AI Agents Learning Path
From basics to production deployment
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How AI Agents Work: Architecture Behind Autonomous AI
A deep dive into how AI agents are built. Covers the core components -- LLM brain, tools, memory, and planning -- plus the execution loop, tool calling, structured outputs, error handling, and real architecture patterns like ReAct and plan-and-execute.
What Are AI Agents? A Complete Introduction for 2026
Learn what AI agents are, how they differ from chatbots and simple LLM chains, and why they represent the next evolution in applied AI. Covers the agent loop, real-world examples, agent types, and when to use them.
Agent Frameworks Compared: LangGraph, CrewAI, OpenAI Agents SDK, and More (2026)
A comprehensive comparison of the top AI agent frameworks in 2026 including LangGraph, CrewAI, OpenAI Agents SDK, Anthropic Claude Agent SDK, AutoGen, Semantic Kernel, and Haystack. Features code examples, architecture diagrams, and practical guidance.
Memory Systems for AI Agents: Short-term, Long-term, and RAG
Comprehensive guide to memory architectures for AI agents. Covers conversation buffers, sliding windows, summary memory, vector stores, entity memory, episodic memory, and hybrid systems with implementation examples.
AI Agent Tools and Function Calling Explained
Deep dive into how AI agents use tools and function calling to interact with external systems. Covers OpenAI GPT-5, Claude 4.5, and Gemini 3 tool APIs with Python examples and architecture patterns.
Building Your First AI Agent: A Step-by-Step Tutorial
Follow this hands-on tutorial to build a working AI agent from scratch. Learn tool definition, memory implementation, and the agent loop with practical Python code examples.
Planning and Reasoning in AI Agents: ReAct, CoT, and Tree of Thoughts
Deep dive into the planning and reasoning strategies that power modern AI agents. Covers Chain of Thought, ReAct, Tree of Thoughts, Plan-and-Execute, self-reflection, and extended thinking in 2026 models.
Deploying AI Agents in Production: Cost, Safety, and Scaling
Learn how to deploy AI agents in production environments with strategies for cost control, safety guardrails, human-in-the-loop oversight, scaling, and monitoring.
Evaluating and Testing AI Agents: Metrics, Benchmarks, and Best Practices
Master the art of evaluating AI agents with comprehensive metrics, industry benchmarks like SWE-bench and GAIA, and practical testing strategies for reliable agent systems.
Multi-Agent Systems: Orchestrating Teams of AI Agents
Learn how multi-agent systems coordinate multiple AI agents to solve complex tasks. Explore communication patterns, orchestration strategies, and real-world applications of agent teams.