TC260 Framework 3.0 Appendix 2: agentic AI risk management (voluntary; 2026-09-14)
Unique identity and least-privilege permissions per agent by decision mode; human checkpoints with tamper-proof approval logs and deny-by-default; tool and skill verification; runtime guardrails (alert, restrict, intercept, suspend, terminate); memory isolation with no credentials in memory; mutual authentication; sandbox validation, red teaming and re-validation on major change; controlled decommissioning
AIGE-OBL-CN-TC260-AGENTS. Drawn from chapter 08.
Text alternative
- Clause: TC260 Framework 3.0, Framework 3.0 Appendix 2.
- Duty holder: Not stated.
- Applies from: No date, Voluntary.
- Artefact: Agent registry with identity….
- Layers: Layer 02, Layer 03, Layer 04, Layer 05.
- Evidence record: Evidence record v1.
- Record schema: Evidence record.
- The same topic in 25 other frameworks; the crosswalk section below links each clause.
- Id
AIGE-OBL-CN-TC260-AGENTS- Instrument
- TC260 AI Safety Governance Framework 3.0 framework
- Clause
- Framework 3.0 Appendix 2
- Applies from
- No date Voluntary · published 2026-09-14
The artefact that evidences it
Agent registry with identity and scope; approval-log store; tool allow-list with integrity checks; runtime guardrails and kill switch; memory-scope policy; decommissioning runbook.
Data table: every relation
| Family | Item | Relation |
|---|---|---|
| Open controls | AIGE-CTL-EVAL-006 Stop Conditions | Direct |
| Same crosswalk topic | AI Verify 9.12.1, 5.5.1, 3.4.1, 9.8.1, 5.10.1, 10.3.1, 10.3.2, 9.4.1, 4.8.1, 4.1.1, 6.1.1, 5.2.1, 5.9.1, 9.5.4, 1.5.1, 1.6.1, 5.11.1, 9.7.1, 5.3.1, 4.5.2, 9.3.1 | Core clause |
| Same crosswalk topic | CSA AICM STA-08, IAM-03, LOG-09, LOG-12, GRC-15, TVM-13, AIS-09, AIS-10, MDS-06, MDS-07, AIS-05, SEF-07, SEF-08, STA-10, STA-09, IAM-18, AIS-11, IAM-12, AIS-13 | Core clause |
| Same crosswalk topic | China Algo. Rec. AlgoRec Art. 24, AlgoRec Art. 17, AlgoRec Art. 8, AlgoRec Art. 9, AlgoRec Art. 7 | Core clause |
| Same crosswalk topic | China Deep Synthesis DeepSyn Art. 19, DeepSyn Art. 10, DeepSyn Art. 15, DeepSyn Art. 20, DeepSyn Art. 14 | Core clause |
| Same crosswalk topic | China GenAI Measures GenAI Art. 17, GenAI Art. 10, GenAI Art. 14, GenAI Art. 15, GenAI Art. 7 | Core clause |
| Same crosswalk topic | CoE Convention CoE Art. 8, CoE Art. 16(2)(g), CoE Art. 13 | Core clause |
