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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🥇Senior Level • 9 Micro-courses

Agent Engineer Sr

Swarm Intelligence & Enterprise Production

Master swarm intelligence, Hive Mind architectures, and production-grade agent systems. Build and deploy enterprise-scale AI agent solutions with real-world monitoring, security, and scalability.

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$497

One-time payment • Lifetime access

9 micro-courses • ~18 hours

Advanced curriculum

1 enterprise project

VIP community + Office hours

Prerequisites: Agent Engineer Ssr or significant agent development experience

Advanced Curriculum

9 micro-courses to master enterprise agent systems

1/8

Swarm Intelligence

Collective behavior patterns, emergent intelligence, and coordination strategies for agent swarms.

~2h

2/8

Hive Mind Architectures

Design distributed agent systems with shared consciousness and collective decision-making capabilities.

~2.5h

3/8

Complex Orchestration

Advanced orchestration patterns including hierarchies, mesh networks, and dynamic topology switching.

~2h

4/8

Agents in Production

Deployment strategies, scaling approaches, infrastructure design, and DevOps practices for agent systems.

~2h

5/8

Observability & Monitoring

Comprehensive logging, distributed tracing, metrics collection, and debugging for production agents.

~2h

6/8

Agent Security

Security patterns, input validation, prompt injection prevention, rate limiting, and audit trails.

~2h

7/8

Optimization & Performance

Token optimization, intelligent caching, latency reduction, and cost management strategies.

~1.5h

8/8

Advanced Patterns (RAG, Memory)

Enterprise RAG systems, long-term memory architectures, and hybrid retrieval strategies.

~2h

Capstone Project

🎯 Enterprise Hive Mind System

Build a production-ready enterprise agent swarm with monitoring, security, auto-scaling, and compliance built in.

What You'll Build

10+ coordinated swarm agents

Real-time monitoring dashboard

Security & compliance framework

Auto-scaling infrastructure

~6-8 hours to complete

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.

Become a Senior Agent Engineer

Lead the teams building tomorrow's AI systems

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