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.
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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.
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.
Types of AI agents
The textbook taxonomy, the workflow-versus-agent split practitioners actually use, and the environments that shape each kind.
AI agent evaluation
Success rates, the public benchmarks, and building your own eval suite — how to know an agent works before trusting it.
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.
Agent memory
The context window as working memory, RAG for knowledge, external stores for long-term state — and when each earns its complexity.
Tool use & MCP
Function calling, tool descriptions the model can actually use, and the Model Context Protocol — how agents touch external systems.
Multi-agent systems
The orchestrator–worker pattern, what parallel subagents buy you, the roughly 15× token cost, and the coordination failures nobody budgets for.
Agent failure modes
Direct and indirect prompt injection, the lethal trifecta, runaway loops, and silent drift — and the guardrail that answers each.
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.
Agent frameworks
What LangGraph and the OpenAI Agents SDK actually add, what the abstraction costs you, and when a framework earns its place.