In this tutorial, we build a hierarchical planner agent using an open-source instruct model. We design a structured multi-agent architecture comprising a planner agent, an executor agent, and an aggregator agent, where each component plays a specialized role in solving complex tasks. We use the planner agent to decompose high-level goals into actionable steps, the executor agent to execute those steps using reasoning or Python tool execution, and the aggregator agent to synthesize results into a coherent final response. By integrating tool usage, structured planning, and iterative execution, we create a fully autonomous agent system that demonstrates how modern AI agents reason, plan, and act in a scalable and modular manner.
AI safety and alignment research has predominantly been focused on methods for safeguarding individual AI systems, resting on the assumption of an eventual emergence of a monolithic Artificial General Intelligence (AGI). The alternative AGI emergence hypothesis, where general capability levels are first manifested through coordination in groups of sub-AGI individual agents with complementary skills and affordances, has received far less attention. Here we argue that this patchwork AGI hypothesis needs to be given serious consideration, and should inform the development of corresponding safeguards and mitigations.
Eigent is the open source cowork desktop application, empowering you to build, manage, and deploy a custom AI workforce that can turn your most complex workflows into automated tasks. Built on CAMEL-AI's acclaimed open-source project, our system introduces a Multi-Agent Workforce that boosts productivity through parallel execution, customization, and privacy protection.
The article discusses the Agent2Agent (A2A) Protocol, an attempt to provide a common language for different AI agents to collaborate without revealing their internal workings. It also compares A2A with the Model Context Protocol (MCP), highlighting their complementary roles in building complex AI systems.
