Public-sector agentic AI deployment in 2026 operates under five constraints that materially narrow the vendor and architectural options compared to private-sector deployment: (1) FedRAMP authorisation (Moderate or High depending on data sensitivity) is required for federal deployments and increasingly for state, (2) sovereign data residency requirements (data and model inference must remain within national or sub-national boundaries), (3) procurement transparency obligations (the deployment, the vendor, and the decision logic typically must be publicly disclosed), (4) explicit accountability under administrative law (decisions affecting individuals are subject to due-process and appeal frameworks that the agent must support), (5) FOIA-equivalent disclosure of audit logs to the public on request. Public-sector deployments cannot reasonably use peer-to-peer multi-agent patterns and cannot accept vendors without published government cloud SKUs; the realistic 2026 options are Microsoft Azure Government, AWS GovCloud-deployed Anthropic, Google Cloud Public Sector, and a small number of specialist government-AI vendors. The NYC MyCity case (claim AM-044) is the canonical 2026 public-sector failure illustrating what happens when the constraints are inadequately addressed.
Public-sector agentic AI procurement constraints. 90-day review cadence. Watches: (1) the OMB M-24-10 successor framework (post-Executive-Order-14110 federal AI guidance is actively evolving), (2) FedRAMP framework updates including the AI-specific authorisation provisions in development, (3) state-level AI procurement laws (Colorado, Utah, Texas, California, Washington) that establish state-specific procurement bars, (4) the NIST AI Safety Institute's outputs that increasingly serve as de facto federal procurement criteria, (5) emerging case-law on public-sector AI deployment liability.
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The claim: Public-sector agentic AI deployment in 2026 operates under five constraints that materially narrow the vendor and architectural options compared to private-sector deployment: (1) FedRAMP authorisation (Moderate or High depending on data sensitivity) is required for federal deployments and increasingly for state, (2) sovereign data residency requirements (data and model inference must remain within national or sub-national boundaries), (3) procurement transparency obligations (the deployment, the vendor, and the decision logic typically must be publicly disclosed), (4) explicit accountability under administrative law (decisions affecting individuals are subject to due-process and appeal frameworks that the agent must support), (5) FOIA-equivalent disclosure of audit logs to the public on request. Public-sector deployments cannot reasonably use peer-to-peer multi-agent patterns and cannot accept vendors without published government cloud SKUs; the realistic 2026 options are Microsoft Azure Government, AWS GovCloud-deployed Anthropic, Google Cloud Public Sector, and a small number of specialist government-AI vendors. The NYC MyCity case (claim AM-044) is the canonical 2026 public-sector failure illustrating what happens when the constraints are inadequately addressed.
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…