Step-by-step tutorials on building voice and chat AI agents using OpenAI Agents SDK, Realtime API, function calling, multi-agent orchestration, and production deployment patterns.
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Learn what defines an AI agent, how it differs from a chatbot, and explore the three core components — perception, reasoning, and action — that make autonomous AI systems work.
Explore the three types of memory that AI agents use — short-term, long-term, and episodic — with practical Python implementations and guidance on when to use each type.
Learn how AI agents break down complex goals into executable steps using task decomposition, hierarchical planning, plan-and-execute architecture, and dynamic replanning strategies.
Master the design and implementation of tools for AI agents — why tools matter, how to write effective tool descriptions, execution flow, error handling, and best practices for production tool systems.
Learn how to craft system instructions that produce consistent, reliable agent behavior — covering instruction engineering, behavioral boundaries, persona design, and techniques for preventing instruction drift.
Explore how self-reflection transforms AI agents from one-shot executors into iterative improvers — covering critique loops, retry-with-feedback, score-and-improve patterns, and practical Python implementations.
A comprehensive comparison of four fundamental agent architectures — single agent, pipeline, router, and swarm — with diagrams, code examples, and guidance on when to use each pattern.
A hands-on tutorial for fine-tuning open-source LLMs using Hugging Face Transformers, PEFT, and TRL libraries, covering setup, training configuration, evaluation, and pushing to the Hugging Face Hub.
