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.

Volver al Programa

🥇Nivel Senior • 9 Micro-cursos

Agent Engineer Sr

Swarm Intelligence y Producción Enterprise

Domina swarm intelligence, arquitecturas Hive Mind y sistemas de agentes production-grade. Construye y despliega soluciones de agentes AI a escala enterprise con monitoreo, seguridad y escalabilidad real.

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$497

Pago único • Acceso de por vida

9 micro-cursos • ~18 horas

Currículum avanzado

1 proyecto enterprise

Comunidad VIP + Office hours

Requisitos: Agent Engineer Ssr o experiencia significativa en desarrollo de agentes

Currículum Avanzado

9 micro-cursos para dominar sistemas de agentes enterprise

1/8

Swarm Intelligence

Patrones de comportamiento colectivo, inteligencia emergente y estrategias de coordinación para enjambres de agentes.

~2h

2/8

Arquitecturas Hive Mind

Diseña sistemas de agentes distribuidos con consciencia compartida y capacidades de toma de decisiones colectiva.

~2.5h

3/8

Orquestación Compleja

Patrones avanzados de orquestación incluyendo jerarquías, redes mesh y cambio dinámico de topología.

~2h

4/8

Agentes en Producción

Estrategias de deployment, enfoques de escalado, diseño de infraestructura y prácticas DevOps para sistemas de agentes.

~2h

5/8

Observabilidad y Monitoreo

Logging comprehensivo, tracing distribuido, recolección de métricas y debugging para agentes en producción.

~2h

6/8

Seguridad en Agentes

Patrones de seguridad, validación de input, prevención de prompt injection, rate limiting y audit trails.

~2h

7/8

Optimización y Performance

Optimización de tokens, caching inteligente, reducción de latencia y estrategias de gestión de costos.

~1.5h

8/8

Patrones Avanzados (RAG, Memory)

Sistemas RAG enterprise, arquitecturas de memoria a largo plazo y estrategias de retrieval híbrido.

~2h

Proyecto Final

🎯 Sistema Hive Mind Enterprise

Construye un enjambre de agentes enterprise production-ready con monitoreo, seguridad, auto-escalado y compliance integrados.

What You'll Build

10+ agentes swarm coordinados

Dashboard de monitoreo en tiempo real

Framework de seguridad y compliance

Infraestructura auto-escalable

~6-8 horas para completar

Enterprise Project

Your Learning Journey

From swarm intelligence to production-grade enterprise systems

Collective Intelligence

Modules 1-3

Master swarm patterns, Hive Mind, and complex orchestration

Production Ready

Modules 4-6

Deploy, monitor, and secure enterprise agent systems

Enterprise Scale

Modules 7-9

Optimize, extend with RAG/memory, and integrate

Prerequisites

This advanced course requires completion of Agent Engineer Ssr or significant experience with multi-agent systems, Claude Agent SDK, and MCP integrations.

Conviértete en Senior Agent Engineer

Lidera los equipos que construyen los sistemas AI del mañana

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