📖 Part 3 of 6 | Microsoft AutoGen: Building Multi-Agent AI Systems 📚 Microsoft AutoGen Series Introduction Communication Patterns Code Generation RAG Integration Production Deployment Advanced Patterns ← Part 2Part 4 → Building on communication patterns from Part 2, we now apply them to automated code generation—one of the most powerful applications of multi-agent systems. […]
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Agent Memory and State Management: Building Persistent AI Agents
Building agents without memory is like building amnesiac assistants. After implementing persistent memory across 8+ agent systems, task completion improved by 60%. Here’s the complete guide to building agents that remember. Figure 1: Agent Memory Architecture Why Agent Memory Matters: The Cost of Amnesia Agents without memory face critical limitations: No context: Can’t remember previous […]
Read more →Mastering Agent Communication Patterns in Microsoft AutoGen: From Two-Agent Chats to Complex Orchestration
📖 Part 2 of 6 | Microsoft AutoGen: Building Multi-Agent AI Systems 📚 Microsoft AutoGen Series Introduction to Agentic Development Agent Communication Patterns Automated Code Generation RAG Integration Production Deployment Advanced Patterns ← Previous: Part 1 Next: Part 3 → Building on the core concepts from Part 1, this article explores the communication patterns that […]
Read more →Building Multi-Agent AI Systems with Microsoft AutoGen: A Comprehensive Introduction to Agentic Development
📖 Part 1 of 6 | Microsoft AutoGen: Building Multi-Agent AI Systems 📚 Microsoft AutoGen Series Introduction to Agentic Development Agent Communication Patterns Automated Code Generation RAG Integration Production Deployment Advanced Patterns Next: Part 2 → 🎯 What You’ll Learn in This Series Part 1: AutoGen fundamentals, core concepts, and your first multi-agent system Part […]
Read more →Agentic Workflow Patterns: Building Autonomous AI Systems That Plan, Act, and Learn
Introduction: Agentic workflows represent a paradigm shift from simple prompt-response patterns to autonomous, goal-directed AI systems. Unlike traditional LLM applications where the model responds once and stops, agentic systems can plan multi-step solutions, execute actions, observe results, and iterate until the goal is achieved. This guide covers the core patterns that make agentic systems work: […]
Read more →Tool Use Patterns: Building LLM Agents That Can Take Action
Introduction: Tool use transforms LLMs from text generators into capable agents that can search the web, query databases, execute code, and interact with APIs. But implementing tool use well is tricky—models hallucinate tool calls, pass invalid arguments, and struggle with multi-step tool chains. The difference between a demo and production system lies in robust tool […]
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