The three open agent protocols active in 2026 (Anthropic's Model Context Protocol, Google's Agent2Agent protocol contributed to the Linux Foundation, and Meta's Llama Stack) are not on a convergence trajectory, and the four major proprietary agentic platforms (Microsoft Copilot Agent, Salesforce Agentforce, SAP Joule, ServiceNow Now Assist) do not adopt any of the three as first-class defaults. The cost of being wrong on the model choice is low because swapping models is a configuration change. The cost of being wrong on the protocol choice is high because the locked asset is the tool inventory — the MCP servers, A2A endpoints, Llama Stack tool plugins, or proprietary extensions the customer has built or commissioned — and re-platforming the tool inventory is the long-tail engineering bill. Standard 2026 agentic AI MSAs do not include the three procurement clauses (protocol portability disclosure, tool inventory exit terms, protocol-roadmap commitment) that would price the protocol-roadmap optionality back to the customer.
Claim is scoped to the protocol-lock-in dimension of the 2026 enterprise agentic AI procurement decision. Does not assert the protocols are technically inferior or superior to one another — asserts that the fragmentation creates re-platform cost that most procurement processes are not modelling. 60-day review cadence. Trigger conditions: (1) MCP, A2A, and Llama Stack publish a converged or interoperable spec — would move toward Partial because the protocol fragmentation is closing structurally; (2) one of the four major proprietary platforms (Microsoft, Salesforce, SAP, ServiceNow) adopts MCP or A2A as a first-class default — would shift the lock-in calculus materially and reduce the per-platform tool-wrapper cost; (3) a published 2026 enterprise re-platform with a cost figure attached — would empirically anchor the 'tool inventory is the locked asset' argument; (4) an OWASP, NIST, or ENISA control set on agent-protocol risk — would change the audit picture and likely accelerate adoption of the three procurement clauses; (5) Llama Stack ecosystem growth (server count, library count, third-party tool support) reaching parity with MCP — would change the picking calculus for self-hosted-aligned customers.
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The claim: The three open agent protocols active in 2026 (Anthropic's Model Context Protocol, Google's Agent2Agent protocol contributed to the Linux Foundation, and Meta's Llama Stack) are not on a convergence trajectory, and the four major proprietary agentic platforms (Microsoft Copilot Agent, Salesforce Agentforce, SAP Joule, ServiceNow Now Assist) do not adopt any of the three as first-class defaults. The cost of being wrong on the model choice is low because swapping models is a configuration change. The cost of being wrong on the protocol choice is high because the locked asset is the tool inventory — the MCP servers, A2A endpoints, Llama Stack tool plugins, or proprietary extensions the customer has built or commissioned — and re-platforming the tool inventory is the long-tail engineering bill. Standard 2026 agentic AI MSAs do not include the three procurement clauses (protocol portability disclosure, tool inventory exit terms, protocol-roadmap commitment) that would price the protocol-roadmap optionality back to the customer.
About this register
The Reporting register tracks claims published from articles addressed to senior enterprise IT leaders — CIOs, IT directors, heads of platform. Claims are reviewed on a 30–90 day cadence; each review either reaffirms the claim, marks one substantive part as Partial, or marks it Not holding once the underlying evidence has been overtaken.
Recent corrections in Reporting
- AM-008 · Partial · 17 Jun 2026
Source-text figure re-review: Google's 2024 Environmental Report reports a 28% year-over-year increase to 8.1 billion gallons, not the 33% (from a 6.1 billion 2023 base) asserted at publish. The 8.1B 2024 figure and the Microsoft WUE 0.30 L/kWh / 39%-improvement figure are unchanged and verified. Article corrected to 28% and the unsupported 6.1B base removed; the claim text retains the original figure with this correction per the Holding-up protocol.
- AM-132 · Partial · 10 Jun 2026
One of four legs unanchored on re-review. The claim text attributes '12% of deployments clearing 300%+ ROI with 88% at or below break-even at 12-18 months' to the Stanford DEL 2026 Enterprise AI Playbook. Full-text verification on 10 Jun 2026 found no such figure in that source: the playbook (Pereira, Graylin, Brynjolfsson, Apr 2026) studies 51 successful deployments by design and contains no ROI distribution, no 300%-plus cohort, and no break-even measurement point (full finding at AM-029, correction of 10 Jun 2026). The only verified figure carrying the same 12/88 numerals is IDC research with Lenovo (via CIO.com, Mar 2025): roughly 88% of AI proof-of-concepts never reach production and roughly 12% graduate — a pilot-to-production graduation metric, not an ROI distribution. The Gartner 28%, McKinsey 23%/17%, and MIT NANDA 95% legs verify; they support a small high-performing tail and a large struggling body, but none documents the two-peak bimodal shape the claim asserts. Status Up -> Partial.
- AM-129 · Partial · 10 Jun 2026
One of three read-against anchors unanchored on re-review. The claim text cites 'Stanford Digital Economy Lab Enterprise AI Playbook (12/88 bimodal ROI distribution at 12-18 months)' and frames the realistic ROI band around 'the highest-discipline 12% cohort'. Full-text verification on 10 Jun 2026 found the playbook contains no 12/88 distribution, no bimodal ROI shape, and no 12-18-month ROI measurement point (full finding at AM-029, correction of 10 Jun 2026). The claim's core negative finding — no mid-market enterprise has produced a documented +240% ROI in 90 days under audited conditions — is unaffected; the McKinsey State of AI 2025 and MIT NANDA legs verify and continue to support it. The '12% cohort' framing has no verifiable referent. The only verified figure carrying the 12/88 numerals is IDC's pilot-graduation finding (roughly 88% of AI proof-of-concepts never reach production; via CIO.com, Mar 2025), a different metric. Status Up -> Partial.
Reviews coming up in Reporting
- AM-063 · Holding · next +9d (27 Jun 2026)
AI agents executing financial transactions need a four-control bundle (action-approval gates by blast radius, kill-swit…
- AM-061 · Holding · next +9d (27 Jun 2026)
Production agentic-AI costs at scale routinely run multiples of POC projections, and a layered optimisation programme c…
- AM-003 · Partial · next +9d (27 Jun 2026)
GPT-5 Pro's tiered-subscription model forces enterprises to classify problems by computational difficulty — $200/month…