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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About Us

The Agent Engineering company

We are a team of engineers, operators, and founders who build AI agents that actually reach production — not Twitter demos.

Our story

Hive Agents was born from a simple frustration: we saw too many brilliant POCs that never made it to production. Teams burning six months on a system that should have shipped in two.

We started as a collective of senior engineers working with fintechs and banks across LATAM. When we realized the problem wasn't the model but the discipline of delivery, we organized around one promise: 14 days, fixed scope, production agent, or refund.

Today we're the team CTOs pick when they need an agent to actually work — and then they call us back for the next one. We operate remote-first from Madrid and Buenos Aires and have shipped production agents across FinTech, Banking, and E-commerce.

Founder

The agent engineering team that turns your workflow into a production agent — led by Sebastian Redondo.

7,000+ followers on LinkedIn

Buenos Aires, Argentina · LATAM + Global

Sebastian Redondo

Founder & CEO · Agent Engineer

I've been building and shipping software before «agentic AI» was a category. I combine 20+ years of engineering, cloud architecture and tech leadership with the latest wave of AI agents — LangGraph, AutoGen, multi-agent orchestration, fine-tuning. I started Hive Agents after watching most AI projects die in demo mode: beautiful slides, never reaching production. Our thesis is blunt: one agent running in your runtime is worth more than five POCs. Every 14-day sprint ships a production agent — with SLA, metrics and support — or we refund the full fee. Today I work with enterprise teams across LATAM, the US and Europe that need to execute, not theorize. If that sounds familiar, let's start a conversation.

20+ years building and leading technical teams
Operations across Argentina, Panama, Mexico and global remote
Deep domain in fintech, payments, core banking and COBOL modernization
Certified Cloud Architect — systems that don't fall over on traffic spikes
Agent stack: LangGraph, AutoGen, CrewAI, Claude, GPT, Gemini
11 public recommendations from ex-colleagues and former managers

Selected credentials

Stanford ML Program (2024)
LangChain for LLM Applications
AI Agentic Design Patterns · AutoGen
AWS Solutions Architect · Professional
Leading Experienced Executives

Case studies

Production agents. Verifiable metrics.

Two recent implementations. Same method: 14-day sprint, agents in production, measured results — not slides.

Guavapay

Fintech · Regulated cross-border paymentsRegulated enterprise · Global operations

Featured case

Challenge

Internal operations at a cross-border payments fintech consume engineering and compliance cycles on repetitive work: KYC/KYB onboarding, reconciliation, transaction investigations, fraud playbooks, regulator responses. The team needed to scale without doubling headcount.

Solution

We shipped a multi-agent system for the fintech's internal operations: specialized agents for automated onboarding (KYC/KYB with pre-verification), payment reconciliation against SWIFT/SEPA rails, compliance ticket triage and regulatory report generation. Every agent runs against the existing internal systems via APIs — no stack rewrite needed.

Results

Significant reduction in manual KYC/KYB cycles

Compliance ops scaling without linear headcount growth

First-response time on regulatory reports measured in minutes

Senior engineers' time reclaimed for product work

Implementation

14-day sprint per agent · HiveOS runtime · internal API integration

Tech stack

LangGraph · Claude · OpenAI · Python · SOC-aware architecture

Probella

DTC · Consumer · LATAM social commerceGrowing brand · Multi-channel

Featured case

Challenge

Probella's sales funnel lived fragmented across Instagram, WhatsApp, TikTok and email. Leads got lost in unanswered DMs, customer voice messages piled up unheard, and after-sales support depended on humans 24/7 that nobody could scale. Acquisition cost kept rising while conversion dropped.

Solution

We automated the full lifecycle end-to-end: from the social-media sales funnel, lead capture and qualification, omnichannel customer service and after-sales. Includes our first system that listens to the voice messages customers send, transcribes them, understands context, and replies with the same naturalness as a human — on the same channel the customer wrote in. Everything runs on HiveOS and integrates with WhatsApp Business, Instagram DMs, TikTok and email.

Results

24/7 coverage across every channel, no overnight human agents

DM and voice-message response time measured in seconds

Funnel conversion rate climbing month-over-month since rollout

Industry-first: a system that listens to and replies to customer voice messages

Implementation

HiveOS multi-agent · voice transcription + LLM · omnichannel integration

Tech stack

LangGraph · Whisper · Claude · Gemini · WhatsApp Business API · Meta APIs

See live product · clients.hiveagents.dev

Ver Cómo Replicar Estos Resultados

Commitment to security and compliance

We operate under standard NDAs, respect GDPR, and can adapt to enterprise regulatory frameworks (HIPAA, SOC 2). The agents we deploy run in your infrastructure with end-to-end auditing, guardrails, and logging. None of your data leaves your perimeter without explicit authorization.

Want to work with us?

Book a 15-minute call. We'll give you timeline, cost, and a concrete path.

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Our story

Hive Agents was born from a simple frustration: we saw too many brilliant POCs that never made it to production. Teams burning six months on a system that should have shipped in two.