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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HiveCobol

Legacy COBOL Modernization with AI

Analyze, document, and modernize your legacy COBOL code with AI. Detect bugs, generate automatic documentation, understand complex dependencies, and plan your transition to modern systems—all without risking your critical operations.

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80% faster documentation

Zero business disruption

Full audit trail

Mainframe compatible

Powerful Features

Everything you need to succeed with HiveCobol

Deep Code Analysis

AI-powered analysis understands your COBOL code at a semantic level—not just syntax. Identify business logic, data flows, and hidden dependencies.

Auto-Generated Documentation

Create comprehensive documentation automatically. Get flowcharts, data dictionaries, and plain-English explanations of what each program does.

Bug Detection & Fixes

AI identifies potential bugs, security vulnerabilities, and code smells. Get suggested fixes with confidence scores and impact analysis.

Dependency Mapping

Visualize how programs, copybooks, and data files connect. Understand the impact of changes before you make them.

Refactoring Suggestions

Get AI-powered recommendations for modernizing code. Identify candidates for extraction, consolidation, or rewriting.

Migration Planning

Estimate effort and risk for migrating to modern languages. Get a prioritized roadmap based on business value and technical complexity.

Real-World Use Cases

See how companies are transforming their operations with HiveCobol

Regional Bank

A bank with 2M+ lines of undocumented COBOL got complete documentation and identified 47 critical bugs.

$3M saved in consulting fees

Insurance Company

An insurer used HiveCobol to plan their modernization, reducing their 5-year timeline to 3 years.

40% faster modernization

Government Agency

A state agency documented decades of legacy code for compliance and knowledge preservation.

100% code coverage documented

Simple, Transparent Pricing

Choose the plan that fits your needs. All plans include core features.

Assessment

One-time comprehensive analysis

$5,000

Up to 500K lines of code
Complete code inventory
Dependency mapping
Risk assessment report
Modernization roadmap
Executive summary
2-week delivery

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Ongoing analysis and documentation

$2,000/month

Unlimited lines of code
Continuous analysis
Auto-updated documentation
Bug detection & alerts
Refactoring suggestions
API access
Priority support
Quarterly reviews

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Enterprise

Full modernization partnership

Custom

Everything in Subscription
On-premise deployment
Custom AI training
Dedicated engineer
Migration execution support
Compliance documentation
SLA guarantee
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Frequently Asked Questions

What versions of COBOL do you support?

HiveCobol supports all major COBOL dialects including IBM Enterprise COBOL, Micro Focus COBOL, GnuCOBOL, and COBOL-85. We also handle JCL, CICS, and IMS.

How do you access our mainframe code?

We offer multiple options: secure file upload, SFTP integration, or on-premise deployment for maximum security. Your code never leaves your infrastructure if you prefer.

Is my code safe?

Absolutely. We're SOC 2 Type II certified. All code is encrypted in transit and at rest. For Enterprise customers, we offer fully on-premise deployment with no data leaving your environment.

How accurate is the AI analysis?

Our AI achieves 95%+ accuracy on code understanding and 90%+ on bug detection. All findings include confidence scores, and critical items are flagged for human review.

Can HiveCobol actually migrate code for us?

HiveCobol focuses on analysis, documentation, and planning. For actual migration execution, we partner with system integrators or can connect you with our Agent Systems team for custom development.

What if we have proprietary copybooks or frameworks?

HiveCobol can be trained on your specific patterns and conventions. Enterprise customers get custom AI training to understand your unique codebase.

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FAQ

Las preguntas que siempre recibimos sobre HiveCobol

Funciona con IBM Enterprise COBOL, Micro Focus, ACUCOBOL, y variantes de mainframe Unisys. Para dialectos propietarios, hay una fase de calibración de 3-5 días donde el agente se entrena sobre tus copybooks específicos. Si al día 5 el agente no mapea >80% de tus dependencias, reembolsamos el assessment.

Origin · HiveAgents Sprint #47

Por qué construimos HiveCobol

En Q3 2024, un banco de LATAM nos llamó con un problema que no habían podido resolver en 3 años. Tenían 12 millones de líneas de COBOL escritas entre 1987 y 2003. El equipo que las entendía se jubilaba. McKinsey había cotizado USD 4M y 18 meses de "discovery".

El CTO nos preguntó: "¿pueden entender el código?". Les dijimos: "no, pero podemos entrenar a un agente que sí lo entienda". En 14 días, el agente mapeó el 87% del codebase, identificó 3.400 dependencias ocultas y entregó un plan de modernización firmado. Eso fue HiveCobol v0.

Hoy corre en producción en 4 bancos. Lo empaquetamos como producto porque los mismos 3.400 dependency-patterns se repiten en el 90% de los mainframes que miramos. No había razón para rescribirlo cada vez. No hay razón para que vos tengas que contratar una consultora para que te venda 6 meses de discovery sobre tu propio código.

— Sebastian Redondo, founder · Buenos Aires