The LLMs GETS works with and why a multi-LLM agentic architecture

Today'slarge language models (LLMs)are the engine of the new artificial intelligence: they understand what you write or say, reason about information and respond in natural language. They are like a digital brain built on a chip, able to read, summarize, write, code and make simple decisions in seconds. At GETS, we use them every day to build agents that sell, serve customers, collect payments and run our clients' systems.

What is an LLM and why does it matter for your business?

An LLM has learned from huge amounts of text to predict and build coherent responses. What matters for a business is not the theory, but what it makes possible:

• 24/7 customer servicevia WhatsApp, web, voice or Telegram, in each customer's language.
• Understand documents: invoices, contracts, catalogs and emails, extracting the data that matters.
• Connect to your systems(ERP, CRM, invoicing, payments) to look up information and take action, not just to chat.
• Write and review code, which speeds up high-quality software development.

The LLMs GETS works with

We don't depend on a single model. We choose the right one for each task:

• GETS AI:our main brain for complex reasoning, agents and software development.
• GPT:writing and general-purpose tasks.
• Gemini:multimodal tasks and reading documents and images.
• Llama and Mistral:open models we can run in a private cloud when data must not leave the company.
• Fast, low-cost models:for classifying, summarizing and routing messages at high volume.

Multi-LLM agentic architecture: how we design it

Anagentis an LLM that doesn't just respond: it plans steps and uses tools. In amulti-LLM agentic architecture, an orchestrator agent receives each request and decides which model and which tool to use at each step.

1. Channels:the customer writes or calls via WhatsApp, the website, voice/PBX, Telegram or from the CRM/ERP.
2. Orchestrator agent (GETS AI):understands the intent and plans the steps, with context memory andguardrailsfor security.
3. Model router:sends each step to the model that best balances quality, speed, cost and privacy.
4. Tools via MCP and API:the agent looks up information and takes action in GETS ERP, DIAN e-invoicing, payments, scheduling and the knowledge base (RAG).
5. Response and control:the customer receives the answer in their language and on their channel; sensitive cases go through human oversight, and everything is measured.

Why several models instead of just one?

• Better quality:each model does what it does best; difficult reasoning goes to the most capable one and simple tasks to the fastest.
• Lower cost:you don't pay for the most expensive model to classify a greeting or summarize a message.
• Continuity:if a provider fails or changes its pricing or terms, the router switches models without interrupting the service.
• Privacy:sensitive information can be processed with open models in a private cloud.
• Constant evolution:when a better model comes out, it is added without rebuilding the whole system.

AI security and governance

A serious agentic architecture doesn't leave everything up to the model. We define what each agent can access and do, protect personal data in accordance with Colombia's Law 1581 of 2012, log every action for auditing, and keep human approval for sensitive cases. That way, AI speeds up operations without losing control.

Want to see these agents in action? Check out our artificial intelligence agents, the GETS AI section or request a custom agent for your business.

Frequently asked questions

What does LLM stand for?

Large Language Model: an AI trained on huge amounts of text that understands and generates natural language.

What is an agentic architecture?

A design in which AI doesn't just respond: it plans steps and uses tools (ERP, payments, scheduling, documents) to complete tasks from start to finish.

Why use several LLMs?

Because each model is better at something. Combining them improves quality, lowers costs and avoids depending on a single provider.

Is my data safe?

Yes. We set permissions per agent, comply with Colombia's Law 1581 of 2012 and, when needed, use open models in a private cloud so information never leaves your company.