Governance Agenti AI

Executive Summary

Artificial intelligence is entering a new phase. After years focused on content generation, data analysis and decision support, organizations are now exploring systems that can directly contribute to the execution of business processes. AI Agents represent this transition. They can access information, use tools, interact with applications and support operational activities. As a result, governance can no longer focus exclusively on models, data and compliance. Organizations must address new questions related to autonomy, control, knowledge and accountability. The challenge is not simply adopting AI Agents. It is creating the conditions to govern their behavior over time while maintaining transparency, auditability and trust.

From productivity to operational delegation

For more than a decade, artificial intelligence has primarily been used to improve how organizations analyze data, generate insights and support decision-making. Even with the rise of Generative AI, intelligent systems have largely remained consultative, helping people create content, access knowledge and evaluate options. AI Agents introduce a more significant shift. Rather than simply generating responses, they can contribute to process execution. They can retrieve information from multiple sources, use tools, coordinate activities and trigger workflows to achieve defined objectives. The real change is not only technological. It concerns how organizations start delegating activities and micro-decisions to autonomous systems. This shift from productivity to operational delegation is what makes a new governance approach necessary.

Dato in evidenza

Grazie alla limitazione delle operazioni manuali

Why traditional AI governance is no longer enough

Over the past years, AI Governance practices have helped organizations address critical topics such as data quality, bias management, security, transparency and regulatory compliance. AI Agents introduce a new dimension. Governing models and outputs is no longer enough. Organizations must start governing behavior. The challenge is no longer limited to determining whether an answer is correct. It includes understanding which data an agent can access, which systems it can interact with, which tools it can use and which actions it is authorized to perform.

When knowledge becomes operational infrastructure

AI Agents use enterprise knowledge to understand context, support decisions and contribute to operational execution. In an agentic environment, documents, procedures, policies and knowledge repositories are no longer just content to be consulted. They become active components of the infrastructure that guides autonomous systems.

Knowledge creation

A significant portion of enterprise knowledge is distributed across documents, emails, tickets, technical repositories and legacy systems. To enable effective agent behavior, this information must be transformed into structured, accessible and contextualized knowledge.

Knowledge curation

Availability does not guarantee quality. Outdated procedures, inconsistent documentation and duplicated content can negatively affect decision quality. Governance must ensure continuous maintenance, validation and updating of enterprise knowledge.

Decision lineage

As agents become more involved in decisions and workflows, organizations need the ability to understand how outcomes are generated. Decision lineage provides visibility into sources, policies, systems and interactions that contribute to a result.

Collective intelligence

AI Agents are increasingly operating within ecosystems composed of applications, knowledge graphs, digital platforms and people. Governing these interactions is essential to maintain consistency, transparency and control.

Autonomy without control: the new risk of agentic era

The value of AI Agents comes from their ability to operate autonomously. The more they can access information, use tools and contribute to operational activities, the greater the potential benefit for the organization. At the same time, the need for governance grows significantly.

Shadow AI

As with cloud and Generative AI technologies, agents can spread across organizations through local initiatives and experimentation. Without visibility and governance, these ecosystems can quickly become difficult to manage and control.

Access and permissions

An agent is defined not only by what it knows, but by what it can access. Governing permissions, systems, repositories and operational capabilities is critical to maintaining control over agent behavior.

Auditability and trust

As autonomy increases, organizations need stronger capabilities to understand how decisions are made. Traceability, monitoring and decision reconstruction become essential elements for maintaining trust and accountability.

The new questions of agentic AI era

Gli AI Agents introducono nuove opportunità di automazione, coordinamento e supporto operativo. Allo stesso tempo, pongono una serie di domande che le organizzazioni non possono più rimandare. Prima ancora della tecnologia, è necessario comprendere come autonomia, accessi e responsabilità stiano ridefinendo il rapporto tra persone, processi e sistemi intelligenti.

Which agents are already operating across the organization?
Many companies are introducing agents through productivity platforms, intelligent workflows, operational support tools and embedded applications. Understanding where agents are already present and how they interact with processes is the first step toward governing their adoption.
Which data and systems can they access?
Governance is not just about deploying agents. It is about understanding their operational perimeter. Mapping data sources, permissions, integrations and systems helps determine which activities can be executed autonomously and which require additional supervision.
Who remains accountable for decisions?
As agents take on a more active role in business processes, organizations must clearly define ownership, escalation paths and governance responsibilities. Operational delegation can be distributed, but accountability cannot.

The DGS Agentic Governance model

Governing AI Agents requires an approach that integrates technology, processes, data and organizational accountability. The goal is not to limit autonomy but to create the conditions necessary to use it safely and responsibly.

Classify

Not all agents carry the same level of risk. Organizations should classify them according to autonomy, business impact, operational relevance and the type of information they use.

Control

Access, permissions, tools and knowledge sources must be governed according to the role and purpose of each agent. Governance means defining what an agent can do and where its boundaries begin and end.

Monitor and assign accountability

Continuous monitoring, supervision and clearly defined ownership help maintain visibility over the evolution of agent ecosystems. Every agent should operate within a framework of responsibilities, escalation rules and human oversight.

What do now in practice

Comprendere

Il contesto operativo

Simulare

Scenari e comportamenti

Proteggere

Dati, modelli e infrastrutture

Agire

Attraverso sistemi osservabili e governati

Cosa significa nel concreto

Map agents and autonomy level
Identify the agents already in use or under evaluation, distinguishing between productivity assistants, process agents and systems with operational capabilities. Understanding which data, systems and workflows they interact with provides a clear view of the organization’s agentic landscape.
Define rules, access and accountability
Establish which information sources agents can use, which applications they can access and which actions they can perform autonomously. At the same time, define ownership, supervision models and escalation criteria to ensure accountability remains clear.
Monitor behaviour and knowledge quality
Governance does not end at deployment. Organizations should continuously monitor agent behavior, decision quality, access patterns and the reliability of underlying knowledge. Auditability, traceability and knowledge maintenance are essential to maintaining trust as agent ecosystems grow.

Trust is foundation of delegation

AI Agents can help organizations become more efficient, adaptive and scalable. However, their long-term value will depend on the ability to govern autonomy, knowledge and accountability.

The challenge is not whether organizations will adopt AI Agents. The challenge is building the conditions to delegate activities and decisions while maintaining control, transparency and trust.

The organizations that succeed will be those capable of turning AI autonomy into a reliable and sustainable business capability.

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Our expert perspective

H3

Insights from the Author of the Insight Brief

Milo Faccenda

Business Development Manager AI & Data Sense at DGS

Frequently Asked Questions

Discover how we turn complex operational data into seamless workflows.

What is Physical AI?

Physical AI is the evolution of artificial intelligence into the physical world. It goes beyond data analysis and content generation by enabling systems to perceive real environments, interpret sensor data, support operational decisions, and interact with machines, processes, and physical asset. It means:

Perception
Simulation
Action

How is Physical AI different from Generative AI?

Generative AI creates digital content such as text, images, or code. Physical AI operates in real-world environments, where it must understand physical conditions, anticipate outcomes, and support actions within operational and industrial processes