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AI Agents 101

A practical guide series on AI agents - what actually makes something an agent instead of a chatbot, how the plan-act-observe loop works, and what it takes to trust one in production. Framework-agnostic: the same concepts apply whether you build in LangGraph, n8n, or straight API calls.

Get Started → Core Concepts → Go Deeper · ~59 minutes total
plan act observe loop if not done Goal Plan Act tool call Observe Done

The agent loop in one picture: plan a step, act on it, observe the result, and either loop again or stop.

GET STARTED

Start Here

Heard "AI agent" everywhere and not sure what actually separates one from a chatbot with extra steps? These two guides get you oriented.

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What Is an AI Agent?

The plain-English version: what separates an agent from a chatbot or a script, and why the difference actually matters.

Get Started 6 min read
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The Agent Loop: Plan, Act, Observe

The cycle every agent runs on, and why it's the same shape whether the framework calls it ReAct, a graph, or something else.

Get Started 6 min read
CORE CONCEPTS

What Actually Makes an Agent Useful

Two capabilities turn a loop into something worth trusting with real work - the ability to act on the world, and the ability to remember.

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Giving Agents Tools

How tool calling actually works under the hood, and the design choices that determine whether an agent uses a tool correctly.

Core Concepts 6 min read
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Memory and State in Agents

Short-term working memory versus long-term memory across sessions, and why conflating the two causes most of the weird behavior.

Core Concepts 7 min read

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GO DEEPER

Know If It's Actually Working

The guide that separates "I built an agent demo" from "I trust this running unattended."

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Evaluating and Deploying Agents

Task success rate, guardrails, and human-in-the-loop checkpoints - what it actually takes to trust an agent in production.

Go Deeper 7 min read
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Multi-Agent Systems: Orchestration, Handoffs, and Supervisors

When one agent isn't enough - the supervisor pattern, how handoffs actually work, and what breaks down as you add more agents.

Go Deeper 7 min read
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Agent Design Patterns: ReAct, Plan-and-Execute, and Reflection

Three different shapes an agent's reasoning can take, and a real comparison of when each one actually fits the task.

Go Deeper 7 min read
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Common Agent Failure Modes and How to Debug Them

Hallucinated tool calls, infinite loops, tool misuse, and context poisoning - the recurring ways agents break.

Go Deeper 7 min read
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Cost and Latency: Making Agents Fast and Affordable

Token burn from long loops, model routing, and caching repeated tool results - the constraint that shows up right after reliability.

Go Deeper 7 min read