| Same crosswalk topic | EU AI Act Art. 49, Art. 71, Art. 6, Art. 3(1), Art. 52, Art. 12, Art. 26(6), Art. 26, Art. 19, Art. 14, Art. 14(4)(b), Art. 5, Art. 15, Art. 5(1)(a)–(b), Art. 55, Art. 60, Art. 15(3), Art. 9, Art. 42(3), Art. 72, Art. 73, Art. 26(5), Art. 3(49), Art. 20, Art. 25, Art. 25(4), Art. 22, Art. 23, Art. 24, Art. 54, Art. 57, Art. 58, Art. 59, Art. 61 | Core clause |
| Same crosswalk topic | G7 Code G7 Action 1, G7 Action 2, G7 Action 4, G7 Action 11 | Core clause |
| Same crosswalk topic | GAO AI Accountability 3.1, 4.3, 3.9, 3.7, 3.2, 4.1, 4.2, 2.6 | Core clause |
| Same crosswalk topic | GB/T 45654 GB/T 45654 Security assessment | Core clause |
| Same crosswalk topic | GDPR Art. 22, Art. 32, Arts. 33–34, Art. 28, Arts. 44–46 | Core clause |
| Same crosswalk topic | GPAI Code Safety C5, Safety 3.2, Safety C6, Safety C9, Safety 3.5, Transparency 1.2 | Core clause |
| Same crosswalk topic | ISO 42001 A.4, A.6, A.6.2.8, A.9, 9.1, A.8, 10.2, A.10, A.6.2.4 | Core clause |
| Same crosswalk topic | Korea AI Act Art. 33, Art. 34(1)(5), Art. 34(1)(4), Art. 32(1) | Core clause |
| Same crosswalk topic | NIST AI RMF GOVERN 1.6, GOVERN 1.7, MANAGE 4, MEASURE 3, MANAGE 2.4, MAP 3.5, GOVERN 3.2, MANAGE 2, MEASURE 2, MEASURE 2.7, MEASURE 2.1, MEASURE 1, MANAGE 4.3, GOVERN 4.3, GOVERN 6, MAP 4, MANAGE 3, MANAGE 3.1, GOVERN 6.2, MEASURE 2.3 | Core clause |
| Same crosswalk topic | OECD AI Principles OECD 1.5(b), OECD 1.2(b), OECD 1.4 | Core clause |
| Same crosswalk topic | OWASP Agentic ASI09, ASI05, ASI04, ASI03, ASI02, ASI07, ASI10 | Core clause |
| Same crosswalk topic | OWASP LLM LLM01:2026, LLM10:2026, LLM06:2026, LLM04:2026, LLM03:2026 | Core clause |
| Same crosswalk topic | Singapore Agentic Agentic 2.3.3, Agentic 2.2.2, Agentic 2.3.1, Agentic 2.3.2, Agentic 2.1.2 | Core clause |
| Same crosswalk topic | Singapore GenAI GenAI 5, GenAI 6, GenAI 4 | Core clause |
| Same crosswalk topic | TC260 Framework 3.0 TC260 4.4.1, TC260 5.3.6, TC260 3.2.1, TC260 3, TC260 5.3.14, TC260 5.3.7, TC260 5.3.18, TC260 4.4.4 | Core clause |
| Same crosswalk topic | UK ATRS ATRS Tier 1, ATRS 2.3.2, ATRS 2.1.4 | Core clause |
| Same crosswalk topic | UK DUAA UK GDPR Art. 22C | Core clause |
| Same crosswalk topic | prEN 18229-1 prEN 18229-1 | Core clause |
| Same crosswalk topic | China AI Labelling Label Art. 5 | Related clause |
Patterns that build it
No pattern in the catalogue names this clause on its "Maps to" line yet; the artefact above is the engineering answer.
The same topic in other frameworks
From the topic crosswalk: the clauses filed under the same topics as this one. Mappings are illustrative, not a claim of conformity.
