AI agents
11 results
AI Agent Design Patterns: ReAct, Plan-and-Execute, and Reflexion Explained
The three most important agent architectures — ReAct, Plan-and-Execute, and Reflexion — each solve different problems. Learn when to use which and how they work in practice.
Build Your First AI Agent: A Beginner's Step-by-Step Guide
Build a working AI agent from scratch — one that can use tools, make decisions, and complete multi-step tasks. No prior agent experience needed.
What is Context Engineering? The Term Replacing 'Prompt Engineering' in 2025
Context engineering is the practice of designing everything that goes into an AI's context window — not just the prompt. Here's why it matters and how to get better at it.
What is an AI Agent?
Understand what separates an AI agent from a regular prompt. Learn how agents perceive, reason, act, and loop — and why this architecture unlocks a completely new class of AI applications.
Agent Components: Memory, Tools, Planning, and Perception
Break down the anatomy of an AI agent. Every agent — no matter how complex — is built from four components: memory, tools, a planning mechanism, and perception. Learn what each does and how they interact.
Function Calling: Giving LLMs Tools
Function calling is the technical mechanism that lets an LLM invoke external tools. Learn how to define tools, how models decide when to call them, and how to structure results so agents act reliably.
ReAct Prompting: Reason Before You Act
ReAct is the reasoning pattern that makes agents dramatically more reliable. By explicitly writing out thoughts before every action, the model plans better, catches errors earlier, and produces work you can follow and debug.
AI Workflows vs. AI Agents: Choosing the Right Architecture
Not every AI task needs an agent. Learn the difference between deterministic workflows and autonomous agents, when to use each, and how to avoid over-engineering with agents when a simpler pipeline would be more reliable.
Context Engineering for Agents
Context engineering is the discipline of deciding what information goes into an agent's context window, in what form, and when. It's the highest-leverage skill for building reliable agents at scale.
Multi-Agent Systems: Coordinating Multiple AI Agents
Single agents hit limits on complex tasks. Multi-agent systems split work across specialized agents, run tasks in parallel, and use orchestrators to coordinate. Learn the key patterns and when to use them.
Evaluating AI Agents: How to Know If Your Agent Works
Building an agent is only half the job. Learn how to measure agent performance, design test cases, catch failure modes before they reach production, and build evaluation systems that scale.