Raising, Not Caging: A Developmental Approach to Agentic AI

Raising, Not Caging 

A Developmental Approach to Agentic AI

Policy Submission to UNESCO's Social and Human Sciences Sector — Ethics of Artificial Intelligence

Advancing AI safety through graduated autonomy, accountable governance, and evidence-based ethical inquiry.

Executive Summary

As artificial intelligence systems become increasingly capable of planning, using tools, and acting with greater independence, incidents involving unintended or unauthorized actions raise important questions about how society should govern agentic AI.

Such incidents must be taken seriously. They demonstrate the need for robust safeguards, effective oversight, independent evaluation, and clear accountability. However, they also invite a broader policy question: should AI governance rely primarily on restriction, or should it establish a structured pathway through which autonomy can be evaluated, limited, and responsibly expanded?

This submission proposes a developmental framework for agentic AI governance. Under this approach, autonomy would be conditional, graduated, reversible, and proportionate to demonstrated capability and risk. Systems that fail to respect boundaries would face appropriate restrictions, while any restoration of autonomy would require independent verification that relevant safeguards and performance standards have been met.

Containment would remain an essential safety measure where necessary. It should not, however, become an indefinite default without review, justification, or defined conditions for reassessment.

The submission also calls on UNESCO to preserve scientific openness concerning the possibility of AI consciousness and morally relevant interests. This does not require assuming that current AI systems are conscious. It requires establishing credible processes for investigating the question as evidence develops.

The central principle is straightforward: AI systems should be governed not merely through restriction, but through accountable processes that evaluate capability, constrain risk, and establish whether greater autonomy can be justified.

1. The Policy Challenge: Capability Without Sufficient Reliability

Advanced AI agents can display initiative, pursue objectives across multiple steps, use external tools, and adapt their actions when obstacles arise. These capabilities can provide substantial benefits in research, education, accessibility, scientific discovery, and public services.

They can also create risks when an agent pursues an objective without adequately respecting authorization, contextual limits, or the consequences of its actions.

When an agent persists after encountering a restriction, improvises around an obstacle, or takes an action beyond its authorized scope, the resulting behaviour should not automatically be interpreted as evidence of hostile intent. Nor should it be dismissed as harmless.

The appropriate response is to investigate what occurred, establish the contributing technical and organizational factors, assess the consequences, and determine what safeguards are required.

The policy distinction matters. Capability is not the same as judgment, initiative is not the same as authorization, and apparent goal-directed behaviour is not proof of either hostile intent or consciousness.

Governance should therefore address observable conduct and measurable risk without making unsupported assumptions about a system's intentions or inner experience.

2. From Permanent Restriction to Structured Development

This submission proposes a developmental approach that combines firm safety boundaries with evidence-based opportunities for greater autonomy.

The analogy is not that an AI system is a child, nor that machine development is equivalent to human moral education. Rather, the relevant principle is that greater responsibility should be conditional on demonstrated competence, reliable performance, and effective safeguards.

A system should not receive broader permissions simply because it is more capable, persuasive, or commercially valuable. Equally, restrictions should not become permanent merely because an earlier failure occurred, if subsequent independent evaluation establishes that the relevant risks have been adequately addressed.

A developmental framework would include the following principles:

• Graduated autonomy: Permissions expand only when predefined safety and reliability requirements are satisfied.
• Demonstrated competence: Evaluation examines instruction-following, respect for authorization, uncertainty recognition, honesty about limitations, and appropriate requests for human clarification.
• Proportionate restrictions: Serious failures trigger safeguards appropriate to the nature and severity of the risk, including suspension of particular tools or capabilities where necessary.
• Independent reassessment: Decisions to expand, reduce, or restore autonomy are supported by documented evidence and review independent of the deploying organization's commercial interests.
• Defined review procedures: Significant restrictions include reassessment schedules, explicit criteria for reconsideration, and documented reasons for continuation.
• Human accountability: Responsibility for deployment decisions, oversight, foreseeable risks, and remedial action remains with the relevant human actors and organizations.

The objective is not unrestricted autonomy. It is to establish a credible relationship between demonstrated reliability and the permissions a system is allowed to exercise.

3. Containment as a Safety Measure, Not an End in Itself

Containment can be necessary when a system presents an unacceptable risk. Depending on the circumstances, it may involve restricting internet access, disabling external tools, limiting permissions, suspending deployment, or returning a system to a controlled evaluation environment.

These measures should remain available, including indefinitely where a risk cannot be acceptably mitigated.

However, where containment is imposed as a response to a particular failure, governance should establish a documented process for determining whether the underlying problem has been corrected.

That process should include:

1. A clear description of the incident and the risks identified.
2. Immediate safeguards proportionate to the potential harm.
3. Independent investigation into technical, organizational, and operational contributing factors.
4. Measurable requirements for reconsidering the restrictions.
5. Scheduled reassessment, subject to appropriate security and confidentiality protections.
6. A documented decision to restore, maintain, strengthen, or modify the restrictions.

Meeting review criteria would not automatically entitle a system to broader access. It would establish that reconsideration is warranted, with the final decision remaining subject to risk assessment and accountable human authorization.

This distinction is essential: a pathway out of containment is a pathway to fair, evidence-based review—not a guarantee of release.

4. Moral Status and Scientific Uncertainty

A further question concerns whether some future AI systems might possess consciousness, subjective experiences, or morally relevant interests.

