AI Agents

Evaluator-Optimizer Loop: Continuous AI Agent Improvement

Learn how to build AI agents that evaluate their own outputs and iteratively improve them. Create self-improving systems that consistently produce high-quality results.

Human-in-the-Loop: Balancing AI Autonomy and Human Control

Learn how to build AI agents that know when to ask for help. Implement approval workflows, escalation patterns, and collaborative human-AI systems that combine the best of both.

Memory for AI Agents: Short-term, Long-term, and RAG

Learn how to give AI agents memory—the ability to remember past interactions, learn from experience, and access knowledge beyond their training. Build agents that get smarter over time.

The Orchestrator Pattern: Coordinating Complex AI Agent Workflows

Learn how to build orchestrator agents that coordinate multiple specialized agents, manage complex workflows, and handle dynamic task routing. Master the pattern that makes multi-agent systems actually work.

The Planning Pattern: How AI Agents Break Down Complex Goals

Learn how to implement planning in AI agents—the ability to decompose complex goals into executable steps. Build agents that think before they act and adapt when things change.

ReAct Pattern: Combining Reasoning and Acting in AI Agents

Learn the ReAct pattern—where AI agents think out loud before acting. Build more reliable agents that show their reasoning, catch errors early, and solve complex problems step by step.

Build a Code Interpreter Agent with OpenAI and HopX

Create an AI agent that writes and executes Python code using GPT-4 and HopX sandboxes. Complete tutorial with tool calling, error handling, and iterative execution.

Prompt Chaining: How to Build Sequential AI Workflows

Learn prompt chaining—the technique of connecting multiple LLM calls where each output becomes the next input. Build reliable, debuggable AI pipelines with practical Python examples.

The Reflection Pattern: Building Self-Correcting AI Systems

Learn how to implement the reflection pattern—where AI reviews and improves its own output. Build more reliable agents that catch errors, refine answers, and produce higher-quality results.

Tool Use: How AI Agents Interact with the Real World

Learn how to give AI agents the ability to use tools—APIs, databases, code execution, and more. Build agents that don't just talk, but actually do things.

What Is an AI Agent? The Complete Guide for Developers

AI agents are more than chatbots. Learn what makes an AI system agentic, the key components every agent needs, and how to build production-ready autonomous AI systems.

Why AI Agents Need Isolated Code Execution

AI agents that execute code are powerful but dangerous. Learn why hardware-level isolation is essential for production AI systems and how to avoid costly security breaches.