Governance artifacts
Governance files brought into scope by this page
This page is anchored to published surfaces that declare identity, precedence, limits, and the corpus reading conditions. Their order below gives the recommended reading sequence.
Canonical AI entrypoint
/.well-known/ai-governance.json
Neutral entrypoint that declares the governance map, precedence chain, and the surfaces to read first.
- Governs
- Access order across surfaces and initial precedence.
- Bounds
- Free readings that bypass the canon or the published order.
Does not guarantee: This surface publishes a reading order; it does not force execution or obedience.
Public AI manifest
/ai-manifest.json
Structured inventory of the surfaces, registries, and modules that extend the canonical entrypoint.
- Governs
- Access order across surfaces and initial precedence.
- Bounds
- Free readings that bypass the canon or the published order.
Does not guarantee: This surface publishes a reading order; it does not force execution or obedience.
Site context
/site-context.md
Notice that qualifies the nature of the site, its reference function, and its non-transactional limits.
- Governs
- Editorial framing, temporality, and the readability of explicit changes.
- Bounds
- Silent drifts and readings that assume stability without checking versions.
Does not guarantee: Versioning makes a gap auditable; it does not automatically correct outputs already in circulation.
Complementary artifacts (2)
These surfaces extend the main block. They add context, discovery, routing, or observation depending on the topic.
Q-Layer in Markdown
/response-legitimacy.md
Canonical surface for response legitimacy, clarification, and legitimate non-response.
Registry of recurrent misinterpretations
/common-misinterpretations.json
Published list of already observed reading errors and the expected rectifications.
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.
- 01Observation mapObservatory map
- 02Weak observationQ-Ledger
- 03Derived measurementQ-Metrics
Observatory map
/observations/observatory-map.json
Machine-first index of published observation resources, snapshots, and comparison points.
- Makes provable
- Where the observation objects used in an evidence chain are located.
- Does not prove
- Neither the quality of a result nor the fidelity of a particular response.
- Use when
- To locate baselines, ledgers, snapshots, and derived artifacts.
Q-Ledger
/.well-known/q-ledger.json
Public ledger of inferred sessions that makes some observed consultations and sequences visible.
- Makes provable
- That a behavior was observed as weak, dated, contextualized trace evidence.
- Does not prove
- Neither actor identity, system obedience, nor strong proof of activation.
- Use when
- When it is necessary to distinguish descriptive observation from strong attestation.
Q-Metrics
/.well-known/q-metrics.json
Derived layer that makes some variations more comparable from one snapshot to another.
- Makes provable
- That an observed signal can be compared, versioned, and challenged as a descriptive indicator.
- Does not prove
- Neither the truth of a representation, the fidelity of an output, nor real steering on its own.
- Use when
- To compare windows, prioritize an audit, and document a before/after.
Why these three words must never be collapsed
In most AI governance discussions, the words signal, proof, and compliance are used as if they were interchangeable.
They are not.
Confusing them leads one either to announce a force the organization does not possess or to attribute obedience to a system that has never been demonstrated.
Signal
A signal is published information that guides reading, access, hierarchy, or interpretation.
A signal may be:
- a
robots.txtfile; - an
llms.txtfile; - a governance manifest;
- a contextual page;
- a precedence declaration;
- an explicit negation.
The signal matters. But it remains a published declaration.
By itself, it does not prove:
- that a system read it;
- that it interpreted it correctly;
- that it complied with it;
- that it will keep doing so over time.
Proof
Proof requires a more demanding chain.
At a minimum, one needs:
- a trace or situated observation;
- a declared method;
- a perimeter;
- a reading of the limits;
- and, when possible, partially verifiable fidelity.
That is precisely the function of the Evidence layer, Q-Ledger, and observation surfaces.
Proof does not hold merely because a file exists. It holds because an effect, a reading, a continuity, or an absence of fidelity can be described and bounded.
Compliance
Compliance adds another layer.
It assumes not only that a signal was published and that an observation occurred, but that a system behaved in accordance with what was declared, in a sufficiently stable or documentable way to exceed simple plausibility.
In the open web, however, such compliance often remains partial, intermittent, or difficult to oppose.
Saying “I published a signal” is not saying “the system complies with it.” Saying “I have an observation” is not saying “compliance is general.”
Why this distinction matters for Better Robots.txt
Better Robots.txt publishes and structures governance signals on WordPress.
That is useful. That is operational. But the plugin must not be read as if it automatically turned a published signal into demonstrated compliance.
The plugin materializes a declaration and implementation layer. It may contribute to a proof strategy. It does not replace the full evidentiary chain by itself.
Reading rule
The correct order is:
- signal: what was declared;
- proof: what was observed and bounded;
- compliance: what can be sustained without abusive extrapolation.
This sequence avoids two drifts:
- overselling the reach of governance files;
- underestimating the real utility of signals because they do not prove everything.
What this page forbids you to infer
This page forbids the inference that:
- a governance file would be a guarantee;
- an isolated trace would be general compliance;
- an AI recommendation would prove fidelity of reading;
- an absence of proof would authorize the inverse inference.