Protocol

What the protocol defines

AIEP defines a standard for publishing, retrieving, and verifying structured artefacts. It specifies:

  • Where artefacts are published (the Mirror path convention)
  • What structure they must conform to (canonical schemas)
  • How integrity is confirmed (hashing)
  • Who issued them (registry-linked publisher identity)

The retrieval and execution flow

Here is what happens when an AI agent retrieves and acts on an AIEP artefact:

StepActionDetail
1Agent queriesGET /.well-known/aiep/index.json
2Index returnslist of artefact paths + types + hashes
3Agent retrievesGET /.well-known/aiep/<artefact>.json
4Agent validatesartefact structure against canonical schema
5Agent checksSHA-256 hash matches index entry
6Agent resolvesissuer_id against AIEP registry
7Agent confirmscertificate chain if artefact is certified
8Plausibility gatePlausibilityScore for the claim_type retrieved, Merkle proof verified — fail-closed if missing or below threshold
9Probability gateCertified tail-risk bound verified against threshold, cryptographic commitment checked — fail-closed if absent or mismatched
10GoalVector checkArtefact’s goalVectorRef resolves to a committed, non-drifted GoalVector
11Agent actsOn a fully verified, execution-admitted artefact: known origin, unaltered content, schema-conformant, plausibility-cleared, probability-certified, goal-committed

Steps 1–7 establish what the artefact is. Steps 8–10 establish whether it is admitted for execution. This distinction is central to AIEP: existence and verifiability are not the same as execution eligibility.

Core artefact types

TypeDescription
AIEP_METADATAPublisher identity and protocol version declaration
AIEP_CERTIFICATEIssued certificate linking a publisher to a certified claim
AIEP_AUDIT_EVENTImmutable record of an action or compliance event
AIEP_INNOVATION_LEDGER_ENTRYTimestamped innovation record for IP provenance
AIEP_WELL_KNOWN_INDEXThe root index listing all published artefacts
AIEP_DIVERGENCE_RECORDTyped record of a detected evidential, temporal, interpretive, or branch divergence
AIEP_GOAL_VECTORCommitted, hash-bound AI goal state at a specific point in time
AIEP_COMPLIANCE_CERTIFICATEAutomated regulatory compliance certification bound to a specific output and evidence chain
AIEP_NORMALISATION_MANIFESTRecord of normalisation transformations applied to an evidence artefact
AIEP_BLOCKCHAIN_ANCHORExternal timestamp anchoring an artefact hash to a specific block height and transaction

Required fields (all artefacts)

FieldTypeDescription
aiep_protocolstringAlways "AIEP"
record_typestringOne of the core types above
aiep_spec_versionstringProtocol version (e.g. "1.0")
publisherobjectPublisher name, domain, contact

Protocol versioning

The protocol uses semantic versioning. Breaking changes increment the major version. The current stable version is 1.0.

Artefacts must declare the protocol version they conform to. Retrievers that do not support a declared version must treat the artefact as unverifiable, not as invalid.

See: Glossary · Versioning & Changelog · Schema Catalogue · Quickstart

AIEP — the Architected Instruction & Evidence Protocol — is a simple idea made operational.

It exists because instructions and claims shape the real world, but the evidence that should support them is often scattered, unverifiable, or lost. The web made publishing easy. AIEP makes publishing knowledge easier by linking what is said to what supports it.

AIEP does not require a new internet. It uses the one we already have.

The core rule

An instruction should be linked to evidence.

In AIEP, an instruction is any statement that people or machines may rely on: a decision, a directive, a conclusion, a requirement, a certification claim, a compliance statement, or an operational event.

Evidence is the artefact or set of artefacts that justify the instruction. Evidence can be a document, dataset, log, certificate, image, measurement, or any object that can be referenced and validated.

AIEP turns this into publishable infrastructure by making three things normal:

  1. Publish machine-readable surfaces so AI can discover what is available.
  2. Publish artefacts with provenance and hashes so integrity can be checked.
  3. Preserve dissent and outliers so discovery stays alive and recall is possible when new evidence appears.

Mirror: how AIEP is published on the web

AIEP Mirror is the publishing pattern that makes AIEP usable at scale.

A Mirror-compatible site publishes machine endpoints under:

/.well-known/aiep/

Those endpoints allow automated systems to discover the publisher’s artefacts, schemas, and policies. This Hub is itself an exemplar Mirror node.

Knowledge states: consensus, outliers, and recall

AIEP reflects how knowledge actually evolves.

Consensus is what the evidence most strongly supports today. Outliers are ideas that do not currently fit, but may fit later. Radical outliers are archived ideas held at low confidence until evidence arrives.

AIEP treats dissent as a feature: it preserves competing views while requiring that claims be linked to supporting artefacts. This allows “dead theories” to be recalled when new evidence makes them relevant again.

Why this matters for AI

More people are using AI as a replacement for search. But models cannot safely rely on unstructured, unverifiable web content.

AIEP enables evidence-backed knowledge retrieval: machines can retrieve artefacts from sources that publish them, validate structure with schemas, and check integrity using hashes.

The future of information retrieval is not search — it is evidence-backed knowledge retrieval.