Artificial intelligence is beginning to move beyond chatbots and document-support tools toward a new operational layer for digital government. While many current public-sector AI tools still depend on human users to make decisions and initiate actions, agentic AI introduces systems that can pursue defined objectives across multiple steps, gather information, query systems and apply rules or context.
This shift could change how governments organize and deliver services. Instead of public officials executing every step in a process, AI agents could support or carry out operational tasks while humans focus on designing processes, supervising outcomes and intervening when exceptions arise.
The Inter-American Development Bank notes that this transition is emerging alongside existing digital transformation challenges in Latin America and the Caribbean. Many governments still face fragmented legacy systems, weak interoperability and gaps between basic digitization and deeper institutional transformation.
Agentic AI could help address some of these challenges, but only if governments prepare the right foundations. This includes stronger governance, better data systems, process redesign, interoperability standards, institutional leadership and the technical capacity to manage AI systems that participate in public-sector execution.
Examples such as Estonia’s Bürokratt initiative show how interoperable AI assistants can help citizens access services across multiple government organizations. Instead of requiring people to navigate separate agencies, such systems can identify relevant steps and services based on a citizen’s stated need.
The rise of Agentic-as-a-Service also signals a wider shift in the technology landscape. Unlike traditional software models where humans operate tools directly, AI agents may increasingly carry out tasks autonomously and continuously across multiple systems. In government, this could apply to processes such as business licence renewals, where an AI agent could retrieve registry data, review inspection records, identify inconsistencies and escalate only complex cases to human officials.
Interoperability will be central to this transition. In many countries, citizens still act as messengers between public institutions because systems do not communicate effectively. Agentic architectures could reduce this burden by enabling AI agents to coordinate across systems through shared standards and protocols.
One emerging standard is the Model Context Protocol, introduced in 2024 and adopted by several major technology providers. It offers a shared interface for AI agents to interact with tools and data sources, reducing the need for custom integrations. For governments, this could make it easier for AI systems to work across public data environments where full structural integration has not yet been achieved.
However, agentic AI also creates serious governance challenges. Existing public-sector accountability frameworks were not designed for systems that can retrieve data, apply probabilistic reasoning and take actions within defined limits. When an AI agent acts, questions of responsibility, auditability, escalation and oversight become more complex.
New governance models are emerging to address these risks. Governance-as-a-Service has been proposed as an external enforcement layer that monitors, evaluates and constrains agent actions in real time. This approach shows that AI governance must become operational, technical and testable, rather than remaining only a policy statement.
Governments also face risks related to cybersecurity, unauthorized actions, reasoning errors, vendor lock-in and limited institutional readiness. Many public administrations do not yet have the legal, technical and operational capacity needed to manage these risks at scale.
For Latin America and the Caribbean, early engagement is especially important. If governments do not help shape the standards and operating models for agentic AI, these systems may evolve without reflecting the region’s institutional realities, public-service constraints or development priorities.
Preparing for agentic AI means more than experimenting with new tools. It requires redesigning government processes for agent readiness, investing in digital infrastructure, strengthening interoperability and building governance systems that can operate at scale.
The IDB has been leading regional discussions on the governance and operational implications of agentic AI, including the 2025 Regional AI Policy Dialogue in San José, Costa Rica. A new regional dialogue planned for 2026 is expected to explore how governments can shape this emerging layer of the state in ways that strengthen public institutions and improve service delivery.
Agentic AI is not simply another phase of digital government. It could represent a deeper transformation in which AI systems help execute and coordinate public operations. The key question is whether governments will build the capacity to guide this transition before it reshapes public administration on its own.