Inventory and registration
- EU AI Act Art. 49 Registration (core)
- EU AI Act Art. 71 EU database for high-risk AI systems (core)
- ISO 42001 A.4 Resources for AI systems (core)
- NIST AI RMF GOVERN 1.6 GOVERN 1.6: Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities (core)
- China Algo. Rec. AlgoRec Art. 24 Algorithm filing (core)
- China Deep Synthesis DeepSyn Art. 19 Filing for public-opinion services (core)
- China GenAI Measures GenAI Art. 17 Algorithm filing (core)
- UK ATRS ATRS Tier 1 Summary information (the published record) (core)
- AI Verify 9.12.1 Accountability: inventory AI systems and their data, tooling, computing and human resources according to risk priorities (core)
- EU AI Act Art. 6 Classification rules for high-risk AI systems
- TC260 Framework 3.0 TC260 4.4.1 CII registration and filing
- EU AI Act Art. 3(1) Definition of an AI system
- EU AI Act Art. 52 Procedure
- NIST AI RMF GOVERN 1.7 GOVERN 1.7: Processes and procedures are in place for decommissioning and phasing out AI systems safely and in a manner that does not increase risks or decrease the organization's trustworthiness
- CSA AICM STA-08 Supply Chain Inventory
- CSA AICM IAM-03 Identity Inventory
- Korea AI Act Art. 33 Confirmation of high-impact AI
- GAO AI Accountability 3.1 Documentation: catalog model and non-model components, along with operating specifications and parameters
- AI Verify 5.5.1 Security: identify, track and protect AI-related assets (models, data, prompts, logs, assessments)
Logging and traceability
- EU AI Act Art. 12 Record-keeping (core)
- ISO 42001 A.6 AI system life cycle (core)
- TC260 Framework 3.0 TC260 5.3.6 Logs kept and audited (core)
- EU AI Act Art. 26(6) Deployers keep the automatically generated logs (core)
- ISO 42001 A.6.2.8 AI system recording of event logs (core) (clause not verified)
- CSA AICM LOG-09 Log Records (core)
- Korea AI Act Art. 34(1)(5) Documents showing the measures taken (core)
- OECD AI Principles OECD 1.5(b) Traceability of datasets, processes and decisions (core)
- prEN 18229-1 prEN 18229-1 AI trustworthiness framework, Part 1: logging (draft; supports Art. 12) (core) (clause not verified)
- GAO AI Accountability 4.3 Traceability: document results of monitoring activities and any corrective actions taken (core)
- AI Verify 3.4.1 Reproducibility: log all inputs and inference outputs of the model and keep them for a reasonable time for review (core)
- AI Verify 9.8.1 Accountability: process or governance-by-design technology for auditability: versioned code, models and data, and an audit trail of deployments and inputs/outputs (core)
- EU AI Act Art. 26 Obligations of deployers of high-risk AI systems
- NIST AI RMF MANAGE 4 MANAGE 4: Risk treatments, including response and recovery, and communication plans for the identified and measured AI risks are documented and monitored
- NIST AI RMF MEASURE 3 MEASURE 3: Mechanisms for tracking identified AI risks over time are in place
- China AI Labelling Label Art. 5 Implicit metadata labels
- EU AI Act Art. 19 Automatically generated logs
- CSA AICM LOG-12 Transaction/Activity Logging
- Singapore Agentic Agentic 2.3.3 When deploying, continuously monitor and test
- AI Verify 5.10.1 Security: monitor and log inputs to the AI system (queries, prompts, requests)
Human oversight
- EU AI Act Art. 14 Human oversight (core)
- ISO 42001 A.9 Use of AI systems (core)
- NIST AI RMF MANAGE 2.4 MANAGE 2.4: Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use (core)
- GDPR Art. 22 Automated individual decision-making, including profiling (core)
- NIST AI RMF MAP 3.5 MAP 3.5: Processes for human oversight are defined, assessed, and documented in accordance with organizational policies from the GOVERN function (core)
