Human-Agent Collaboration Architecture
(Redirected from Human-AI Collaboration Framework)
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A Human-Agent Collaboration Architecture is a collaborative system architecture that is an agent architecture enabling coordinated interaction between human operators and AI agents through interface layers, oversight mechanisms, and shared decision-making protocols (for human-AI teams).
- AKA: Human-in-the-Loop Agent Architecture, Human-AI Collaboration Framework, Hybrid Intelligence Architecture, Human-Agent Interaction System.
- Context:
- It can typically provide Human Interface Layers through natural language interfaces, visual dashboards, and control panels.
- It can typically implement Approval Workflow Mechanisms for critical decision points, action authorization, and override capability.
- It can typically enable Bidirectional Communication Channels via query-response protocols, notification systems, and feedback loops.
- It can typically support Role-Based Access Control through permission management, capability restriction, and audit logging.
- It can typically maintain Transparency Mechanisms via explanation generation, decision tracing, and action justification.
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- It can often facilitate Collaborative Task Planning through goal negotiation, task allocation, and responsibility assignment.
- It can often enable Adaptive Autonomy Levels via delegation control, intervention thresholds, and escalation rules.
- It can often support Knowledge Transfer Processes through learning from demonstration, preference learning, and correction incorporation.
- It can often implement Trust Calibration Mechanisms via confidence indicators, uncertainty expression, and reliability metrics.
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- It can range from being a Supervisory Human-Agent Collaboration Architecture to being a Peer Human-Agent Collaboration Architecture, depending on its interaction paradigm.
- It can range from being a Synchronous Human-Agent Collaboration Architecture to being an Asynchronous Human-Agent Collaboration Architecture, depending on its temporal coupling.
- It can range from being a Single-Human Collaboration Architecture to being a Multi-Human Collaboration Architecture, depending on its user scalability.
- It can range from being a Domain-Specific Human-Agent Collaboration Architecture to being a General-Purpose Human-Agent Collaboration Architecture, depending on its application scope.
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- It can integrate with Multi-Agent Development Frameworks for agent orchestration.
- It can utilize Agent Governance Frameworks for policy enforcement.
- It can connect to Enterprise Architectures for organizational integration.
- It can leverage AI Agent Development Environments for system configuration.
- It can interface with Agent Performance Monitoring Systems for performance tracking.
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- Example(s):
- Microsoft Copilot Architecture, enabling human-AI collaboration in productivity tasks.
- Medical Diagnosis Collaboration Systems, supporting physician-AI partnership.
- Enterprise Collaboration Architectures, such as:
- Creative Collaboration Systems, such as:
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- Counter-Example(s):
- Fully Autonomous Agent System, which lacks human interaction capability.
- Manual Control System, which lacks agent autonomy.
- Static Rule-Based System, which lacks adaptive collaboration.
- See: Human-in-the-Loop System, Agent Autonomy Level, Collaborative AI, Human-Computer Interaction, Agent Governance Framework, Trust in AI System, Explainable AI.