Reports

Findings, written for decision-makers

Our reports distil what we learn into something a lab lead, a policy team, or a board can act on. Each one is free to download and cites the studies behind every number.

MRF-R-2026-03

August 2026

Report

Agents in Financial Services

Adoption, customer-facing risk, model error rates, and the rules already in force

Dr. Ricardo Arcifa, Francieli Carra

Three quarters of UK financial firms already use AI, a third say they fully understand the systems they run, and more than a third of the US population has dealt with a bank's chatbot. Against that, a tribunal has held an airline liable for its chatbot's advice, a finance question-answering benchmark found a frontier model wrong or silent on four questions in five, and the EU has classified credit scoring as high-risk. This report puts the numbers together and sets out controls a risk function can sign off.

MRF-R-2026-02

August 2026

Report

Coding Agents in Production

What controlled trials, delivery data, and the benchmarks themselves say about AI-assisted software engineering

Dr. Ricardo Arcifa, Francieli Carra

Engineering leaders are deciding how far to lean on coding agents on the strength of vendor demos and leaderboard scores. The published evidence is more mixed than either suggests: two controlled trials point in opposite directions, delivery telemetry shows stability falling as adoption rises, and the benchmark most often quoted has been retired by one of its own maintainers. This report sets out that evidence and the measurements a team should run on its own codebase before it changes how it works.

MRF-R-2026-01

August 2026

Report

The State of Agent Evaluation

What certified task families reveal about how frontier labs measure agents

Dr. Ricardo Arcifa, Francieli Carra

Illustrative sample report. It draws on the foundation's three 2026 preprints to show how task certification, held-out grading criteria, and expectancy framing change what an agent benchmark actually measures, and what a lab or policy team should ask for before trusting a headline pass rate.

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