- CSA AICM GRC-15 Human supervision (core)
- Korea AI Act Art. 34(1)(4) Human management and supervision (core)
- UK DUAA UK GDPR Art. 22C Safeguards for automated decision-making (core)
- UK ATRS ATRS 2.3.2 Human review (core)
- Singapore Agentic Agentic 2.2.2 Design for meaningful human oversight (core)
- GAO AI Accountability 3.9 Human supervision: define and develop procedures for human supervision of the AI system (core)
- AI Verify 10.3.1 Human Agency and Oversight: define the role of the human in oversight and control of the AI system (in, over or out of the loop) (core)
- AI Verify 10.3.2 Human Agency and Oversight: flag decisions the model is significantly unsure of for human review, and monitor human overrides (core)
- EU AI Act Art. 26 Obligations of deployers of high-risk AI systems
- NIST AI RMF GOVERN 3.2 GOVERN 3.2: Policies and procedures are in place to define and differentiate roles and responsibilities for human-AI configurations and oversight of AI systems
- China Algo. Rec. AlgoRec Art. 17 User option to switch off
- China GenAI Measures GenAI Art. 10 User guidance and protection
- EU AI Act Art. 14(4)(b) Awareness of automation bias
- OWASP Agentic ASI09 Human-Agent Trust Exploitation
- CoE Convention CoE Art. 8 Transparency and oversight
- OECD AI Principles OECD 1.2(b) Human agency and oversight safeguards
- AI Verify 9.4.1 Accountability: a manual to deactivate the AI model and a communications plan for disengagement
Runtime guardrails
- ISO 42001 A.9 Use of AI systems (core)
- NIST AI RMF MANAGE 2 MANAGE 2: Strategies to maximize AI benefits and minimize negative impacts are planned, prepared, implemented, documented, and informed by relevant AI actors (core)
- TC260 Framework 3.0 TC260 3.2.1 Technological countermeasures for agentic AI (core)
- China GenAI Measures GenAI Art. 10 Guided, bounded use (core)
- China GenAI Measures GenAI Art. 14 Stop unlawful generation (core)
- China Deep Synthesis DeepSyn Art. 10 Input and output review (core)
- CSA AICM TVM-13 Guardrails (core)
- CSA AICM AIS-09 Input Validation (core)
- CSA AICM AIS-10 Output Validation (core)
- OWASP LLM LLM01:2026 Prompt Injection (core)
- OWASP LLM LLM10:2026 Improper Output Handling (core)
- Singapore Agentic Agentic 2.3.1 During design and development, use technical controls (core)
- EU AI Act Art. 5 Prohibited AI practices
- EU AI Act Art. 15 Accuracy, robustness and cybersecurity
- ISO 42001 A.6 AI system life cycle
- China Algo. Rec. AlgoRec Art. 8 Periodic algorithm review
- China Algo. Rec. AlgoRec Art. 9 Feature database for unlawful content
- EU AI Act Art. 5(1)(a)–(b) Manipulative techniques; exploitation of vulnerabilities
- GPAI Code Safety C5 Commitment 5: Safety mitigations
- OWASP LLM LLM06:2026 Unbounded Consumption
- AI Verify 4.8.1 Safety: assess harms and implement measures that prevent the model generating unlawful or harmful content (CSAM, NCII, CBRN, offensive cyber)
Robustness, security and evaluations
- EU AI Act Art. 15 Accuracy, robustness and cybersecurity (core)
- EU AI Act Art. 55 Obligations for providers of general-purpose AI models with systemic risk (core)
- ISO 42001 A.6 AI system life cycle (core)
- NIST AI RMF MEASURE 2 MEASURE 2: AI systems are evaluated for trustworthy characteristics (core)
- TC260 Framework 3.0 TC260 3 Technological countermeasures (core)
- China Deep Synthesis DeepSyn Art. 15 Technology management and algorithm verification (core)
- China Deep Synthesis DeepSyn Art. 20 Security assessment of new products (core)
- GB/T 45654 GB/T 45654 Security assessment Security-assessment requirements for generative AI services (core) (clause not verified)
- GPAI Code Safety 3.2 Measure 3.2: Model evaluations (core)
