Production AI Agents: Lessons from Running 10 Agents at Scale
Deployed 10 AI agents serving 100K users/day - monitoring, error handling, cost optimization, and scaling strategies that actually work
10 posts
Deployed 10 AI agents serving 100K users/day - monitoring, error handling, cost optimization, and scaling strategies that actually work
Created 20+ tools for AI agents - web search, code execution, database access, and more. Increased agent capabilities by 10x
Implementing persistent memory for AI agents - short-term, long-term, and episodic memory. Improved task completion from 70% to 95%
Implemented multi-agent system with 5 specialized agents - 3x faster than single agent, 40% better quality, handling complex workflows autonomously
Complete guide to building AI agents - architecture, tools, memory, error handling, and deployment. Built 5 agents serving 50K users/day
Testing 3 major AI agent frameworks in production - built the same application with each, compared performance, ease of use, and capabilities
Production-ready LangChain implementation - error handling, monitoring, cost optimization, and scaling strategies from running LangChain apps serving 100K+ requests/day
Exploring the evolution from conversational AI to autonomous agents that can plan, execute tasks, and make decisions independently.
Using GPT's function calling feature to build AI agents that can use tools, query databases, and perform actions.
Complete guide to building autonomous AI agents using LangChain, including tool integration, memory management, and real-world applications.