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Artificial intelligence has penetrated public administration with a speed that is rarely accompanied by the technical precision needed to assess its true effects. When policymakers or researchers speak of 'AI' as a single block, they overlook fundamental differences between systems that determine how they impact essential public values such as transparency, algorithmic fairness, and accountability. To advance in this field, it is not enough to know that artificial intelligence is being used: one must distinguish between rule-based systems, black-box models, transparent architectures, general-purpose systems, and those with autonomous action capabilities, the so-called AI agents. Each of these types imposes different levels of explainability, human control, and bias risk, which requires designing governance policies tailored to each case.
This technical typology is not just an academic exercise; it has direct practical implications. For example, a black-box system used to prioritize social benefits may be efficient, but it is opaque to a citizen who wants to challenge a decision. Conversely, a hand-coded rule-based system is much more auditable, but less flexible in the face of environmental changes. Modern public administration needs both tailored applications that fit its processes and a robust technological infrastructure that ensures data integrity and operational security. This is where the capabilities of companies like Q2BSTUDIO come into play, offering custom software and cybersecurity services to protect critical systems against vulnerabilities. Furthermore, integration with aws and azure cloud service platforms allows AI solutions to scale with the flexibility that the public sector demands, without compromising traceability or auditability.
Recent research on AI in public administration suffers from a lack of specificity that limits its usefulness. Many studies refer to one type of system but draw general conclusions that only apply to another. To avoid this, public managers and developers must adopt a common language that describes in detail the architecture, level of autonomy, and transparency of each system. In this regard, a typology such as the one proposed by the authors of the original article (hand-coded, glass-box, black-box, general-purpose, and agentic systems) provides a solid framework for classifying and comparing solutions. However, in practice, the challenge is not only technical: it is also organizational. Implementing AI for businesses in the public sector requires aligning efficiency objectives with the principles of procedural justice and non-discrimination. That is why more and more organizations are turning to solutions that combine business intelligence services such as power bi to monitor system performance and detect deviations in real time.
One aspect that deserves special attention is autonomous systems or AI agents. These systems do not merely process information: they make decisions and execute actions without direct human supervision. In the context of public administration, their use must be surrounded by ethical and technical safeguards. For example, an agent that manages the scheduling of medical appointments or the granting of subsidies must be designed with human interruption mechanisms and the ability to explain its decisions. Q2BSTUDIO, as a software development and technology company, understands this complexity and offers solutions that integrate AI agents within secure and auditable platforms. Additionally, its experience in aws and azure cloud services allows these agents to be deployed in scalable, compliant environments, while its cybersecurity services ensure that no agent can be manipulated or exploited.
For professionals working on the digital transformation of the public sector, the main recommendation is clear: do not treat AI as a black box, but as an ecosystem of diverse technologies that require case-by-case analysis. Each project should begin with a technical diagnosis that answers questions such as: What type of system are we implementing? What is its level of transparency? What data does it use and how is it collected? Who has final responsibility for decisions? Answering these questions does not require specialized data science knowledge, but rather a practical guide and a proven methodology. Companies that offer tailored applications and custom software, such as Q2BSTUDIO, can support this process by providing both technical insight and knowledge of ethical and legal implications. Ultimately, public administration does not only need technology: it needs the trust of citizens, and that trust is built with precision, transparency, and accountability.
