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A new tutorial demonstrates how to build a hierarchical planner AI agent using open-source LLMs with a multi-agent architecture for complex task solving.
Learn how to create and test AI agents that interact with each other, demonstrating how uncontrolled interactions can lead to system failures similar to those observed in OpenClaw AI research.
Learn how automated failure attribution works in multi-agent systems, helping identify root causes of system failures in complex digital environments.
Researchers from PSU and Duke University develop a framework to automatically identify which agent in an LLM multi-agent system causes task failures and when the failure occurs.
Composio has open-sourced its Agent Orchestrator to help AI developers build scalable multi-agent workflows beyond traditional ReAct loops, addressing key limitations of current AI agent architectures.
