Making AI agents work for the realities of Latin America

What is it about?

Artificial intelligence is evolving beyond systems that simply answer questions. A new generation of technologies, known as agentic AI, can plan tasks, make decisions, use digital tools, and carry out complex activities with limited human intervention. However, most of these systems are designed and developed in countries with advanced digital infrastructure, predominantly English-speaking environments, and more consolidated regulatory systems. These conditions are very different from those found in many parts of Latin America and the Caribbean. This research examines four major challenges that may limit the effective use of agentic AI in the region: differences in digital infrastructure and internet connectivity, the limited representation of Spanish, Portuguese, and Indigenous languages in AI systems, fragmented regulatory frameworks, and unequal access to technology and adoption capabilities. Based on this analysis, the study proposes the LATAM Contextual Adaptation Framework (LATAM-CAF), a conceptual framework with three layers: understanding the local context, adapting how AI agents operate, and evaluating their performance according to regional conditions. The central idea is that AI systems should not simply be transferred from more technologically advanced countries. They need to be adapted to the linguistic, institutional, technological, and social realities of the places where they will be used.

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Photo by Igor Omilaev on Unsplash

Photo by Igor Omilaev on Unsplash

Why is it important?

The global development of agentic AI creates important opportunities for productivity, innovation, and public and private sector transformation. However, if these technologies are designed exclusively around the conditions of high-income countries, their adoption in Latin America may reproduce existing inequalities rather than reduce them. This research contributes to an emerging discussion by identifying four structural dimensions that influence the deployment of agentic AI in Latin America and the Caribbean. Its main contribution is the LATAM Contextual Adaptation Framework (LATAM-CAF), a conceptual proposal that connects local context assessment, adaptive system execution, and region-specific performance evaluation. The framework offers a starting point for technology developers, policymakers, researchers, and organizations interested in designing AI systems that respond to local needs. It also highlights the importance of linguistic inclusion, infrastructure resilience, appropriate regulation, and equitable access. Although the framework still requires empirical validation and practical implementation, it provides a foundation for future research and encourages a broader perspective on AI development: technological innovation should be evaluated not only by what systems can do, but also by how effectively and equitably they work in different regional contexts.

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