Basic AI concepts

How is the use of AI evolving in companies? 

The use of AI in corporations is undergoing a major transformation: moving from the stage of isolated experimentation—such as chatbots and restricted copilots—toward autonomous operations. In this new phase, known as the "agentic era," AI Agents stop merely conversing and start executing business actions, accessing systems, calling APIs, making decisions, and triggering real transactions.

Why should companies invest in AI strategies? 

Structured investment in AI is essential because it automates complex tasks, improves the analysis of large volumes of information, and increases productivity. This application has the potential to transform entire departments, optimizing everything from customer support to decisions based on predictive scenarios. Furthermore, building a well-governed AI infrastructure has become an essential competitive differentiator and is now financially viable.

The transition to decentralized agentic systems is a basic requirement for corporate resilience, but a truly hybrid architecture is the only realistic approach. Companies need to build environments that natively support multi-LLM, multi-MCP, and multi-gateway ecosystems. 

What is an AI agent?

An AI agent acts as a "new digital employee"—that is, a software system designed to be autonomous and act with a focus on specific goals. Using LLMs as a reasoning engine, the agent is capable of understanding intent, making decisions, remembering context, and interacting with databases and external APIs to complete workflows and solve complex problems on its own.

What is an LLM?

Large Language Models (LLMs) are artificial intelligence models that act as the "brain" of non-deterministic applications. They are trained on massive datasets (text) to understand and interact using natural language, making them capable of generating responses, interpreting complex specifications, and making the logical decisions essential for guiding AI agents.

What is an AI Gateway? 

According to Gartner, an AI Gateway is a technology or platform that acts as an intermediary layer between applications and artificial intelligence services or models. It functions as a natural evolution of the API Gateway, designed specifically to provide a central point of control, enabling security, governance, and observability in AI workloads. 

The AI gateway must securely connect and govern all AI agents, APIs, and integrations across any application infrastructure. It is a solution that scales operations by transforming AI governance into a strategic accelerator.

It acts as a centralized and secure hub, governing how agents access essential resources (including LLMs, MCPs, and other agents), ensuring they have the exact tools to execute tasks safely and efficiently.

What is AI governance?

AI governance is the framework that ensures manageability, observability, security, and control over the consumption of agents and models within organizations. It acts as a strategic accelerator that transforms AI adoption—often started in a fragmented and insecure way (Shadow AI).

True AI governance ensures data sovereignty. Governing AI agents' access to sensitive business data is, fundamentally, a matter of consent and sovereignty, ensuring that all prompts, MCP calls, and agentic workflows are executed securely within the customer's own infrastructure to maintain regulatory compliance. 

What is "Shadow AI" and how does the Gateway help combat it?

"Shadow AI" occurs when different teams within a company use artificial intelligence (such as ChatGPT, Claude, or Llama) in a fragmented manner, without integration and outside the control of IT policies. The AI Gateway resolves this problem by acting as a central hub that unifies access, including the models used, A2A communications, and MCPs.

What is an MCP server?

The Model Context Protocol (MCP) server is a tool that functions as a bridge or an "API broker," designed to provide the AI with precise and secure business context. It translates the company's data, transactional systems, and APIs into tools and instructions that an autonomous agent can access and understand, strictly managing access permissions and defining exactly what the AI is allowed to see within the corporate environment.

‍