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.
Interpretation policy
/.well-known/interpretation-policy.json
Published policy that explains interpretation, scope, and restraint constraints.
- Governs
- Response legitimacy and the constraints that modulate its form.
- Bounds
- Plausible but inadmissible responses, or unjustified scope extensions.
Does not guarantee: This layer bounds legitimate responses; it is not proof of runtime activation.
Definitions canon
/canon.md
Canonical surface that fixes identity, roles, negations, and divergence rules.
- Governs
- Public identity, roles, and attributes that must not drift.
- Bounds
- Extrapolations, entity collisions, and abusive requalification.
Does not guarantee: A canonical surface reduces ambiguity; it does not guarantee faithful restitution on its own.
Complementary artifacts (1)
These surfaces extend the main block. They add context, discovery, routing, or observation depending on the topic.
Identity lock
/identity.json
Identity file that bounds critical attributes and reduces biographical or professional collisions.
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.
- 01Response authorizationQ-Layer: response legitimacy
- 02Weak observationQ-Ledger
- 03AttestationQ-Attest protocol
Q-Layer: response legitimacy
/response-legitimacy.md
Surface that explains when to answer, when to suspend, and when to switch to legitimate non-response.
- Makes provable
- The legitimacy regime to apply before treating an output as receivable.
- Does not prove
- Neither that a given response actually followed this regime nor that an agent applied it at runtime.
- Use when
- When a page deals with authority, non-response, execution, or restraint.
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-Attest protocol
/.well-known/q-attest-protocol.md
Optional specification that cleanly separates inferred sessions from validated attestations.
- Makes provable
- The minimal frame required to elevate an observation toward a verifiable attestation.
- Does not prove
- Neither that an attestation endpoint exists nor that an attestation has already been received.
- Use when
- When a page deals with strong proof, operational validation, or separation between evidence levels.
The presence of a human at the end of the chain is not enough to make an agentic system governed. In many architectures, final approval comes only after the agent has already framed the problem, prioritized options, selected sources, proposed the action, and sometimes silently excluded alternatives. The human does not always cancel the decision. They may simply ratify it.
The false comfort of “human in the loop”
The expression sounds reassuring. Yet it often hides a weaker reality: the human intervenes only after a long interpretive process already carried out by the system. If the agent has:
- picked the right or wrong tool;
- retained a scope that is too broad;
- removed options during summarization;
- presented a hypothesis as the natural path,
then final approval applies to a world that has already been framed.
Where the decision really moves
In agentic systems, the decision shifts upstream:
- when the sub-problem is formulated;
- when a tool is selected;
- when risks are prioritized;
- when escalation is chosen or avoided;
- when an ambiguous request is turned into a plausible action.
If those moments are not governed, final human approval looks like an administrative stamp applied to a trajectory that was already chosen.
What real human supervision requires
Serious human supervision is not merely clicking “approve.” It requires at least:
- a trace showing what was arbitrated;
- visible alternatives;
- enforceable response conditions;
- a real possibility to refuse, escalate, or request silence;
- scopes that prevent the agent from pre-deciding outside its mandate.
Without those elements, the human becomes the psychological support of a system that is already decision-making.
Why this matters
The issue is not theoretical. The more the agent is integrated into workflows, the more final approval risks becoming ritual. It appears to protect the organization while leaving the responsibility shift untouched. A ritualized control layer is often more dangerous than openly acknowledging the absence of validation, because it creates an illusion of control.