AI agents, decoded.

AiAgentNook explains AI agents in plain language — what they are, how to judge them, and how to build your own. The guides are just the start: model comparisons, costs, and more are on the way.

Explore the guides

Start with the
four foundations

Quick check · Do you need an AI agent?

Fig. 00 — automation and a bot answer the same; only the agent chooses for itself
The library

Ten guides to understand AI agents

Ten guides. Three minutes each. Half an hour from now you'll know what an AI agent actually is, what it costs, how it fails, and when you don't need one — which is more than most people selling them will tell you. Plain language, every claim sourced.

01 Basics

What is an AI agent

A definition narrow enough to be useful: a model using tools in a loop it controls itself — and why that autonomy is a cost, not a feature.

02 Basics

Types of AI agents

The textbook taxonomy, the workflow-versus-agent split practitioners actually use, and the environments that shape each kind.

03 Evaluation

AI agent evaluation

Success rates, the public benchmarks, and building your own eval suite — how to know an agent works before trusting it.

04 Building

Build flow & sequence

The order of operations for shipping an agent: task, baseline, tools, loop, evals, guardrails — and why the sequence is the safety feature.

05 Building

Agent memory

The context window as working memory, RAG for knowledge, external stores for long-term state — and when each earns its complexity.

06 Building

Tool use & MCP

Function calling, tool descriptions the model can actually use, and the Model Context Protocol — how agents touch external systems.

07 Building

Multi-agent systems

The orchestrator–worker pattern, what parallel subagents buy you, the roughly 15× token cost, and the coordination failures nobody budgets for.

08 Safety

Agent failure modes

Direct and indirect prompt injection, the lethal trifecta, runaway loops, and silent drift — and the guardrail that answers each.

09 Cost

The cost of running agents

Why token bills multiply with every loop iteration — about 4× a chat for one agent, 15× for multi-agent — and the levers that control them.

10 Basics

Agent frameworks

What LangGraph and the OpenAI Agents SDK actually add, what the abstraction costs you, and when a framework earns its place.