- NIST AI RMF MEASURE 2.7 MEASURE 2.7: AI system security and resilience as identified in the MAP function are evaluated and documented (core)
- CSA AICM MDS-06 Adversarial Attack Analysis (core)
- CSA AICM MDS-07 Robustness against Adversarial Attack / Model Hardening (core)
- Singapore GenAI GenAI 5 Testing and Assurance (core)
- Singapore GenAI GenAI 6 Security (core)
- Singapore Agentic Agentic 2.3.2 Before deploying, test agents (core)
- CoE Convention CoE Art. 16(2)(g) Testing before first use and when significantly modified (core)
- OECD AI Principles OECD 1.4 Robustness, security and safety (core)
- G7 Code G7 Action 1 Identify, evaluate and mitigate risks across the lifecycle, including testing (core)
- GAO AI Accountability 3.7 Assessment: assess performance against defined metrics to ensure the AI system functions as intended and is sufficiently robust (core)
- AI Verify 4.1.1 Safety (technical test): run use-case benchmarks and red-teaming to evaluate for safety and possible harms (e.g. hallucination, toxicity) (core)
- AI Verify 6.1.1 Robustness (technical test): compare accuracy with and without added noise to document and monitor performance (core)
- AI Verify 5.2.1 Security: conduct security risk assessments that model threats and risks to the AI system (core)
- EU AI Act Art. 60 Testing of high-risk AI systems in real-world conditions outside AI regulatory sandboxes
- ISO 42001 9.1 Monitoring, measurement, analysis and evaluation
- TC260 Framework 3.0 TC260 5.3.14 Resilience
- China GenAI Measures GenAI Art. 17 Security assessment
- EU AI Act Art. 15(3) Declared accuracy levels and metrics
- EU AI Act Art. 9 Risk management system
- EU AI Act Art. 42(3) Presumption of conformity for cybersecurity (Cyber Resilience Act)
- GPAI Code Safety C6 Commitment 6: Security mitigations
- GDPR Art. 32 Security of processing
- NIST AI RMF MEASURE 2.1 MEASURE 2.1: Test sets, metrics, and details about the tools used during TEVV are documented
- NIST AI RMF MEASURE 1 MEASURE 1: Appropriate methods and metrics are identified and applied
- CSA AICM AIS-05 Application Security Testing
- OWASP LLM LLM01:2026 Prompt Injection
- OWASP Agentic ASI05 Unexpected Code Execution (RCE)
- Korea AI Act Art. 32(1) Safety duties for AI above the compute threshold
- GAO AI Accountability 3.2 Metrics: define performance metrics that are precise, consistent, and reproducible
- AI Verify 5.9.1 Security: release AI systems only after appropriate and effective security checks and evaluation
Incident response and monitoring
- EU AI Act Art. 72 Post-market monitoring by providers and post-market monitoring plan (core)
- EU AI Act Art. 73 Reporting of serious incidents (core)
- ISO 42001 A.8 Information for interested parties (core)
- ISO 42001 10.2 Nonconformity and corrective action (core)
- NIST AI RMF MANAGE 4 MANAGE 4: Risk treatments, including response and recovery, and communication plans for the identified and measured AI risks are documented and monitored (core)
- TC260 Framework 3.0 TC260 5.3.7 Real-time risk monitoring (core)
- TC260 Framework 3.0 TC260 5.3.18 Incident reporting (core)
- China GenAI Measures GenAI Art. 14 Handle and report unlawful content (core)
- China GenAI Measures GenAI Art. 15 Complaint and reporting mechanism (core)
- EU AI Act Art. 26(5) Deployer monitoring, informing the provider and suspending use (core)
- GPAI Code Safety C9 Commitment 9: Serious incident reporting (core)
- GDPR Arts. 33–34 Notification and communication of a personal data breach (core)
- NIST AI RMF MANAGE 4.3 MANAGE 4.3: Incidents and errors are communicated to relevant AI actors, including affected communities. Processes for tracking, responding to, and recovering from incidents and errors are followed and documented (core)
