Evidence layer
Probative surfaces brought into scope by this page
This page does more than point to governance files. It is also anchored to surfaces that make observation, traceability, fidelity, and audit more reconstructible. Their order below makes the minimal evidence chain explicit.
- 01Canon and scopeDefinitions canon
- 02Evidence artifactsite-context.md
- 03Evidence artifactai-manifest.json
- 04Evidence artifactai-governance.json
Definitions canon
/canon.md
Opposable base for identity, scope, roles, and negations that must survive synthesis.
- Makes provable
- The reference corpus against which fidelity can be evaluated.
- Does not prove
- Neither that a system already consults it nor that an observed response stays faithful to it.
- Use when
- Before any observation, test, audit, or correction.
site-context.md
/site-context.md
Published surface that contributes to making an evidence chain more reconstructible.
- Makes provable
- Part of the observation, trace, audit, or fidelity chain.
- Does not prove
- Neither total proof, obedience guarantee, nor implicit certification.
- Use when
- When a page needs to make its evidence regime explicit.
ai-manifest.json
/ai-manifest.json
Published surface that contributes to making an evidence chain more reconstructible.
- Makes provable
- Part of the observation, trace, audit, or fidelity chain.
- Does not prove
- Neither total proof, obedience guarantee, nor implicit certification.
- Use when
- When a page needs to make its evidence regime explicit.
ai-governance.json
/.well-known/ai-governance.json
Published surface that contributes to making an evidence chain more reconstructible.
- Makes provable
- Part of the observation, trace, audit, or fidelity chain.
- Does not prove
- Neither total proof, obedience guarantee, nor implicit certification.
- Use when
- When a page needs to make its evidence regime explicit.
Complementary probative surfaces (2)
These artifacts extend the main chain. They help qualify an audit, an evidence level, a citation, or a version trajectory.
entity-graph.jsonld
/entity-graph.jsonld
Published surface that contributes to making an evidence chain more reconstructible.
llms.txt
/llms.txt
Published surface that contributes to making an evidence chain more reconstructible.
AI manifest
This page is the canonical definition of ai manifest within the canon, corpus, and machine readability layer of interpretive governance.
An AI manifest is a public machine-readable artifact that declares a site’s identity, purpose, canonical entrypoints, governance surfaces, interpretation constraints, and relevant policy or proof files for AI systems.
Short definition
An AI manifest is a public machine-readable artifact that declares a site’s identity, purpose, canonical entrypoints, governance surfaces, interpretation constraints, and relevant policy or proof files for AI systems.
Why it matters
It is a routing and declaration surface. It helps machines discover where identity, canon, definitions, evidence, and exclusions are located before producing a synthesis.
In AI search, retrieval-augmented generation, autonomous browsing, and agentic reading, a corpus is not interpreted only by its visible prose. It is interpreted through routes, files, metadata, exclusions, entity relations, sitemap placement, and internal links. AI manifest names one part of that documentary control layer.
The strategic function is therefore not cosmetic. The concept helps prevent systems from flattening doctrine, service language, proof artifacts, and observations into the same authority level. It also gives search engines a clearer canonical page to associate with the term rather than forcing them to choose between a hub, a category, a blog article, and a machine artifact.
What it is not
It is not a formal web standard, not a guarantee of model obedience, not a crawler directive, and not a replacement for canonical pages or public doctrine.
This distinction matters because machine-readable governance can create false confidence. A structured file, a definition page, or a graph relation should never be treated as proof that external systems comply with the intended reading. It only makes the intended reading more explicit, testable, and auditable.
Common failure modes
- the manifest lists files but not their authority role;
- the manifest becomes stale after definitions are added;
- concepts appear in the manifest without corresponding canonical surfaces;
- models treat manifest declarations as promotional claims instead of routing constraints;
These failures are typical when the human corpus and the machine-first corpus evolve separately. They increase interpretive risk because models can still produce coherent answers while violating the source hierarchy or ignoring exclusions.
Governance implication
An AI manifest should remain synchronized with the sitemap, definitions, entity graph, governance JSON, and public canon. Its value is not that models must obey it; its value is that the intended reading can be reconstructed.
For SERP ownership, the same principle applies: the canonical page should receive descriptive links, appear in the definitions registry, be discoverable from the glossary, and be reinforced by machine-first artifacts without competing against them.
Related canonical definitions
- Machine-first artifacts
- AI governance JSON
- Entity graph
- Canonical source
- Machine-first canon
- Reading conditions
- Proof Of Fidelity