AI Agents
AI Agent Architectures: A Complete Technical Guide
From single-agent loops to multi-agent orchestration, a comprehensive overview of every major AI agent architecture pattern driving autonomous systems today.
Multi-Agent Systems
LLM Optimization
Persistent Q4 KV Cache Enables Multi-Agent LLM on Edge
New research introduces quantized KV cache persistence for running multi-agent LLM systems on resource-constrained edge hardware, enabling local AI agents without cloud dependency.
Multi-Agent Systems
Supervisor Agents: Orchestrating Multi-Agent AI 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
Mirror: Multi-Agent AI System Automates Ethics Review
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
PBSAI: Multi-Agent Architecture for Enterprise 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 A2A Protocol Enables AI Agent Collaboration at Scale
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
AgentArk: Distilling Multi-Agent Systems Into Single LLMs
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
New Benchmark Framework Evaluates Multi-Agent LLM Systems
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
How Persuasion Spreads Through Networks of AI Agents
New research examines how persuasive content propagates through multi-agent LLM systems, revealing critical insights for AI safety and synthetic influence detection.
AI Agents
AI Agent Architecture Guide: Shallow, ReAct, or Deep?
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
Insight Agents: Multi-Agent LLM System Automates Data Analysis
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: Building Scalable Multi-Agent LLM 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.