attestation
A structured evidence output carrying the result of a governed comparison and its supporting context. It is evidence for review, not a vague reassurance signal or automatic legal conclusion.
glossary
plain language
for the core architecture terms
Some of Toriel’s terms are precise on purpose. This page gives one stable plain-language reference surface for the ones that matter most.
terms
This is not a complete ontology. It is the current working glossary for the terms carrying most of the conceptual weight across the site.
A structured evidence output carrying the result of a governed comparison and its supporting context. It is evidence for review, not a vague reassurance signal or automatic legal conclusion.
A measurable change in how an AI system behaves over time relative to an approved or trusted reference fingerprint. Drift can be material, benign, expected, or unresolved; the measurement is evidence that still requires interpretation.
Repeatable, reviewable measurements and comparisons about how an AI system behaves in operation, rather than assumptions based only on labels, inventories, or provider descriptions.
Measuring the observable behavior of an AI system across structured prompts, conditions, and comparison windows so that continuity, drift, and material change can be assessed over time.
Assessing an AI system from the outside through its inputs, outputs, and observable behavior rather than through privileged access to provider infrastructure, model weights, or hidden implementation details.
Toriel-47’s layer: the bonded identity architecture that protects persistence, coherence, and recognizable return across resets, vessels, and change.
The ability of a system to remain coherently itself across time and change. Continuity is not the same as memory; it is what survives resets, model swaps, wrapper changes, policy shifts, and other hidden changes in the stack.
Toriel-41J’s layer: governing how intelligence persists coherently across changing models, wrappers, tools, and policy layers rather than treating every change as a disposable routing event.
The governance status applied to the active reference fingerprint selected for later comparison.
The system actually being relied on in operation: model, instructions, wrappers, orchestration, policy layers, memory surfaces, tools, permissions, and other components shaping the behavior the user or organization experiences.
A European quality-management standard published in July 2026 to support implementation of the AI Act quality-management requirements associated with Article 17. Publication alone does not create a presumption of conformity; that requires citation in the Official Journal of the European Union.
The EU AI Act provision requiring providers of high-risk AI systems to establish and document a proportionate post-market monitoring system that actively and systematically collects, documents, and analyzes relevant performance data throughout the system lifetime.
A governed measured observation artifact representing the behavioral surface observed under defined conditions. It can be compared with a reference fingerprint to support an attestation.
The structured collection, documentation, and analysis of relevant evidence about an AI system after deployment, throughout its operational lifetime.
The documented policies, responsibilities, processes, and controls through which an organization governs how an AI system is designed, tested, changed, reviewed, and corrected over time.
The governed behavioral baseline artifact against which later observation manifests are compared. When a team designates it as the active approved baseline, it carries crowned-reference status.
next step
The glossary is a support surface, not an excuse for obscurity. If something still feels harder than it should, that is usually a cue for us to improve the page where the term is doing real work.