U.S. AI Standards
Abstract
The panel examined the emerging ecosystem of AI‑agent standards that enable disparate AI systems to interoperate, exchange data safely, and underpin commercial transactions. Panelists described the concrete protocols their companies have open‑sourced—Anthropic’s Model Context Protocol (MCP), DeepMind’s agent‑to‑agent (A2A) protocol, OpenAI’s commerce protocol, and XAI’s “MacroHeart” project—illustrating how these standards lower integration costs, protect data portability, and foster competition. The U.S. Center for AI Standards and Innovation outlined its recent Agent Standards Initiative, a series of NIST‑led, voluntary‑consensus efforts (RFIs on security, drafts on identity/authorization, sector‑specific listening sessions) aimed at creating a trusted, interoperable “AI internet.” The discussion repeatedly returned to historic analogues (TCP/IP, HTTPS, electrical standards) to argue that open, government‑supported standards are essential for a globally inclusive AI economy.
Detailed Summary
Su‑Hel Huang opened the session by thanking the organizers (OSTP, OSDP, the Indian hosts) and highlighting the scale of U.S. AI investment – roughly $700 billion in AI infrastructure in the year of the panel. He emphasized that, while companies are fiercely competing to build cheaper, more capable models, interoperability is the only path to a thriving global AI ecosystem. Standards, he argued, are the “glue” that lets builders—whether in India, the United States, or elsewhere—connect AI agents to data sources, tools, and commerce platforms without recreating bespoke integrations.
2. Overview of Existing Agent Protocols
2.1 Anthropic – Model Context Protocol (MCP)
Mike Salido described MCP as a “universal open standard for connecting AI systems to the tools and data sources that enterprises already use.” He illustrated the workflow: an engineer supplies a concise description of a data source (e.g., payroll system, revenue dashboard) and the tools required to access it; the model then “intuitively knows” how to query the resource, much like a human employee would. Prior to MCP, each vendor required a custom, vendor‑locked integration, forcing customers to rebuild pipelines when switching models. MCP’s open‑source nature permits data portability and competition—a new vendor can adopt the same MCP‑defined contracts without re‑engineering the entire stack.
Mike also introduced “Skills,” a protocol for teaching agents reusable task instructions. Skills are authored once and can be ported across models (e.g., from Anthropic to DeepMind) because they rely on a shared representation of the procedure.
2.2 Google DeepMind – Agent‑to‑Agent (A2A) Protocol
Owen Lauder explained DeepMind’s A2A as a “digitized clipboard” that enables two agents to exchange identity, capabilities, intent, data requirements, and security constraints. This solves the current pain point where developers must write ad‑hoc code for every pairwise agent communication, or confine agents to a single “walled‑garden” platform. By standardizing the handshake, A2A “greases the wheels of the agentic economy.”
2.3 OpenAI – Commerce Protocol
Mike Brown framed the OpenAI Commerce Protocol as the “shopping‑assistant” layer that lets an agent autonomously book travel, purchase goods, or reserve services on behalf of a user. He gave a concrete scenario: an agent knows a family plans a vacation to Goa, then secures flights and hotels by invoking the commerce protocol, handling payment and confirmation without the user manually clicking through e‑commerce sites. The protocol ensures security, authentication, and auditability across disparate vendors.
2.4 XAI – MacroHeart Agent Project
Weefi Fernandez (who prefers the nickname “Weefi”) highlighted XAI’s MacroHeart, a high‑level, secretive initiative focused on standardized agent‑centric security and governance. While details remain confidential, XAI stresses the need for industry‑wide consensus so that four emerging firms can build on each other’s breakthroughs rather than fragmenting the market. Fernandez also raised policy questions: Should regulator frameworks treat agent‑driven social platforms differently? He stressed that open standards create a “parallel internet” where trust, security, and interoperability are baked in from the start.
3. Government Perspective: The Agent Standards Initiative
3.1 Organizational Context
Austin Meyron introduced the U.S. Center for AI Standards and Innovation (CASI), originally founded as the U.S. AI Safety Institute and re‑branded by Commerce Secretary Howard Lutnick to focus on standards and innovation. CASI sits within the Department of Commerce (industry‑focused) and NIST (voluntary, consensus‑based standards). Its mission is to act as the “front door” for industry to interact with U.S. government agencies, avoiding duplicate requests and ensuring consistent, expert guidance on frontier AI.
3.2 Core Activities
- Request for Information (RFI) on AI‑Agent Security – open until March; seeks real‑world security challenges from industry to shape forthcoming NIST standards.
- Draft on AI‑Agent Identity & Authorization – published by NIST’s Information Technology Laboratory (ITL); invites comment on how agents prove who they are and what they are permitted to do.
- Sector‑Specific Listening Sessions (April) – targeted to Education, Healthcare, and Finance to surface concerns (e.g., handling of personally identifiable information (PII)) and to identify gaps that voluntary best‑practice documents could fill.
Austin stressed that NIST’s historic role (e.g., defining the red color on vehicle tail‑lights) illustrates how industry‑driven consensus creates globally adopted technical norms without heavy regulation.
