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A real map of agents in 2026

A useful agent is not a chat with a fancy name. It is a system with a goal, tools, permissions, minimal memory, verification, and a clear way to stop.

  • Distinguish skills, subagents, hooks, MCP, loops, and external automations.
  • Choose the right piece based on context, risk, and repetition.
  • Design agents that do not get stuck in loops or act without control.

Practical taxonomy

Which piece to use for each problem

Terminal
problem -> recommended piece
"repeat a recipe inside Claude/Codex" -> skill
"review code with an isolated role" -> subagent
"block dangerous commands" -> hook
"query GitHub, DB, or browser" -> MCP with minimal permissions
"orchestrate state, branches, and retries" -> LangGraph
"connect CRM, email, Sheets, or webhooks" -> n8n
"prototype researcher/writer/reviewer roles" -> CrewAI
"run something every morning" -> GitHub Actions or scheduler

Do not turn every automation into a multi-agent system. If a deterministic rule solves the problem, use code, a workflow, or a hook. Reserve agents for steps that require judgment, natural language, or contextual search.

  • CLAUDE.md: project rules always in context.
  • Skill: reusable procedure that activates when needed.
  • Subagent: specialist with separate context and tools.
  • Hook: deterministic action before or after a tool.
  • MCP: connection to external services such as GitHub, databases, or a browser.
  • Loop or scheduled task: repeated execution with an exit condition.
  • GitHub Actions: declarative automation for CI, tests, and deployments.
Terminal
necesidad: "revisar PRs cada mañana"
decision:
  conocimiento_del_proyecto: CLAUDE.md
  checklist_reutilizable: skill code-review
  verificacion_aislada: subagente reviewer
  reglas_obligatorias: hook pre/post tool
  integracion_externa: GitHub MCP o gh CLI
  programacion: GitHub Actions o tarea programada
limites:
  no_merge_automatico: true
  publicar_solo_comentarios: true
  parar_si_no_hay_tests: true
Complete Aulafy mapSee how this lesson fits without leaving your path.

Complete Aulafy map

How all courses connect

This is not a checklist. Start with the foundation, choose an outcome, and go deeper only when your project needs more control.

  1. 1Understand
  2. 2Apply or build
  3. 3Operate with confidence
01

Choose an application

Turn the foundation into a visible outcome: a website, a business improvement, media, or an interactive experience.

Continue into the technical branch when you need to maintain code, data, or infrastructure.

02

Build with code

Prepare your environment, work with coding agents, and run models while keeping control of your projects.

This branch prepares you to design and operate reliable AI systems.

03

Take systems to production

Combine retrieval, agents, evaluation, security, deployment, and model adaptation when the problem requires it.

You do not need every course: choose the component your system needs and return as it grows.

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