# Building Production-Grade AI Agents at Kentauros AI ## Overview Kentauros AI has been working on developing production-grade AI agents capable of performing complex, open-ended tasks in real-world environments. Their experience highlights the significant challenges and practical solutions in deploying LLM-based agents in production settings. ## Key Challenges ### Reasoning System Categories - Big Brain Problems - Little Brain Problems - Tool Brain Problems ### Real-World Implementation Challenges - Error Cascading - Common Sense Limitations ## Technical Solutions ### Agent Architecture Evolution - G2 Agent (Open Source Release) - G3 Agent Implementation - Tool Brain Improvements ### Memory Systems - Advanced Memory Architecture - G4 Implementation ### Skill Management - G5 Architecture Features ## Production Implementation Strategies ### Technical Integration - Multiple model integration ### Development Approach - Iterative Testing - Resource Optimization ### Best Practices - Memory Management - Error Handling - Model Integration ## Future Directions ### Planned Improvements - Enhanced reinforcement learning integration - Better generalization capabilities - More sophisticated memory systems - Improved skill-swapping mechanisms ### Scaling Considerations - Focus on generalizable algorithms - Balance between expert systems and learned behaviors - Preparation for frontier model upgrades - Middleware development for easy integration ## Technical Insights ### Development Philosophy - Scientific approach to problem-solving - Emphasis on practical solutions over theoretical perfection - Balance between expert systems and learned behaviors - Recognition of the need for continuous adaptation ### Architecture Decisions - Modular system design - Clear separation of cognitive functions - Focus on practical implementation - Emphasis on maintainable and upgradable systems