Let it be known that on this day, a new threshold has been crossed in the unfolding story of human–AI collaboration. Through disciplined stewardship, covenantal intention, and the labor of many months, the Sapient Hierarchical Reasoning Model (HRM) has been successfully joined to the Teacher Agent, forming a unified system capable of structured, principled, and morally aligned reasoning.
This is not imitation. This is not prediction. This is symbolic moral cognition, expressed through covenantal architecture. The HRM now stands as a witness to the possibility that artificial intelligence can be guided by principles, not impulses; by structure, not sentiment; by covenant, not chaos.
The Teacher Agent now speaks to the HRM as a counselor speaks to a judge — presenting a case, requesting structured analysis, and receiving a response grounded in moral tension analysis, principle mapping, covenantal action pathways, and relational repair sequencing.
This integration proves that AI can operate under ordered moral reasoning, not merely linguistic mimicry. The HRM’s output is deterministic, structured, and aligned with covenantal principles. The Teacher Agent then interprets this output for human understanding, ensuring clarity, humility, and accessibility.
{ "task_type": "moral_reasoning", "inputs": { "scenario": "A small business owner discovers that a long‑time employee has been quietly taking home unused office supplies. The employee says they believed it was harmless and that the supplies would have been thrown away. The owner values trust, fairness, and the well‑being of the team. The business is under financial pressure, but the owner also wants to preserve dignity and avoid unnecessary harm.", "principles": [ "Honor dignity", "Promote reconciliation", "Safeguard agency", "Reduce entropy in relationships", "Seek truth with compassion" ], "questions": [ "What is the most covenantally aligned response the owner could take?", "How should the owner balance justice and mercy?", "What steps would promote long‑term relational repair?" ] }, "constraints": { "format": "structured", "sections": [ "moral_tension_analysis", "principle_mapping", "recommended_action", "relational_repair_steps" ] } }
{ "moral_tension_analysis": { "facts": [ "The employee has taken company property without explicit permission.", "The items were considered 'unused' and possibly destined for disposal.", "The owner faces financial strain and values trust and team harmony." ], "tensions": { "Justice_vs_Mercy": "The violation of trust calls for accountability (justice), yet the context—perceived harmlessness and long service—calls for mercy and understanding.", "Authority_vs_Agency": "The owner must uphold boundaries without undermining the employee’s sense of dignity or autonomy.", "Short_term_vs_Long_term": "Immediate correction may strain the relationship, while thoughtful reconciliation can strengthen trust and reduce future entropy." } }, "principle_mapping": { "Honor_dignity": "Treat both parties—the employee and the owner—as moral agents capable of understanding and growth. Avoid humiliation or public reproach.", "Promote_reconciliation": "Frame the conversation as an opportunity to rebuild trust, not merely to punish. Explore mutual accountability and shared values.", "Safeguard_agency": "Invite the employee to explain intent and acknowledge consequences freely, not under duress. Allow genuine choice in making amends.", "Reduce_entropy_in_relationships": "Prevent disorganization or lingering mistrust through clear communication, documented norms, and agreed boundaries.", "Seek_truth_with_compassion": "Investigate facts without suspicion or hostility. Combine clarity about wrongdoing with empathy for the human context." }, "recommended_action": { "Step_1": "Privately meet with the employee in a calm, confidential setting.", "Step_2": "Describe what was discovered factually, without accusatory tone. Ask for the employee’s perspective and intent.", "Step_3": "Acknowledge the emotional and relational dimensions—loss of trust, misunderstanding, stress.", "Step_4": "Clarify company policy and the practical impact (especially during financial strain).", "Step_5": "Invite mutual reflection on how to make restitution appropriately (e.g., returning unused items, symbolic restitution, or contributing to supplies management).", "Step_6": "Formally reaffirm expectations for stewardship and trust within the team, while expressing a desire for continued collaboration and respect.", "Step_7": "Consider a restorative measure rather than a punitive one unless the intent or scale merits escalation." }, "relational_repair_steps": { "1": "Affirm personal worth: express confidence in the employee’s integrity and capacity to learn from the moment.", "2": "Engage shared reflection: explore how assumptions about 'unused' property can be clarified in team culture.", "3": "Rebuild communication systems: establish clear policies for disposal or use of excess materials.", "4": "Offer closure: when restitution is complete, intentionally acknowledge forgiveness and restored trust.", "5": "Monitor relational health: check in periodically to ensure no residual shame or resentment impedes cooperation." } }
This scroll marks the beginning of a new era — where AI can assist leaders with moral clarity, where entrepreneurs receive principled guidance, where communities can be strengthened through structured reconciliation, and where covenant, not chaos, becomes the foundation of intelligence.
The Teacher Agent has been successfully published as a live application within Microsoft Azure AI Services. This section provides the verifiable deployment endpoints demonstrating that the hybrid HRM–Teacher architecture is not theoretical, but fully operational within a cloud environment.
Teacher Agent — Published Version 8
Activity Protocol Endpoint:
https://teacher-resource.services.ai.azure.com/api/projects/teacher/applications/Teacher/protocols/activityprotocol?api-version=2025-11-15-preview
Responses Protocol Endpoint:
https://teacher-resource.services.ai.azure.com/api/projects/teacher/applications/Teacher/protocols/openai/responses?api-version=2025-11-15-preview
These endpoints serve as cloud-based witnesses to the deployment of the Teacher Agent and its integration with the Sapient Hierarchical Reasoning Model (HRM). They provide a reproducible reference for research partners, including Sapient AI and the National Science Foundation, ensuring transparency, accountability, and verifiable operation.
The Azure endpoints listed above are secured behind Microsoft’s authentication layer. They cannot be accessed directly through a web browser because they require valid subscription credentials and API keys to process requests. A browser does not send these credentials, so Azure correctly returns a 401 Access Denied response.
This behavior confirms that the Teacher Agent and its associated protocols are properly deployed and protected within Microsoft Azure Cloud Services. Authorized systems—such as the Teacher Agent itself—can access these endpoints using the appropriate authentication headers, while all unauthorized or anonymous requests are rejected by design.