol Reader, Venkatesh Rao cited Ethereum researcher Danny Ryan’s definition of a protocol as a “stratum of codified behavior” enabling coordination. He pointed out that protocols tend to become invisible once adopted. Rao calls this a “Whitehead advance,” after the philosopher Alfred North Whitehead’s observation that civilization advances by extending what we can do without thinking.

But he also made the thought-provoking point that a protocol is an “engineered argument,” in contrast with an API, which he says is an “engineered agreement” enforced by one dominant actor. There’s more to it than just the power asymmetry of enforced agreement, though. In a followup conversation, Venkatesh Rao noted that protocols are “not just codified modes of information exchange, but modes of live, structured, argumentation, often with an active computational element. For example, CSMA/CD (Ethernet) must detect packet collisions and compute and execute a random delay for retransmittal of packets. This is not mere structured communication. This is argumentation with what philosophers call dynamic semantics.”

I love the idea of protocols as an engineered argument. The dynamism this suggests is going to be ever more true in a future of agentic protocols. But this notion also triggered another thought, which is that markets are also engineered arguments. My bridge to this reformulation was the difference between de jure protocols that arise from a formal standards process, and de facto protocols that arise through market contention.

In short, we are not just building communication protocols for software agents. We are developing a new way to standardize the best practices and workflows that will shape the human + AI future, allowing humans and agents to cooperate across organizations, industries, and borders.

Once the Summer of Protocols team (Venkatesh Rao and Timber Schroff) planted in my mind the connection between workflows and protocols, it occurred to me that Agent Skills are also a “stratum of codified behavior,” and perhaps even a set of competing “engineered arguments” for how to do work with AI.

At the simplest level, a Skill is a piece of structured knowledge: here’s how to create a Word document; here’s how to extract the text from a PDF; here’s how to publish on the Hugging Face Hub. There can be many Skills that attempt to codify the same knowledge, but some may be better than others. As Skills multiply, how will we find the best ones?