- CSA AICM SEF-07 Incident Management and Response (core)
- CSA AICM SEF-08 Security Breach Notification (core)
- Korea AI Act Art. 32(1) Safety duties for AI above the compute threshold (core)
- Singapore GenAI GenAI 4 Incident Reporting (core)
- Singapore Agentic Agentic 2.3.3 When deploying, continuously monitor and test (core)
- G7 Code G7 Action 2 Identify and mitigate vulnerabilities, incidents and misuse after deployment (core)
- G7 Code G7 Action 4 Responsible information sharing and reporting of incidents (core)
- GAO AI Accountability 4.1 Planning: develop plans for continuous or routine monitoring of the AI system (core)
- GAO AI Accountability 4.2 Drift: establish the range of data and model drift that is acceptable (core)
- AI Verify 9.5.4 Accountability: enable an incident management process to report and respond to actions or decisions that affect the AI system's outcome (core)
- AI Verify 1.5.1 Transparency: communicate to end users whenever an incident has occurred (core)
- EU AI Act Art. 55 Obligations for providers of general-purpose AI models with systemic risk
- China Algo. Rec. AlgoRec Art. 7 Security management and emergency response
- EU AI Act Art. 3(49) Definition of serious incident
- EU AI Act Art. 20 Corrective actions and duty of information
- GPAI Code Safety 3.5 Measure 3.5: Post-market monitoring
- NIST AI RMF MANAGE 2.4 MANAGE 2.4: Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use
- NIST AI RMF GOVERN 4.3 GOVERN 4.3: Organizational practices are in place to enable AI testing, identification of incidents, and information sharing
- AI Verify 1.6.1 Transparency: give end users and external parties the means to report adverse impacts, with whistleblower protection
- AI Verify 5.11.1 Security: monitor AI system outputs and behaviour after deployment and alert system owners to issues
Supply chain and third parties
- EU AI Act Art. 25 Responsibilities along the AI value chain (core)
- ISO 42001 A.10 Third-party and customer relationships (core)
- NIST AI RMF GOVERN 6 GOVERN 6: Policies and procedures are in place to address AI risks and benefits arising from third-party software and data and other supply chain issues (core)
- NIST AI RMF MAP 4 MAP 4: Risks and benefits are mapped for all AI system components including third-party software and data (core)
- NIST AI RMF MANAGE 3 MANAGE 3: AI risks and benefits from third-party entities are managed (core)
- EU AI Act Art. 25(4) Written agreement with third-party suppliers (core)
- GDPR Art. 28 Processor (core)
- NIST AI RMF MANAGE 3.1 MANAGE 3.1: AI risks and benefits from third-party resources are regularly monitored, and risk controls are applied and documented (core)
- CSA AICM STA-10 Supply Chain Risk Management (core)
- CSA AICM STA-09 Service Bill of Material (BOM) (core)
- OWASP LLM LLM04:2026 Supply Chain (core)
- OWASP Agentic ASI04 Agentic Supply Chain Vulnerabilities (core)
- AI Verify 9.7.1 Accountability: clear responsibilities between parties in the supply chain, set in service level agreements and contracts (core)
- AI Verify 5.3.1 Security: secure the supply chain by assessing and monitoring its security risks across the life cycle (core)
- AI Verify 4.5.2 Safety: continuously monitor third-party systems, with a fallback plan and contracts that assign liability (core)
- EU AI Act Art. 26 Obligations of deployers of high-risk AI systems
- TC260 Framework 3.0 TC260 4.4.4 Open-source ecosystem
- China GenAI Measures GenAI Art. 7 Lawful data and model sources
- China Deep Synthesis DeepSyn Art. 14 Providers and technical supporters
- EU AI Act Art. 22 Authorised representatives of providers of high-risk AI systems
- EU AI Act Art. 23 Obligations of importers
- EU AI Act Art. 24 Obligations of distributors
