Each is a deep technology with published papers, working prototypes, and open questions.
How to measure trust between humans and AI agents. Three metrics (KRG, JIS, DEP) give quantitative answers. Aviation-grade assurance levels (AAL A-E). Trust as a service.
How humans and agents learn together. The HALA Framework maps 7 layers of human transformation mirrored by 7 layers of agent development, with 6 intersection zones where the real learning happens.
How agents grow and adapt. Agent DNA separates genotype from phenotype. Instinct lifecycle follows Markov dynamics. Arena tests different architectures.
TR-004
How to capture and replicate what makes organizations work. We sequence the "DNA" of AI-native organizations so new ones can be assembled from proven patterns.
TR-007
How to diagnose where a person is on their path to AI-nativity and what's blocking them. 5-layer diagnostic framework with 10 real profiles. Not a personality test — a map of your sovereign function.
How to create an AI agent that actually knows its human. Personal Agent = delegated attention. Five sub-personalities mirror the five properties of an organization.
Aviation-grade reliability for AI agents. Quantitative assurance levels, 12 failure modes in FMEA matrix, Monte Carlo validation. The bottleneck inversion theorem.
How agents understand their environment. Three-level organizational cartography. Cognitive Light Cone defines the horizon of what an agent can know. Digital twins for rehearsal.
TR-008
How agents talk to each other and to humans across system boundaries. Bridge protocol for cross-world routing. Avatars as projections of entities in foreign worlds.
How to prevent AI agents from being confidently wrong. Two-layer defense against sycophancy. Misalignment as architectural problem, not rule violation. Agency detection from first principles.
TR-012
