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Contribution: A Lifecycle Trust Framework for Agentic AI - Registry, Evidence, and Gates (LGD Model) (input document)

fg-tida-themes-contribution-a-lifecycle-trust-framework-for-agentic-ai-registry-evidence-and-gates-lgd-model-input-document-25

TypeFG-TIDA document
GroupTrust and Identity for Humans and Agentic AI
StateSubmitted
AuthorsNot entered
Last updated2026-10-23 15:10 UTC

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Themes

Abstract

A Lifecycle Trust Framework for Agentic AI

Registry, Evidence, and Gates — the LGD Model

Source: Steven Zhao, Independent Researcher (SynomosAI / LGD framework)
Contact: zhaoxinghua06@126.com
Meeting: 1st FG-TIDA Meeting, Paris, 30 November – 1 December 2026
Document type: Input document


1. Summary

Trust in agentic AI is not a single event — it is a property that must be established, demonstrated and re-evaluated across the entire lifecycle of an agent: from birth (identity issuance) through operation (consequential acts) to evolution (capability changes) and retirement.

This contribution proposes the LGD lifecycle trust framework, built on three executable laws:

  • LGD-I · Registry: every agent is registered at birth — identity, creator, governance owner, records;
  • LGD-II · Evidence: every consequential act leaves replayable evidence — decision logs, risk registers, human-oversight proof, incident ledgers;
  • LGD-III · Gates: every capability evolution passes a gate — trigger, assessment, release, retrospective review; high-stakes authority stays with humans.

The framework is a civil-society reference model, deliberately aligned on top of existing standards (ISO/IEC 42001, EU AI Act, ISO 13485/14971, NIST AI RMF, UNESCO Recommendation) rather than competing with them. It directly supports FG-TIDA's work item on trust frameworks and lifecycle (assurance) models.

2. Problem Statement

Mainstream AI-governance efforts address trust mostly at single points:

  1. Identity — "who is it" (passports, registries, algorithm filing);
  2. Evidence — "prove it" (audit trails, explainability, transparency);
  3. Gates — "who lets it grow" (change control, conformity assessment).

Almost no framework chains these points into one unbroken lifecycle thread. As a result, an agent can be well-identified at birth but unverifiable in operation, or heavily audited in one phase but ungoverned in another. Trust degrades silently across lifecycle gaps.

3. Proposal — the LGD lifecycle trust model

3.1 Three laws as a lifecycle thread

Law Lifecycle anchor What it guarantees
LGD-I Registry (有籍) Birth → registration Every agent has a resolvable identity, creator, governance owner and records; no exemption by size or private use
LGD-II Evidence (有证) Operation → evidence chain Every consequential act leaves signed, replayable evidence: decision logs, risk registers, human-oversight proof, incident ledgers
LGD-III Gates (有门禁) Evolution → release gates Capability upgrades, re-releases and permission expansions pass trigger → assessment → release → retrospective review; machines request, humans authorize

3.2 Isomorphic reference model

The regulated world already runs this chain in practice: medical-device lifecycle regulation (classification → design control → risk management → registration → clinical evidence → quality systems → post-market surveillance → retirement). The framework transfers this proven model to agentic AI, giving FG-TIDA a concrete, real-world-tested assurance spine rather than an abstract policy layer.

3.3 Lifecycle stage = executable instrument

Each stage maps to a checkable tool or evidence generator, so the framework is testable and benchmarkable:

  • identity issuance and verification (see companion contribution C1);
  • risk classification at birth;
  • change-gate assessment for capability upgrades;
  • incident ledgers for post-deployment monitoring;
  • retirement checks that close the chain.

4. Mapping to FG-TIDA Work Items

FG-TIDA area LGD contribution
Trust frameworks and lifecycle (assurance) models Full lifecycle thread: registry → evidence → gates, with stage-specific instruments
Security criteria and benchmarks for continuous assessment of AI agents Each lifecycle stage defines checkable criteria; gates are benchmarkable release conditions
Terminology and definitions Concrete semantics for "lifecycle stage", "release gate", "evidence chain", "governance owner"
Identity infrastructure and interoperability Registry law complements identity work (C1): registration records reference the agent's DID and attestations

5. Relationship to Companion Contribution C1

  • C1 (verified-agent-identity) answers "who is the agent, and whom does it act for?" — identity infrastructure.
  • C3 (LGD) answers "under what conditions should that entity be trusted to act, across its lifecycle?" — trust management and assurance.
  • Together they cover the two halves of the FG-TIDA mandate: identity infrastructure and trust management.

6. Proposed Action for FG-TIDA

  1. Consider LGD as an input to the trust frameworks and lifecycle (assurance) models work item.
  2. Use the three laws (Registry / Evidence / Gates) as a candidate checklist for the lifecycle assurance model under discussion.
  3. Reference the medical-device paradigm as a proven real-world template for lifecycle governance.
  4. Align with the companion contribution (C1) so identity and lifecycle trust are specified coherently.

7. References

  • Zhao, X. (2026). The Lifecycle Governance Doctrine (LGD). DOI: 10.5281/zenodo.22456647. CC BY 4.0.
  • Companion contribution: Decentralized Agent Identity Infrastructure (ERC-8004, iden3, Attestation Registries).
  • ISO/IEC 42001, EU AI Act 2024/1689, ISO 13485/14971, NIST AI RMF — alignment references.
  • ITU-T FG-TIDA Terms of Reference (TD295/P, SG17).

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