multi-agent systems
Learn how supervisor agents coordinate specialized AI workers in multi-agent systems. This guide covers architectural patterns, LangGraph implementation, and practical orchestration strategies.
AI Ethics
New research introduces Mirror, a multi-agent framework using AI to assist in ethics review processes, potentially transforming how AI systems evaluate content for safety and compliance.
AI Security
New research proposes a multi-agent AI reference architecture for securing enterprise AI deployments, addressing governance challenges in managing AI systems at scale.
AI Agents
Google's new Agent2Agent protocol establishes a standard for AI agents to communicate and collaborate across platforms, enabling complex multi-agent workflows for enterprise applications.
LLM agents
New research introduces AgentArk, a framework that transfers multi-agent intelligence into single LLM agents, potentially revolutionizing how complex AI systems are deployed efficiently.
LLM
Researchers introduce a unified benchmark for evaluating multi-agent LLM frameworks, providing systematic analysis of how autonomous AI agents collaborate on complex tasks.
LLM Safety
New research examines how persuasive content propagates through multi-agent LLM systems, revealing critical insights for AI safety and synthetic influence detection.
AI Agents
Understanding when to use shallow tool-calling, ReAct reasoning loops, or deep multi-agent systems is crucial for building effective AI applications. Here's how to choose.
multi-agent systems
New research introduces Insight Agents, an LLM-powered multi-agent framework that automates complex data analysis workflows through specialized agent collaboration.
multi-agent systems
AgentScope provides a flexible framework for orchestrating multiple LLM agents with built-in communication protocols, fault tolerance, and scalability features for complex AI workflows.
LLM agents
New research introduces a systematic framework for identifying why LLM agents fail to invoke tools correctly, addressing a critical reliability gap in multi-agent AI systems.
AI research
New research proposes interactive multi-agent architectures for AI scientists, moving beyond single-model approaches to collaborative systems that could transform how AI tackles complex research problems.