- EU AI Act Art. 54 Authorised representatives of providers of general-purpose AI models
- GPAI Code Transparency 1.2 Providing relevant information
- GDPR Arts. 44–46 Transfers to third countries
- NIST AI RMF GOVERN 6.2 GOVERN 6.2: Contingency processes are in place to handle failures or incidents in third-party data or AI systems deemed to be high-risk
- UK ATRS ATRS 2.1.4 Third party involvement
- G7 Code G7 Action 11 Implement data input measures and protect personal data and intellectual property
- GAO AI Accountability 2.6 Dependency: assess interconnectivities and dependencies of data streams that operationalize the AI system
- AI Verify 9.3.1 Accountability: select AI system suppliers aligned with the organisation's approach, with remedial action when they underperform
Agent identity and autonomy
- CSA AICM IAM-18 Agent Access Restriction (core)
- CSA AICM AIS-11 Agents Security Boundaries (core)
- OWASP Agentic ASI03 Identity and Privilege Abuse (core)
- OWASP LLM LLM03:2026 Excessive Agency (core)
- Singapore Agentic Agentic 2.1.2 Bound risks through design by defining agents limits and permissions (core)
- EU AI Act Art. 14 Human oversight
- NIST AI RMF GOVERN 3.2 GOVERN 3.2: Policies and procedures are in place to define and differentiate roles and responsibilities for human-AI configurations and oversight of AI systems
- CSA AICM IAM-12 Unique Identities
- OWASP Agentic ASI02 Tool Misuse and Exploitation
- OWASP Agentic ASI07 Insecure Inter-Agent Communication
- OWASP Agentic ASI10 Rogue Agents
- Singapore Agentic Agentic 2.2.2 Design for meaningful human oversight
Sandboxes and real-world testing
- EU AI Act Art. 57 AI regulatory sandboxes (core)
- EU AI Act Art. 60 Testing of high-risk AI systems in real world conditions outside AI regulatory sandboxes (core)
- CoE Convention CoE Art. 13 Safe innovation (controlled testing environments) (core)
- EU AI Act Art. 58 Detailed arrangements for, and functioning of, AI regulatory sandboxes
- EU AI Act Art. 59 Further processing of personal data in the AI regulatory sandbox
- EU AI Act Art. 61 Informed consent to participate in testing in real world conditions
- ISO 42001 A.6.2.4 AI system verification and validation (clause not verified)
- NIST AI RMF MEASURE 2.3 MEASURE 2.3: AI system performance or assurance criteria are measured qualitatively or quantitatively and demonstrated for conditions similar to deployment setting(s)
- CSA AICM AIS-13 AI Sandboxing
- Singapore Agentic Agentic 2.3.2 Before deploying, test agents
Open controls that evidence it
Draft controls in the open control profiles that map to this row: each states a requirement and the evidence it must leave behind.
-
AIGE-CTL-EVAL-006Stop Conditions (Evaluation environment profile)
Source
Chapter 08, section China, checked against its sources on the review date above.
Machine-readable
- This obligation:
/api/v1/obligations/aige-obl-cn-tc260-agents.json - The register:
/api/v1/obligations.json· CSV - Schema and stability promise: open data and API
Cite this obligation
García Aibar, J. (2026). TC260 Framework 3.0 Appendix 2: agentic AI risk management (voluntary; 2026-09-14) (AIGE-OBL-CN-TC260-AGENTS). In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0). https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/obligations/aige-obl-cn-tc260-agents. CC BY 4.0
BibTeX
@misc{aige2026obligation,
author = {Jorge García Aibar},
title = {{TC260 Framework 3.0 Appendix 2: agentic AI risk management (voluntary; 2026-09-14) (AIGE-OBL-CN-TC260-AGENTS)}},
howpublished = {In AI Governance Engineering: The Thesis \& Body of Knowledge},
year = {2026},
version = {0.5.0},
doi = {10.5281/zenodo.22956197},
url = {https://aigovernanceengineer.com/obligations/aige-obl-cn-tc260-agents},
note = {Version 0.5.0}
}