White papers and operational essays on decision-level proof and execution-time governance.
Research PreviewMRP-2026-04v1.09 Jun 2026
A disambiguation study of governance-context effects in AI decision agents
E2 left three open questions about why an AI agent's verdicts shift as governance context is added. E3 disambiguated them. Three L3 arms separated precedents from raw context density; an L4-without-nudge variant isolated the policy text from the anti-sycophancy clause; a scale…
Research PreviewMRP-2026-03v1.027 May 2026
Why giving an AI agent more governance context doesn't make it steadily more decisive
Regulated teams deploying AI agents cannot say what makes one commit to a decision rather than defer it, or whether more governance context reliably helps. We reused the frozen 283-record UK procurement corpus from MRP-2026-02 and ran the same agent over the same records five…
Research PreviewMRP-2026-02v1.018 May 2026
Why an AI agent reaches for 'needs review' where a policy engine gives a straight yes or no
Regulated firms cannot routinely answer how a specific AI-augmented decision was made. We ran 300 public UK Contracts Finder filings through an LLM agent and the same records through a MeshQu policy evaluator at the same moment. Both verdicts, the agent's reasoning, the exact…
White PaperWP-PROOF-01v1.06 May 2026
Why AI governance fails at the moment of execution
AI governance frameworks describe how decisions should be made — but at the moment a consequential decision is executed, the evidence required to defend it almost never exists in a verifiable form. This paper names that structural gap, traces it across regulated sectors, surve…
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