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.

HiveAgents

Agendar llamada

The Hive Agents Show

Notes

Cuatro pillars. Publicadas por Sebastián. Sin ghostwriters, sin fluff, sin workshops sobre workshops.

Production Agent EngineeringPróximamente
Cómo shipeamos agentes que no alucinan en la noche
Observability, eval harness, guardrails, rollbacks. Lo que aprendimos después de 150+ deploys. Sin notebook code, sin demos de Jupyter.
LATAM Fintech RealityPróximamente
Por qué ningún LLM escucha tu audio en rioplatense
Cross-border, KYC/AML, compliance velocity, cultural cadence de español rioplatense vs neutral. Qué se puede automatizar y qué no — con ejemplos reales.
Sprint Behind-the-ScenesPróximamente
Día 1 · Día 5 · Día 14 de un Sprint real
Anonimizado pero real. Qué pasa entre el kickoff y el día que el agente atiende su primer ticket de producción. Errores incluidos.
Contrarian Takes on AI AgenciesPróximamente
Por qué McKinsey te cotiza 9 meses y nosotros 14 días
No es magia. Es architecture + risk-sharing + no subcontratar a un junior. Lectura incómoda para quien esté pagando discovery hace 6 meses.

Avisame cuando publique

Monday Note — 1 essay por lunes. Sin spam. Podés unsubscribe con 1 click.

Subscribirme al Monday Note →

The Hive Agents Show

Notes

Cuatro pillars. Publicadas por Sebastián. Sin ghostwriters, sin fluff, sin workshops sobre workshops.

Production Agent EngineeringPróximamente
Cómo shipeamos agentes que no alucinan en la noche
Observability, eval harness, guardrails, rollbacks. Lo que aprendimos después de 150+ deploys. Sin notebook code, sin demos de Jupyter.
LATAM Fintech RealityPróximamente
Por qué ningún LLM escucha tu audio en rioplatense
Cross-border, KYC/AML, compliance velocity, cultural cadence de español rioplatense vs neutral. Qué se puede automatizar y qué no — con ejemplos reales.
Sprint Behind-the-ScenesPróximamente
Día 1 · Día 5 · Día 14 de un Sprint real
Anonimizado pero real. Qué pasa entre el kickoff y el día que el agente atiende su primer ticket de producción. Errores incluidos.
Contrarian Takes on AI AgenciesPróximamente
Por qué McKinsey te cotiza 9 meses y nosotros 14 días
No es magia. Es architecture + risk-sharing + no subcontratar a un junior. Lectura incómoda para quien esté pagando discovery hace 6 meses.

Avisame cuando publique

Monday Note — 1 essay por lunes. Sin spam. Podés unsubscribe con 1 click.

Subscribirme al Monday Note →

Cómo shipeamos agentes que no alucinan en la noche

Por qué ningún LLM escucha tu audio en rioplatense

Día 1 · Día 5 · Día 14 de un Sprint real

Por qué McKinsey te cotiza 9 meses y nosotros 14 días

Avisame cuando publique

Cómo shipeamos agentes que no alucinan en la noche

Por qué ningún LLM escucha tu audio en rioplatense

Día 1 · Día 5 · Día 14 de un Sprint real

Por qué McKinsey te cotiza 9 meses y nosotros 14 días

Avisame cuando publique