FUURAA AI Knowledge Library · Downloadable record template

AI Agent Material Change Decision Record

Use sixteen fields to connect a candidate update to its commissioning baseline, seven change surfaces, evidence transfer, predeclared verification, rollback controls, independent authorisation and transition review. Entry, import and export stay in this device's browser.

Tool statusPublicly usable · no upload by defaultRecord rulefuuraa.ai-agent-material-change-decision-record/v1Sources checked16 August 2026

Change decision workspace

Prove what changed and which evidence stopped transferring before deciding what the old authority still covers.

Start blank or load the clearly labelled demonstration record. Completeness checks fields only; it does not decide materiality, evidence authenticity or release suitability.

0%16 fields remain incomplete.

01

Freeze baseline and candidate

Name the exact operating decision, candidate and accountable change owner.

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02

Inventory seven change surfaces

Record direct, transitive and provider-originated changes—including what remains unknowable.

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03

Bound evidence transfer

Separate evidence that still covers the candidate from findings whose assumptions changed.

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04

Predeclare and run proportionate verification

Freeze the plan before results, retain failures and prove control continuity.

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05

Issue and observe an expiring change decision

Classify the change, preserve dissent, bind authority and verify the transition.

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JSON

Machine-readable change decision record preview

JSON only saves and hands off this template. This structure is not an international standard and does not replace change control, risk assessment, legal determination, audit or certification.

{
  "schema": "fuuraa.ai-agent-material-change-decision-record/v1",
  "identity": {
    "baseline_decision_and_release_id": "",
    "candidate_release_and_change_owner": ""
  },
  "change_inventory": {
    "machine_readable_component_diff": "",
    "direct_and_transitive_dependencies": "",
    "provider_originated_changes_and_unknowns": "",
    "affected_users_tasks_and_environments": "",
    "authority_and_data_boundary_delta": "",
    "harm_reliance_and_reversibility_analysis": ""
  },
  "evidence_transfer": {
    "prior_evidence_retained": "",
    "prior_evidence_invalidated": ""
  },
  "verification": {
    "predeclared_verification_plan": "",
    "test_results_and_retained_failures": "",
    "control_rollback_and_migration_evidence": ""
  },
  "decision": {
    "decision_class_conditions_and_dissent": "",
    "authoriser_effective_time_and_expiry": "",
    "deployment_proof_and_transition_review": ""
  }
}

Applicability boundary

Field completeness does not mean a change is immaterial or a candidate is ready to release.

What the tool can do

  • Bind a candidate to an exact baseline decision and component diff
  • Separate retained from invalidated evidence instead of inheriting conclusions by default
  • Keep verification, dissent, authorisation, expiry and transition proof in one JSON

What the tool cannot prove

  • That the inventory is complete, providers made no silent update or evidence is untampered
  • That tests are sufficient, controls effective, rollback viable or the candidate safe
  • That a decision satisfies jurisdictional, sector, contractual, audit or certification requirements

FUURAA analysisMaterial-change review is not mainly about labelling an update. It is about whether the causal links supporting prior findings still hold. This record keeps component differences, use boundaries, evidence transfer and renewed verification together. Unobservable dependency changes remain unknowns; they cannot silently become ‘immaterial’.

Primary sources and boundaries

Use primary frameworks to build an inspectable record while preserving each source's own boundary.

Published 26 January 2023

NIST AI RMF 1.0

Connects governance, context, measurement and risk treatment across the AI lifecycle.

BoundaryVoluntary and use-case agnostic; it does not define a universal material-change threshold or approve a release.

Open primary source ↗
Living resource · checked 16 August 2026

NIST AIRC · AI RMF Playbook: Map

Connects system changes to affected context, people, impacts and accountable lifecycle roles.

BoundarySuggested actions are informative and context dependent; the Playbook is being updated with the AI RMF.

Open primary source ↗
Living resource · checked 16 August 2026

NIST AIRC · AI RMF Playbook: Manage

Connects post-deployment monitoring, change management, renewed TEVV, risk response and decommissioning.

BoundaryIt does not prescribe this record, sector duties or evidence sufficient for every agent.

Open primary source ↗
Final published 26 July 2024

NIST SP 800-218A

Adds AI-specific secure-development practices for model producers, system producers and acquirers.

BoundarySecure-development guidance does not establish operating safety, change materiality or release approval.

Open primary source ↗
Published 27 November 2023

UK NCSC · Secure operation and maintenance

Calls for behaviour and input monitoring, secure-by-design updates and operational learning.

BoundaryHigh-level security guidance does not set task-specific tests, legal duties or universal thresholds.

Open primary source ↗
W3C Recommendation · 30 April 2013

W3C PROV-DM

Models entities, activities, agents, time, derivation and responsibility for provenance.

BoundaryProvenance can show lineage; it does not prove truth, control effectiveness or release suitability.

Open primary source ↗

Method and evidence chain

Move from the full protocol to record verification, then carry transition evidence into the operating-period record.

Check this material change decision recordRead material change and recommissioningBuild an Agent operating-period recordEnter AI Evidence AtlasReturn to AI Knowledge Library