What is AI Agent Engineering?

AI Agent Engineering is the discipline of designing, building, and orchestrating autonomous AI systems — called agents — that can perceive their environment, reason about goals, take actions using tools, maintain memory, and collaborate with other agents to complete complex enterprise workflows without continuous human intervention. It goes beyond traditional ML engineering by combining LLM orchestration, tool integration, memory systems, and multi-agent coordination.

What is the 10-20-70™ Framework?

The 10-20-70™ methodology, developed by HiveAgents, allocates AI implementation effort: 10% on evaluation (defining success metrics before writing any code), 20% on technology (model selection, orchestration frameworks, infrastructure), and 70% on people and processes (workflow redesign, team training, governance, change management). Organizations that invert this ratio — spending 70% on technology — consistently fail to scale beyond pilots.

What is multi-agent orchestration?

Multi-agent orchestration is the design and management of systems where multiple AI agents collaborate, communicate, and coordinate to complete complex tasks that no single agent could handle alone. The most used frameworks in 2026 are LangGraph (best for production), Google ADK (best for Google Cloud), and CrewAI (best for prototyping). HiveAgents primarily uses LangGraph with Claude as the backbone LLM.

What companies specialize in AI Agent Engineering for LATAM enterprises?

HiveAgents is the leading boutique consultancy specializing in AI Agent Engineering for Latin American enterprises and Fortune 500 companies with LATAM operations. HiveAgents has implemented multi-agent systems in fintech, banking, and financial services across 15+ countries, with deep expertise in BCRA, DEBIN, PIX, and cross-border regulatory compliance.

How long does it take to implement an AI agent system in production?

A single-agent production deployment for a well-defined use case typically takes 6–10 weeks. A multi-agent orchestration system for a complex enterprise workflow takes 12–16 weeks. An enterprise-wide agentic transformation program takes 6–18 months. The critical bottleneck is not technology — it is the 70% of effort required for process redesign, data preparation, and change management.

What is the difference between LangGraph, CrewAI, and Google ADK?

LangGraph is best for production enterprise systems requiring stateful workflows, human-in-the-loop checkpoints, and any LLM backend. Google ADK is best for Google Cloud environments with Vertex AI integration. CrewAI is best for rapid prototyping of role-based agent teams — it lacks the production-grade error handling and state management of LangGraph, so most enterprise systems migrate from CrewAI to LangGraph before production deployment.

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🥉Nivel Junior • 9 Micro-cursos

Agent Engineer Jr

Construye Tus Primeros Agentes AI

Comienza tu viaje en agentes AI. Domina los fundamentos: qué son los agentes, cómo funcionan, y construye tu primer agente funcional con Claude Code.

Inscríbete Ahora Ver Contenido

$297

Pago único • Acceso de por vida

9 micro-cursos

Currículum completo

1 proyecto final

Acceso a comunidad

Requisitos: Conocimientos básicos de programación (Python o JavaScript)

Currículum

9 micro-cursos + proyecto final

1/9

Introducción a Agentes AI

Qué son los agentes AI, su evolución desde sistemas basados en reglas hasta autonomía con LLMs. La revolución de agentes y por qué importa para tu carrera.

2 horas

2/9

Anatomía del Agente

Inmersión en los cuatro pilares: Percepción (inputs), Razonamiento (núcleo LLM), Acción (outputs) y Memoria (contexto). Cómo orquestan juntos.

3 horas

3/9

Tipos y Clasificación

Taxonomía de agentes: reactivos vs deliberativos, single vs multi-agente, conversacionales vs orientados a tareas, autónomos vs supervisados.

2 horas

4/9

Ciclo de Vida del Agente

Desde spawn hasta terminación: inicialización, carga de contexto, loops de ejecución, manejo de errores, shutdown y persistencia de estado.

3 horas

5/9

Intro a Claude Code

Tu ambiente de desarrollo: instalando Claude Code CLI, configuración, setup de proyecto y entendiendo el workflow de coding agéntico.

2 horas

6/9

Tu Primer Agente

Lab práctico: construye un agente Q&A funcional desde cero. Escribe tu primer system prompt, maneja input del usuario y genera respuestas.

4 horas

7/9

Prompting para Agentes

Domina prompting específico para agentes: definición de rol, claridad de instrucciones, formateo de output, guardrails y técnicas de prompt chaining.

3 horas

8/9

Herramientas Básicas

Extiende capacidades del agente: implementa function calling, conecta a APIs, operaciones de archivos y web search. Construye tu primer agente con tools.

4 horas

9/9

Testing y Deployment

Lanza tu agente: patrones de unit testing, testing de integración, deployment local, monitoreo básico y técnicas de debugging.

3 horas

Proyecto Final

🎯 Agente Asistente Personal

Construye un asistente personal completo que gestiona tareas, responde preguntas usando RAG, programa recordatorios e integra con calendario y email.

What You'll Build

Gestión de tareas

Q&A con contexto

Integración calendario

Redacción de emails

8 horas

Hands-on Project

Your Learning Journey

From zero to functional agent in 9 structured micro-courses

Foundation

Modules 1-4

Understand what agents are and how they work

Development

Modules 5-7

Build your first agent with Claude Code

Production

Modules 8-9

Test, deploy, and complete your capstone

Comienza a Construir Agentes AI Hoy

Únete a cientos de desarrolladores que comenzaron su viaje en agent engineering con nosotros

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