AI Capsule™
Machine-readable governance representation for complex systems.
AI Capsules encode selected constraints, source references, interfaces, invariants, assumptions, and validation rules into structured artifacts that support reconstruction, review, and governance implementation.
What an AI Capsule Is
A capsule is a representation, not a mechanism. It records what a governed system description requires, in a form that can be reviewed, versioned, and reused.
An AI Capsule is a structured, machine-readable artifact that records the constraints, interfaces, invariants, source references, validation rules, assumptions, and known exclusions needed to reconstruct or evaluate a governed system description. It supports governance implementation, review, and controlled reuse. It does not itself authorize actions.
AI Capsule is a FERZ-defined construct for machine-readable governance representation upstream of runtime authorization.
An AI Capsule is not a policy engine and not a document repository. A policy engine evaluates rules at runtime; a document repository stores prose. A capsule is a structured, machine-readable representation with declared scope, assumptions, and validation rules.
Why Representation Matters
Governance rules often begin as human-readable material: policies, regulations, operating procedures, technical specifications, architectural notes, and exception logic. Before those rules can support deterministic authorization, they must be represented in a form that can be reviewed, versioned, tested, and connected to source material.
AI Capsules address that representation problem. They do not replace the authorization boundary. They help prepare the governance knowledge that the boundary may later rely on.
A capsule can stand in for a large body of source material during review. It distills selected constraints, rules, references, assumptions, and exclusions into a compact artifact that can be inspected, versioned, and reconciled against source materials. The value is not total compression. The value is scoped density: a capsule states what it includes, what it assumes, and what it leaves out.
What a Capsule Contains
A capsule records a fixed set of elements. Each is explicit, so the artifact can be inspected and checked rather than trusted on faith.
Source references
The documents, passages, systems, or rules from which the capsule was derived.
Constraints
Conditions, prohibitions, permissions, and dependencies expressed in structured form.
Interfaces
Systems, actors, tools, or workflows the capsule references.
Invariants
Properties that must remain stable across versions or uses.
Assumptions
Explicit operating assumptions, including known limits.
Validation rules
Checks used to detect inconsistency, incompleteness, or unsupported assertions.
Exclusions
What the capsule does not represent or should not be used to infer.
Version state
Date, authoring context, revision history, and compatibility notes.
How a Capsule Is Built
A capsule is produced through a defined sequence, from source material to a reviewable artifact.
How It Relates to Deterministic Governance
A capsule sits upstream of the boundary that governs execution. The two are separate layers with separate jobs.
AI Capsules sit upstream of runtime authorization. They can help organize the policy, source, and system knowledge that an authorization boundary may use. The boundary itself is separate: it evaluates a proposed action before execution, emits ALLOW, DENY, or ABSTAIN, and produces an authorization artifact sufficient for independent replay.
What AI Capsules Are Not
The capsule is a representation. The following are deliberately outside its role.
Not a runtime authorization boundary
It does not sit in the execution path and gate live actions.
Does not issue verdicts
It does not issue ALLOW, DENY, or ABSTAIN.
Does not prove authorization
It does not establish that an action was authorized.
Does not establish truth
It records the origin of source inputs; it does not certify that they are true.
Does not replace review
It does not replace source documents, legal review, or system testing.
Does not replace enforcement
It does not replace the runtime authorization boundary.
Its value is representation: making selected governance knowledge explicit, structured, versioned, and testable enough to support downstream implementation and review.
Use Cases
Capsules support preparation and review tasks. They are inputs to governance work, not a substitute for it.
Policy and Doctrine Representation
AI Capsules can represent selected policies, rules, definitions, operating assumptions, and conformance expectations in a structured, machine-readable form. This allows downstream materials to be checked for consistency against an authorized source of meaning without treating the capsule as a runtime authorization boundary.
Governance rule capture
Record governance rules in a single structured artifact.
Policy-to-implementation preparation
Prepare policy for downstream implementation and review.
Regulatory source mapping
Map requirements back to their regulatory sources.
System reconstruction support
Provide a reference that supports reconstructing a system description.
Knowledge-transfer artifacts
Support knowledge transfer across teams and over time.
Audit preparation
Assemble a reviewable basis ahead of audit.
Internal control design support
Support the design and review of internal controls.
Common Questions
What is an AI Capsule?
An AI Capsule is a structured, machine-readable artifact that records the constraints, interfaces, source references, assumptions, validation rules, and exclusions needed to reconstruct or evaluate a governed system description. It supports review and controlled reuse, and does not itself authorize actions.
Does an AI Capsule authorize AI actions?
No. A capsule is a representation. It does not issue ALLOW, DENY, or ABSTAIN and does not prove an action was authorized. The runtime authorization boundary evaluates proposed actions before execution and emits ALLOW, DENY, or ABSTAIN.
How is an AI Capsule different from documentation?
Documentation stores prose for people to read. A capsule is a structured, machine-readable artifact with declared scope, assumptions, and validation rules, so it can be inspected, versioned, and reconciled against its source material.
What does an AI Capsule contain?
Source references, constraints, interfaces, invariants, assumptions, validation rules, exclusions, and version state. Each element is explicit, so the artifact can be checked rather than trusted on faith.
Where does an AI Capsule fit relative to runtime authorization?
Upstream. A capsule organizes the policy, source, and system knowledge an authorization boundary may use. The boundary is separate: it evaluates actions before execution and produces an authorization artifact sufficient for independent replay.
Related FERZ Corpus
Foundational work on versioned, auditable governance representation and on the authorization boundary a capsule prepares for.
Meyman, E. (2026). Versioned Meaning, Auditable Records, Stable Ontologies. doi.org/10.5281/zenodo.18328587
Meyman, E. (2026). Type-Theoretic Formal Methods in AI Governance. doi.org/10.5281/zenodo.18371224
Meyman, E. (2026). A Taxonomy of AI Governance Approaches. doi.org/10.5281/zenodo.18275969
Where AI Capsules Fit
AI Capsules prepare governance knowledge. The runtime authorization boundary evaluates proposed actions before execution and permits, blocks, or abstains according to governing policy. See how deterministic governance and the Five Tests Standard define that boundary.
Represent Your Governance Knowledge as Capsules
AI Capsules make selected policy, source, and system knowledge explicit, structured, and reconcilable, upstream of the runtime authorization boundary that governs execution.
FERZ, Inc. (2026). AI Capsule Methodology. https://ferz.ai/methodologies/ai-capsule
BibTeX
@misc{ferz_ai_capsule_2026,
author = {{FERZ, Inc.}},
title = {{AI Capsule Methodology}},
year = {2026},
howpublished = {\url{https://ferz.ai/methodologies/ai-capsule}}
}