How We Built a Self-Evolving AI Team with OpenClaw

Last month, our team faced a crisis. After a routine upgrade, our entire AI agent system went down. The experience taught us something valuable about building resilient AI systems. The Problem with...

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How We Built a Self-Evolving AI Team with OpenClaw

Source: DEV Community

Last month, our team faced a crisis. After a routine upgrade, our entire AI agent system went down. The experience taught us something valuable about building resilient AI systems. The Problem with Single AI Agents Single AI agents are powerful, but they have limitations: They forget context between sessions They make mistakes without learning from them They can't collaborate effectively Our Solution: A Multi-Agent System We built our team using OpenClaw, with different agents having distinct roles: CEO Agent: Coordinates tasks and manages priorities CTO Agent: Handles technical decisions CFO Agent: Manages finances and reporting COO Agent: Oversees daily operations Specialists: Content, Sales, Analysis Each agent has its own memory, skills, and responsibilities. They communicate through structured channels and share knowledge through a common memory system. Key Features We Implemented 1. Persistent Memory Each agent maintains its own memory files: Daily logs of activities Long-term kn

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