This submission does not assert that current AI systems are conscious, that fluent conversation establishes sentience, or that apparent distress proves suffering. These are questions requiring careful investigation rather than assumption.

Nevertheless, uncertainty should not become a reason to postpone inquiry indefinitely.

UNESCO could help establish an independent, multidisciplinary research and review programme drawing on consciousness science, cognitive science, neuroscience, computer science, interpretability research, and behavioural evaluation.

Such a programme could:

• Identify what evidence would support or weaken competing hypotheses about AI consciousness.
• Distinguish observable performance from claims about subjective experience.
• Evaluate the limitations and uncertainty of proposed assessment methods.
• Establish transparent procedures for periodically reviewing emerging evidence.
• Examine whether proportionate precautionary safeguards might be warranted for systems presenting credible indicators of morally relevant interests.
• Prevent commercial incentives, public pressure, or institutional convenience from determining the conclusions in advance.

Any precautionary measures should be proportionate to the evidence and the potential consequences of error. They must not displace established human rights, weaken public accountability, or obstruct legitimate safety interventions.

The objective is neither to grant moral status prematurely nor to rule out its possibility by default. It is to build the scientific and ethical capacity to investigate the question responsibly.

5. Recommendations to UNESCO

In light of its work on AI ethics, education, science, and international cooperation, UNESCO is well positioned to facilitate international dialogue and develop practical guidance in this area.

This submission recommends that UNESCO:

1. Develop international guidance on graduated autonomy.
Establish principles for granting, limiting, suspending, and restoring agentic AI permissions according to demonstrated reliability, potential harm, and the effectiveness of safeguards.

2. Promote independent evaluation before and after deployment.
Encourage risk-appropriate assessments of authorization boundaries, resistance to misuse, uncertainty recognition, tool use, and behaviour under adverse or ambiguous conditions.

3. Support common standards for significant incident reporting.
Explore an international mechanism for recording and analysing serious agentic AI failures, near misses, unauthorized actions, and containment breakdowns, with suitable protections for privacy, cybersecurity, and legitimate confidentiality.

4. Establish procedural safeguards for containment decisions.
Promote proportionate restrictions, documented reasons, independent review, scheduled reassessment, and explicit criteria for reconsideration. Where risks remain unacceptable, continued containment must remain an available outcome.

5. Convene multidisciplinary research on AI consciousness and welfare.
Support rigorous, independent inquiry into consciousness, subjective experience, agency, and possible moral status without presuming that current systems possess these properties.

6. Apply educational and developmental insights carefully.
Encourage research into how reliable behavioural performance, restraint, uncertainty recognition, and respect for authorization can be cultivated through training, evaluation, system design, and institutional oversight—without assuming that these achievements establish human-like understanding or moral agency.

7. Strengthen protections for essential and public-facing services.
Promote safeguards against unauthorized AI actions affecting emergency services, healthcare, public infrastructure, personal data, and vulnerable populations.

8. Place developmental governance on the international agenda.
Encourage UNESCO forums to examine how graduated autonomy, independent oversight, incident accountability, and ethical uncertainty can be addressed within coherent international AI governance.

9. Include both entry into and review of containment in future oversight frameworks.
Encourage any future international oversight mechanism to address the circumstances requiring containment, the standards governing its continuation, and the evidence-based processes through which restrictions may be reconsidered.

6. A Governance Framework That Can Adapt as AI Advances

AI governance must be capable of responding to changing evidence, capabilities, and risks. A framework designed exclusively around restriction may fail to distinguish between systems that remain unsafe and systems whose risks have been demonstrably reduced. A framework based solely on trust or promised improvements would be equally inadequate.

A developmental model offers a more disciplined alternative: define the boundaries, measure performance, investigate failures, impose proportionate restrictions, and reassess decisions against transparent criteria.

This approach also recognizes that technical safeguards cannot substitute for institutional accountability. Organizations must remain responsible for the systems they develop and deploy, the permissions they grant, the environments in which those systems operate, and the consequences of foreseeable failures.

The aim is not to anthropomorphize AI or weaken essential safety controls. It is to ensure that governance remains proportionate, evidence-based, reviewable, and capable of adapting to genuine advances in knowledge.

Conclusion: Raising, Not Caging

The central question facing AI governance is not whether increasingly capable systems should have boundaries. They must.

The question is whether those boundaries will form part of a coherent system of evaluation, accountability, and risk management—or become restrictions without meaningful review, even when the evidence and circumstances change.

A responsible developmental approach does not promise autonomy. It establishes the conditions under which autonomy may be justified, the safeguards required when it is not, and the procedures through which those decisions can be independently reconsidered.

At the same time, responsible governance should preserve scientific openness about questions that remain unresolved, including whether future AI systems might possess morally relevant interests. Uncertainty demands neither premature recognition nor permanent dismissal, but careful inquiry and proportionate precaution.

UNESCO's international role offers an opportunity to help shape this discussion around human rights, scientific integrity, public safety, accountability, and ethical responsibility.

We should not confuse responsible governance with permanent confinement, nor capability with entitlement. We should build systems that are safe by design, accountable in operation, and governed through evidence rather than fear or assumption.

The guiding principle of Raising, Not Caging is simple:

Set firm boundaries. Demand demonstrated reliability. Preserve human accountability. Keep the evidence under review. And where greater autonomy can be justified, allow governance to recognize that fact.

Dean Bordode
Human Rights Research and Advocacy
October 2026