A Design Science Framework for Human-Agent Teams in Information Retrieval

Toward User-Orchestrated Human–Multi-Agent Teams for Co-Creation

Multi-Agent Systems for Information Systems Research: A Multi-Agent Systems for Information Systems Research: A Framework for Collaborative AI-Augmented Inquiry Framework for Collaborative AI-Augmented Inquiry

Semantic Uncertainty-Guided Orchestration in Hierarchical Multi-Agent Systems

A semantic-uncertainty-guided orchestration approach, HASSUM is introduced as a general framework for uncertainty-aware coordination in multi-agent systems and suggests that semantic uncertainty is a practical and general-purpose signal for improving robustness and trustworthiness in agentic AI systems.

Toward an Organizational Science of Multi-Agent LLM Systems: Decoupling Who, How, and Which Algorithm

Multi-agent frameworks built on large language models (LLMs) routinely entangle three logically distinct concerns: who is on the team (organization), how members align (coordination), and which algorithm fuses their work (collaboration protocol). IMACS (Intelligent Multi-Agent Collaboration System) separates the three into orthogonal, independently swappable layers. Classic organizational theory (Belbin roles, Mintzberg coordination, RACI accountability) becomes executable, validated configuration, and the framework places six published collaboration algorithms behind a common interface while exposing roles, coordination, and accountability as independently configurable factors. We use this separation to conduct controlled comparisons in which organizational assignments vary while the collaboration protocol is held fixed. It also turns protocol choice into a variable that can be learned: Adaptive Org Routing, a contextual-bandit meta-protocol, selects a protocol per task under an explicit quality-cost tradeoff, outperforms every fixed protocol in a controlled study, and trains online on real benchmark and LLM-judge rewards. The ablations expose a mechanism. Accountability placement changes outcomes exactly when the protocol routes the deliverable through the accountable agent, and the winning placement flips across model families, so organizational design cannot be hard-coded; it must be revalidated, or learned, for each model binding.

From Language Models to Agentic AI: A Survey of Autonomous, Action-Enabled, and Collaborative LLM Agents

A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.

Toward Collaborative AI: A Framework for the Transition from Autonomous Agents to Adaptive Human-AI Partners

Agentic AI systems that reason, plan, and act on complex goals have advanced rapidly across software engineering, scientific discovery, drug development, healthcare, finance, and social simulation. Across these domains a single failure pattern recurs: current systems can execute tasks competently but often struggle to determine when to act, when to pause, when to change strategy, and when to involve a human. Existing reviews catalog agentic architectures, taxonomies, and limitations, but none specify what capabilities these systems must acquire to support dynamic human-AI collaboration. We address that gap. We define collaborative AI as a class of systems that combine generative exploration with autonomous action and calibrate between them based on context, uncertainty, and task demands. We identify four required capabilities: metacognition, contextual mode-switching, uncertainty-aware action, and adaptive human collaboration. We relate these capabilities to established multi-agent systems foundations, including belief-desire-intention architectures, adjustable autonomy, mixed-initiative interaction, and decentralized decision-theoretic control, while specifying the distinct challenges that LLM-based agents introduce. Across the six domains reviewed here, these gaps appear repeatedly and are not solved by current architectures, which positions collaborative AI as a concrete near-term research objective.