LLM Agents
Diagnosing Tool Failures in Multi-Agent LLM Systems
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.
Multi-Agent Systems
AI research
Multi-Agent AI Workflows Reshape Automated Scientific Discovery
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.
LLM
Adaptive Trust Metrics Enable Reliable Multi-LLM Systems
New research introduces dynamic trust scoring for multi-agent LLM architectures, enabling safer AI deployment in healthcare, finance, and legal sectors through real-time reliability assessment.
LLM Research
Agent Drift: Measuring LLM Behavior Decay in Multi-Agent Systems
New research quantifies how LLM agents degrade over extended interactions in multi-agent systems, revealing critical reliability challenges for production AI deployments.
AI Agents
Orchestral AI: New Framework Tackles Multi-Agent Coordination
New research introduces Orchestral AI, a framework for coordinating multiple AI agents in complex workflows, addressing key challenges in task distribution and agent communication.
AI Security
Open Framework Detects Attack Patterns in Multi-Agent AI Systems
New research introduces an open framework for training security models that detect temporal attack patterns in multi-agent AI workflows through trace-based analysis.
LLM Agents
LLM Agents Roleplay as VCs to Predict Startup Success
New research uses multi-agent LLM systems simulating venture capitalists to evaluate startups, achieving notable predictive accuracy through collective roleplay-based reasoning.
AI Agents
Consensus-Driven AI Agents Boost Transparency Through Deliberatio
New research proposes multi-agent deliberation framework where AI agents debate decisions before acting, generating human-readable rationales that improve transparency and reduce harmful behaviors.
AI Agents
AI Agent Communication Protocols: MCP, ACP, A2A, and ANP Explaine
A technical breakdown of four emerging protocols enabling AI agents to communicate: Model Context Protocol, Agent Communication Protocol, Agent-to-Agent, and Agent Network Protocol.
Agentic AI
Deep Dive: Architectural Patterns for Building Agentic AI Systems
New research surveys the core architectural patterns enabling autonomous AI agents, from single-agent designs to multi-agent orchestration frameworks that power complex AI workflows.
LLM
DoVer: Auto-Debugging Framework for LLM Multi-Agent Systems
New research introduces DoVer, an intervention-driven debugging approach that automatically identifies and fixes errors in complex LLM multi-agent systems through causal analysis.
LLM Agents
SelfAI: Self-Training LLM Agents Learn Without Human Labels
New research introduces SelfAI, a framework enabling LLM agents to autonomously generate training data and improve performance without human annotation. The system uses multi-agent collaboration for self-supervised learning.