Agent washing — Gartner's term for the rebranding of existing products such as AI assistants, RPA and chatbots as agentic AI without substantial agentic capabilities — is the dominant noise source in the 2026 agentic market (Gartner's April 2026 Hype Cycle assesses only about 130 of the thousands of self-described agentic vendors as delivering real capability, against 17% of organisations deployed and more than 80% intending to deploy within two years), and the buyer's defence is a pre-contract capability test for goal-directed multi-step autonomy, tool-based action, carried state and deviation handling, rather than reliance on vendor labels.
Anchored on three Gartner artifacts. (1) Hype Cycle for Agentic AI, published 2 Apr 2026 (Kandaswamy, Ramos, Olliffe, Coshow, den Hamer, Brethenoux; gartner.com/en/articles/hype-cycle-for-agentic-ai + document 7671861): agent-washing definition quoted verbatim; 17% deployed; 42% within 12 months; +22% the following year (the body states 'more than 80% within two years' per the fact-check correction — the scout's original '60%+' UNDERSTATED Gartner's own data); ~130 of thousands of vendors assessed as delivering real capability. (2) Standing prediction, 25 Jun 2025 (Anushree Verma, Sr Director Analyst): 'Over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls' — deliberately cited as a JUNE 2025 prediction, not new 2026 research, per fact-check. (3) Supply-chain agent-washing release, 20 May 2026 (Barcelona Symposium): Jan Snoeckx (Senior Director Analyst) two-sentence quote verbatim-confirmed; the 'end-to-end autonomous planning before 2027 = overstating' line presented as the RELEASE'S finding, not a Snoeckx verbatim quote, per fact-check (secondaries carry it as paraphrase). Gartner pages 403 to crawlers; figures corroborated via 2+ named secondaries each; canonical newsroom/article URLs verified via search. The four-property capability test (goal-directed sequencing, tool action, state, deviation handling) is the publication's operationalisation of Gartner's described capability bar — framed as ours, not as a Gartner framework. VERIFIED 2026-06-09 by hostile fact-check. 90-day cadence. Triggers: (1) a subsequent Gartner assessment materially revising the ~130 count or intent figures; (2) certification/standards making capability claims independently verifiable; (3) market data showing label-bought deployments succeed at the same rate as test-bought ones. Siblings: AM-187 (car-wash test — model maturity counterpart), AM-041 (procurement playbook), AM-113 (vendor contract gotchas), AM-192 (ISO 42001 checkpoint), the McKinsey 23% read.
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The claim: Agent washing — Gartner's term for the rebranding of existing products such as AI assistants, RPA and chatbots as agentic AI without substantial agentic capabilities — is the dominant noise source in the 2026 agentic market (Gartner's April 2026 Hype Cycle assesses only about 130 of the thousands of self-described agentic vendors as delivering real capability, against 17% of organisations deployed and more than 80% intending to deploy within two years), and the buyer's defence is a pre-contract capability test for goal-directed multi-step autonomy, tool-based action, carried state and deviation handling, rather than reliance on vendor labels.
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-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.
- AM-201 · Partial · 10 Jun 2026
One of four named datasets unanchored on review. The claim text names 'Stanford DEL's 12% clearing 300%+ ROI vs 88% at or below break-even' as one of four independent datasets. Full-text verification on 10 Jun 2026 found the Stanford DEL Enterprise AI Playbook contains no such distribution — it studies 51 successful deployments by design and carries no ROI-realisation failure data (full finding at AM-029, correction of 10 Jun 2026). The McKinsey (23% scaling, 17% EBIT-attribution), Gartner (28% fully paying off), and MIT NANDA (95% no measurable P&L impact) datasets verify; the claim's spine stands on three datasets rather than four. 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 from an ROI distribution. Status Up -> Partial.
Reviews coming up in Reporting
- AM-063 · Holding · next +15d (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 +15d (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 +15d (27 Jun 2026)
GPT-5 Pro's tiered-subscription model forces enterprises to classify problems by computational difficulty — $200/month…