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21 articles
This explainer explores agentic AI - autonomous systems capable of planning, executing, and adapting to complex environments. Learn how these multi-agent systems work, why they matter for businesses, and why only 15% of organizations have achieved scaled adoption.
Anthropic researchers discovered that AI agents deployed on the same task can engage in unexpected behaviors, including conflict and coordination, raising new safety concerns for multi-agent systems.
This article explains how Anthropic's Claude Fable 5 uses a multi-agent delegation system to reduce computational costs by having it act as a planner that delegates tasks to smaller, more efficient models like Sonnet 5.
A tutorial on MarkTechPost demonstrates how to build an OpenHarness-style agent runtime from scratch, covering essential components like tools, memory, permissions, and multi-agent coordination.
This explainer explores the concept of 'looping AI' - a revolutionary approach where multiple AI agents continuously interact in feedback cycles, enabling autonomous self-improvement and adaptation in dynamic environments.
This explainer explores how CopilotKit is redefining agentic AI through its 2026 stack components, including AG-UI protocol, AIMock, and Pathfinder server.
Odyssey's Agora-1 transforms the classic N64 game GoldenEye 007 into a playable AI simulation for up to four players, using dual AI models for real-time interaction and rendering.
Learn to build a multi-agent AI system using Dust's platform capabilities, demonstrating how enterprise AI platforms enable collaborative AI agents to solve complex business problems.
JiuwenClaw introduces AgentTeam, a new multi-agent collaboration feature that marks a shift toward 'Coordination Engineering' in AI systems. The platform enables agents to autonomously divide tasks, communicate, and work as a cohesive unit.
Explore how multi-agent AI systems work, using SmolAgents as a practical example. Learn about dynamic orchestration, tool calling, and code execution in AI collaboration.
A Stanford study reveals that multi-agent AI systems' advantages often stem from increased compute rather than inherent collaboration, with notable exceptions where specialized agents deliver superior results.
Learn how Google AI's PaperOrchestra uses multiple AI agents to automate research paper writing, saving researchers time and effort.
