# A Survey on Context-Aware Multi-Agent Systems: Techniques, Challenges and Future Directions

arXiv: https://arxiv.org/abs/2402.01968 · HTML: https://mlsanity.com/paper/2402.01968/

## Key facts

- Cited by 27 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 22 of its 84 references that are in the map.
- First posted on arXiv 3 Feb 2024.

## 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, 22 of 22)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023)
- [Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution](https://mlsanity.com/paper/2309.16797/) (2023)
- [Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications](https://mlsanity.com/paper/1812.11794/) (2018)
- [Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning](https://mlsanity.com/paper/1805.01956/) (2018)
- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environments](https://mlsanity.com/paper/2005.05441/) (2020)
- [Quantifying the effects of environment and population diversity in multi-agent reinforcement learning](https://mlsanity.com/paper/2102.08370/) (2021)
- [Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance](https://mlsanity.com/paper/2103.14231/) (2021)
- [Context Aware Computing for The Internet of Things: A Survey](https://mlsanity.com/paper/1305.0982/) (2013)
- [Intent-aware Multi-agent Reinforcement Learning](https://mlsanity.com/paper/1803.02018/) (2018)
- [Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges](https://mlsanity.com/paper/2305.10091/) (2023)
- [iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2306.06236/) (2023)
- [Utility-Based Context-Aware Multi-Agent Recommendation System for Energy Efficiency in Residential Buildings](https://mlsanity.com/paper/2205.02704/) (2022)
- [Intent-Aware Autonomous Driving: A Case Study on Highway Merging Scenarios](https://mlsanity.com/paper/2309.13206/) (2023)
- [MIDAS: Multi-agent Interaction-aware Decision-making with Adaptive Strategies for Urban Autonomous Navigation](https://mlsanity.com/paper/2008.07081/) (2020)

## Built on by (most cited first, 27 of 27)

- [Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2410.02506/) (2024)
- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [Agentic AI: A Comprehensive Survey of Architectures, Applications, and Future Directions](https://mlsanity.com/paper/2510.25445/) (2025)
- [Measuring Agents in Production](https://mlsanity.com/paper/2512.04123/) (2025)
- [A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives](https://mlsanity.com/paper/2503.13415/) (2025)
- [AgentGuard: Runtime Verification of AI Agents](https://mlsanity.com/paper/2509.23864/) (2025)
- [From Glue-Code to Protocols: A Critical Analysis of A2A and MCP Integration for Scalable Agent Systems](https://mlsanity.com/paper/2505.03864/) (2025)
- [Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives](https://mlsanity.com/paper/2506.09656/) (2025)
- [A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model](https://mlsanity.com/paper/2408.02920/) (2024)
- [AI, Global Governance, and Digital Sovereignty](https://mlsanity.com/paper/2410.17481/) (2024)
- [BMW Agents -- A Framework For Task Automation Through Multi-Agent Collaboration](https://mlsanity.com/paper/2406.20041/) (2024)
- [Agent-as-a-Service based on Agent Network](https://mlsanity.com/paper/2505.08446/) (2025)
- [Unlocking the Black Box of Latent Reasoning: An Interpretability-Guided Approach to Intervention](https://mlsanity.com/paper/2606.01243/) (2026)
- [Contextual Knowledge Sharing in Multi-Agent Reinforcement Learning with Decentralized Communication and Coordination](https://mlsanity.com/paper/2501.15695/) (2025)
- [An Auditable Agent Platform For Automated Molecular Optimisation](https://mlsanity.com/paper/2508.03444/) (2025)
- [Hybrid Agentic AI and Multi-Agent Systems in Smart Manufacturing](https://mlsanity.com/paper/2511.18258/) (2025)
- [Model Context Protocols in Adaptive Transport Systems: A Survey](https://mlsanity.com/paper/2508.19239/) (2025)
- [Position: Emergent Machina Sapiens Urge Rethinking Multi-Agent Paradigms](https://mlsanity.com/paper/2502.04388/) (2025)
- [OpenAg: Democratizing Agricultural Intelligence](https://mlsanity.com/paper/2506.04571/) (2025)
- [Synchronization Dynamics of Heterogeneous, Collaborative Multi-Agent AI Systems](https://mlsanity.com/paper/2508.12314/) (2025)
- [What's the next frontier for Data-centric AI? Data Savvy Agents](https://mlsanity.com/paper/2511.01015/) (2025)
- [Goal-Oriented Multi-Agent Reinforcement Learning for Decentralized Agent Teams](https://mlsanity.com/paper/2511.11992/) (2025)
- [LLM-Enabled Multi-Agent Systems: Empirical Evaluation and Insights into Emerging Design Patterns & Paradigms](https://mlsanity.com/paper/2601.03328/) (2026)
- [Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures](https://mlsanity.com/paper/2604.18133/) (2026)
- [TRACE: An Operational Reasoning Schema for Auditable Agentic Commitments](https://mlsanity.com/paper/2607.12480/) (2026)
- [Binding Biometrics with AI Agent Identifiers for Delegation of Authority](https://mlsanity.com/paper/2608.04292/) (2026)
- [Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Study](https://mlsanity.com/paper/2609.07395/) (2026)

## Abstract (arXiv)

Research interest in autonomous agents is on the rise as an emerging topic. The notable achievements of Large Language Models (LLMs) have demonstrated the considerable potential to attain human-like intelligence in autonomous agents. However, the challenge lies in enabling these agents to learn, reason, and navigate uncertainties in dynamic environments. Context awareness emerges as a pivotal element in fortifying multi-agent systems when dealing with dynamic situations. Despite existing research focusing on both context-aware systems and multi-agent systems, there is a lack of comprehensive surveys outlining techniques for integrating context-aware systems with multi-agent systems. To address this gap, this survey provides a comprehensive overview of state-of-the-art context-aware multi-agent systems. First, we outline the properties of both context-aware systems and multi-agent systems that facilitate integration between these systems. Subsequently, we propose a general process for context-aware systems, with each phase of the process encompassing diverse approaches drawn from various application domains such as collision avoidance in autonomous driving, disaster relief management, utility management, supply chain management, human-AI interaction, and others. Finally, we discuss the existing challenges of context-aware multi-agent systems and provide future research directions in this field.