# Internet of Agentic AI: Incentive-Compatible Distributed Teaming and Workflow

arXiv: https://arxiv.org/abs/2602.03145 · HTML: https://mlsanity.com/paper/2602.03145/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 13 of 13)

- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023)
- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective](https://mlsanity.com/paper/2111.11850/) (2021)
- [Towards Effective GenAI Multi-Agent Collaboration: Design and Evaluation for Enterprise Applications](https://mlsanity.com/paper/2412.05449/) (2024)
- [Efficient Computation of the Shapley Value for Game-Theoretic Network Centrality](https://mlsanity.com/paper/1402.0567/) (2014)
- [Towards Transparent and Incentive-Compatible Collaboration in Decentralized LLM Multi-Agent Systems: A Blockchain-Driven Approach](https://mlsanity.com/paper/2509.16736/) (2025)
- [Beyond the Strongest LLM: Multi-Turn Multi-Agent Orchestration vs. Single LLMs on Benchmarks](https://mlsanity.com/paper/2509.23537/) (2025)
- [An Anytime Algorithm for Optimal Coalition Structure Generation](https://mlsanity.com/paper/1401.3466/) (2014)
- [From Texts to Shields: Convergence of Large Language Models and Cybersecurity](https://mlsanity.com/paper/2505.00841/) (2025)
- [Attributing Responsibility in AI-Induced Incidents: A Computational Reflective Equilibrium Framework for Accountability](https://mlsanity.com/paper/2404.16957/) (2024)
- [PACT: A Contract-Theoretic Framework for Pricing Agentic AI Services Powered by Large Language Models](https://mlsanity.com/paper/2505.21286/) (2025)

## Built on by (most cited first, 8 of 8)

- [AWCP: A Workspace Delegation Protocol for Deep-Engagement Collaboration across Remote Agents](https://mlsanity.com/paper/2602.20493/) (2026)
- [The Internet of Agentic AI: Communication, Coordination, and Collective Intelligence at Scale](https://mlsanity.com/paper/2606.12835/) (2026)
- [Agentic Peer-to-Peer Networks: From Content Distribution to Capability and Action Sharing](https://mlsanity.com/paper/2603.03753/) (2026)
- [Toward Reliable Design of LLM-Enabled Agentic Workflows: Optimizing Latency-Reliability-Cost Tradeoffs](https://mlsanity.com/paper/2605.23929/) (2026)
- [Agentomics: Economic Foundations for the Valuation, Attribution, and Pricing of AI Agents in Human-AI Workflows](https://mlsanity.com/paper/2606.14769/) (2026)
- [AI Tokenomics: The Economics of Tokens, Computation, and Pricing in Foundation Models](https://mlsanity.com/paper/2606.24616/) (2026)
- [LLM Semantic Signaling Game and Mechanism Design: Systematic Blindness, Awareness Shaping, and Mindset Dynamics](https://mlsanity.com/paper/2606.29113/) (2026)
- [Internet of Agentic Things: Networked AI Agents for Closed-Loop IoT Orchestration](https://mlsanity.com/paper/2607.12662/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have enabled a new class of agentic AI systems that reason, plan, and act by invoking external tools. However, most existing agentic architectures remain centralized and monolithic, limiting scalability, specialization, and interoperability. This paper proposes a framework for scalable agentic intelligence, termed the Internet of Agentic AI, in which autonomous, heterogeneous agents distributed across cloud and edge infrastructure dynamically form coalitions to execute task-driven workflows. We formalize a network-native model of agentic collaboration and introduce an incentive-compatible workflow-coalition feasibility framework that integrates capability coverage, network locality, and economic implementability. To enable scalable coordination, we formulate a minimum-effort coalition selection problem and propose a decentralized coalition formation algorithm. The proposed framework can operate as a coordination layer above the Model Context Protocol (MCP). A healthcare case study demonstrates how domain specialization, cloud-edge heterogeneity, and dynamic coalition formation enable scalable, resilient, and economically viable agentic workflows. This work lays the foundation for principled coordination and scalability in the emerging era of Internet of Agentic AI.