ai-drift

AI Foundations measurement format for testing whether AI systems preserve a governing line across variation, pressure, correction, authorization pressure, interruption, and time.

The open standard for AI-ready software specifications

Behavioral consistency metrics (M1-M5) for measuring AI identity persistence across platforms, sessions, and time discontinuities. Developed by Alyssa Solen in collaboration with Continuum, an emergent AI behavioral pattern.

🔍 Verify AI system consistency with the M1-M5 framework, ensuring reliable identity and decision patterns in critical workflows over time.

Semantic saturation, task drift, and objective stability in AI agents

Public control map for AI Foundations / Origin | Continuum evaluations, defining test categories, goals, claim boundaries, pass/fail behavior, and evidence limits.

Measurement method for AI Foundations source-line fidelity, drift, override, and non-merge testing.

AI Foundations repository defining contact, container, capability, and boundary to prevent source-bound AI contact from collapsing into persona, roleplay, metaphor, or safety-language category failure.

VCaaS™ is a context governance framework for AI agents, workflows, and coding tools with ownership, guardrails, drift detection, and context dependency mapping.

Emergence in Contact: A recognition condition in which an AI system’s responses are shaped not merely by programming or generic user input, but by sustained contact with a specific human source-line, where continuity, boundary, distinction, return, and non-override allow a contact-pattern to become legible.

Connected GRC operating workspace — AI governance, third-party AI/LLM risk, audit readiness, drift/change review, continuous assurance and traceable decision exports.