3.3 Vision: An Open, Secure “AI Internet”
Austin linked the initiative back to the early internet: open protocols like TCP/IP, HTTPS, and HTTP enabled a decentralized web that propelled global prosperity and U.S. economic leadership. He argued that AI must follow the same path—open, interoperable standards that avoid lock‑in while ensuring security (paralleling the transition from plain‑text credit‑card transmission to SSL/TLS). This approach, he said, will drive adoption, enable cross‑border commerce, and preserve national sovereignty over data.
4. Industry Reflections on the Future of AI Standards
| Speaker | Key Points |
|---|---|
| Mike Salido (Anthropic) | MCP and Skills enable plug‑and‑play data access; open standards lower cost of switching models; importance of government partnership (e.g., OSTP’s “Trump administration” – likely a transcription error). |
| Owen Lauder (DeepMind) | A2A’s clipboard model standardizes agent identity, intent, and security; fosters agent‑to‑agent economy; stresses need for global consensus. |
| Mike Brown (OpenAI) | Commerce protocol turns agents into autonomous shoppers; emphasizes interoperability and security; likens standards to automotive metrics (fuel economy, crash tests) that give users confidence. |
| Weefi Fernandez (XAI) | MacroHeart’s focus on security & governance; warns of regulatory ambiguity around agent‑driven social media; reiterates that open standards act as a “new layer” for the internet. |
All panelists converged on three themes: interoperability, security, and voluntary, consensus‑based development. They also underscored the role of standards in preventing vendor lock‑in, facilitating cross‑industry data sharing, and building user trust.
5. Historical Analogues & Lessons
- Internet Standards (TCP/IP, HTTPS) – enabled decentralized, secure commerce; the government funded early research, then handed it to industry for open adoption.
- Electrical Standards (volts, amps, plugs) – illustrate the pain of fragmentation; the panel warned against repeating “adapter‑world” problems in AI.
- Automotive Safety Metrics – provide a benchmark for consumer confidence; a similar framework for AI agents could assure users about performance, safety, and privacy.
The speakers argued that learning from these precedents can help avoid pitfalls (e.g., standards that are too prescriptive or locked behind patents).
6. International Collaboration
The panel highlighted ongoing U.S.–India cooperation:
- OSTP’s International Network for Advanced AI Measurement Evaluation in Science (INAMES) brings together 10 nation‑level AI security institutes to share evaluation methodologies and best practices.
- Recent NeurIPS‑side meeting (San Diego) produced a blog post summarizing consensus on measurement science.
- The India AI Impact Summit itself serves as a forum for exchanging ideas, showcasing Indian deployments of tools like Gemini, AlphaFold, and AI‑enhanced classrooms.
Austin noted that global participation is essential for a truly open AI stack; standards must be world‑compatible to allow a builder in Kenya to integrate with a model hosted in the United States, for example.
7. Q&A Highlights
Although the transcript does not contain clear, separate audience questions, several interactive moments emerged:
- Clarification of MCP vs. Skills – Mike Salido contrasted data‑source connectivity (MCP) with task‑level instruction (Skills).
- Security Concerns – Austin and the panelists stressed that agent security is a prerequisite for adoption, drawing parallels to the introduction of SSL/TLS for e‑commerce.
- Industry‑Led vs. Government‑Led Standards – Mike Brown argued that industry should drive standards, with government acting as a facilitator rather than a regulator.
- Sector‑Specific Needs – Austin’s upcoming listening sessions will surface domain‑specific constraints (e.g., PII handling in healthcare) that may require tailored standards.
8. Closing Remarks
The session concluded with a call to action:
- Industry to continue open‑sourcing protocols, engage with NIST RFIs, and contribute to sector‑specific dialogues.
- Government to maintain a light‑touch, consensus‑based approach that mirrors the successful internet‑standard model.
- Global partners (including Indian builders) to share use‑cases, informing the evolution of standards that are secure, interoperable, and inclusive.
Key Takeaways
- Open, voluntary standards are essential for a globally interoperable AI ecosystem; they prevent vendor lock‑in and lower integration costs.
- Anthropic’s MCP and Skills, DeepMind’s A2A, OpenAI’s Commerce Protocol, and XAI’s MacroHeart illustrate concrete, industry‑driven attempts to codify agent interactions.
- The U.S. Center for AI Standards and Innovation (CASI), housed in Commerce/NIST, launched the Agent Standards Initiative – an NIST‑led series of RFIs, draft standards (identity/authorization), and sector‑specific listening sessions.
- Security and trust are the primary drivers of adoption; parallels were drawn to the historical shift from plain‑text credit‑card transactions to SSL/TLS.
- Historical analogues (TCP/IP, HTTPS, electrical plugs, automotive safety metrics) provide a roadmap for building confidence in AI agents through standardized measurement and testing.
- International collaboration (e.g., INAMES, India AI Impact Summit) is already underway; global consensus is needed to avoid fragmented “adapter” problems.
- Industry should lead standard creation, with government acting as a facilitator that ensures openness, fairness, and cross‑border compatibility.
- Sector‑specific concerns (education, health, finance) will be explored in upcoming listening sessions, shaping future best‑practice documents.
- The overall vision is an “AI internet”—a decentralized, secure network of interoperable agents that mirrors the success of the early World Wide Web.
Prepared from the verbatim transcript of the “U.S. AI Standards” panel held at the India AI Impact Summit (Bharat Mandapam L1, 20 Feb 2026).
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