AutoGen - Multi-Agent Conversation Framework
A comprehensive reference for AutoGen: Microsoft's framework for multi-agent AI systems, conversational patterns, code execution, and …
Agents
LangChain - LLM Application Framework
A comprehensive reference for LangChain: building LLM-powered applications, chains, retrievers, agents, and integration patterns for …
Orchestrator-Worker Pattern
An orchestrator LLM decomposes complex tasks and delegates subtasks to specialized worker models or agents, coordinating results into a …
Reinforcement Learning
What reinforcement learning is, how agents learn from rewards, and where RL applies in enterprise AI systems.
Single Agent vs Multi-Agent Architectures
When to use a single AI agent versus a multi-agent system, covering complexity, reliability, cost, and practical decision criteria.
Model Context Protocol (MCP) - Universal Tool Interface for AI Agents
What the Model Context Protocol is, how it enables AI agents to use tools through a standard interface, and server/client architecture.
Pydantic AI - Type-Safe Agent Development
Using Pydantic AI to build AI agents with validated inputs and outputs, Bedrock backend support, and Python type annotations.
Agentic AI
What makes AI agentic vs assistive, autonomous task execution, tool use, planning capabilities, and current limitations.
Multi-Agent AI Systems - When One Model Is Not Enough
A practical introduction to multi-agent AI architectures: when to use them, how they work, and which frameworks are production-ready.
Multi-Agent Systems
Definition, architecture patterns, and frameworks for multi-agent AI systems - and the signals that indicate a single-agent approach is no …