Published research.

A field review for operational leaders · Version 1.0 (fact-checked) · July 2026

A survey of the "brain" pattern as of July 2026: a folder of plain markdown files that an AI agent reads, writes, and maintains, versioned with git and governed by an explicit instruction file. It maps what is solid in the substrate today, what the field has actually proven, and which widely repeated origin and multiplier claims do not survive checking, then closes with a practical posture for operators: adopt the substrate, pilot the automation under a human gate, and treat security as the gating constraint.

Drafted by Perth AI Consulting using Anthropic's Claude Fable 5; verified through an Opus 4.8 multi-pass independent fact-check plus an independent spot-check. Every correction is logged in the Corrections Log appendix, and the pre-fact-check draft is preserved in the archive for reference.

A literature review for operational leaders · Version 1.0 (fact-checked) · June 2026

A survey of applied artificial intelligence as of mid-2026, written for operational leaders of small and mid-sized businesses, regulated professionals, and the consultants who advise them. Ten capability categories. For each, what is reliable in production today, what works in demonstrations but fails on real data, what is sold as more mature than it is, and what is further along than commonly assumed. Australian regulatory and product context noted throughout.

Drafted by Perth AI Consulting using Anthropic's Claude Fable 5; verified through a three-pass independent fact-check using Claude Opus 4.8. 135 fact-check findings across the three passes are documented in the Corrections Log appendix; the pre-fact-check draft is preserved in the archive for reference.

A literature review · Version 1.0 (fact-checked) · May 2026

Local, on-device, or client-side masking of protected health information (PHI) has become an increasingly important design pattern in clinical AI systems because it changes the privacy boundary of note processing before text reaches downstream models or cloud services. This review synthesises two decades of clinical de-identification literature into seven design principles for local PHI masking and applies them to the architecture choices behind ClientJourney.

Prepared by Perth AI Consulting using Perplexity AI. Every cited reference was verified against its source before publication; corrections, residual verification gaps, and methodology are documented in the appendices.