AI Governance: how to scale AI agents

AI Governance is the set of rules, responsibilities, processes and tools that enables a company to use artificial intelligence in a controlled, secure and measurable way. It is no longer just about experimental models or standalone chatbots: it concerns AI agents capable of consulting data, updating the CRM, triggering workflows, communicating with customers and supporting operational decisions.

For this reason, governance should not be seen as a brake. It is the organizational infrastructure that allows AI to move from experimentation to production, keeping innovation, business objectives and risk management aligned.

Why AI Governance matters now

Companies are entering the “agentic enterprise” phase: AI no longer just produces text or suggestions, but collaborates with people and intervenes in processes. An agent can qualify a lead, prepare a sales communication, update a deal, answer a support request or query a knowledge base. Impresoft Engage describes this evolution as the emergence of a hybrid AI Workforce, integrated into marketing, sales and customer service departments.

The question, therefore, is no longer just “which tool to adopt”, but who can authorize it, which data it can use, which actions it can perform and who is accountable for its results.

This is where AI Governance becomes a matter of corporate governance. A complete guide to AI Governance for AI agents should help the organization define roles, rules and controls before each function builds its own independent ecosystem. Impresoft Engage proposes a framework based on seven operational pillars, designed to make adoption scalable and sustainable.

Shadow AI: the risk you can’t see

One of the most underestimated risks is the proliferation of unregistered tools and agents. One team activates an assistant to speed up content production; another connects a model to the CRM; a third experiments with an agent to answer customers. Each initiative may be useful, but without a central view a form of Shadow AI or “agent sprawl” emerges.

The problem is not people’s initiative. It is the lack of company-wide visibility.

An unregistered agent may have excessive access, use unverified data or keep operating after the project it was created for has ended. In the event of an error, it can become difficult to reconstruct who authorized it, which version of the model it was using and which systems it involved.

You cannot govern what you do not know. A centralized AI agent catalog should therefore record at least purpose, business and technical owner, model used, risk class, accessible data, authorizations and test evidence. The catalog is not a one-off inventory, but a component of the release process and of management across the entire lifecycle.

Who decides what AI can do

AI Governance cannot be delegated exclusively to IT. The choice to allow an agent to send communications, modify information in the CRM or propose commercial terms involves reputation, compliance, costs and business results.

A cross-functional committee is therefore needed, bringing together different skills: senior management, business, IT, data management, security, legal, risk management and the DPO. The composition may vary depending on the size of the company, but the principle remains the same: high-impact decisions should not depend on a single function.

The committee should answer a few concrete questions:

• Who approves a new AI agent?
• Which tests are required before going into production?
• Which risk thresholds require escalation?
• Who can suspend an agent in the event of an anomaly?
• Who is accountable for the outcomes produced?

A well-designed Steering Group does not slow down innovation. It reduces uncertain steps, because it establishes in advance who decides, with which criteria and within what timeframes.

The 7 pillars of AI Governance

1. Committee and policy: define who makes decisions on AI
2. Agent catalog: know systems, owners, access and authorizations
3. Security by design: apply least privilege, Zero Trust and sandboxing
4. Data Governance: verify data quality, origin and freshness
5. Observability and FinOps: monitor behavior, anomalies, costs and ROI
6. Calibrated autonomy: establish which actions require human oversight
7. Compliance and risk: integrate the AI Act, GDPR and accountability into the design.

Data and security by design

An AI agent is only as good as the information context it can access. If it uses incomplete, outdated or contradictory data, it can generate very convincing but operationally wrong answers.

In Customer Engagement processes the risk is evident. An agent connected to the CRM might use outdated information about a customer; a sales assistant might suggest an offer that is incompatible with contractual terms; a customer service agent might respond based on an outdated procedure.

Data Governance must therefore accompany the agent throughout its lifecycle. It is necessary to establish which sources are reliable, who ensures they are kept up to date and how errors, duplicates and data changes are handled. Continuous Data Governance for AI agents reduces context contamination and also improves the reliability of automations.

The same principle applies to access. An AI agent should not be able to consult the entire CRM or the whole knowledge base just because it is technically possible. It must see only the information necessary for the assigned task, following the principles of least privilege, environment segregation, temporary access and continuous verification.

Autonomy and human control

Not all agents should have the same level of autonomy. Governance must assess each action separately based on its impact, reversibility, the data involved and the consequences of an error.

An AI agent that retrieves an answer from an approved knowledge base can operate with relatively high autonomy. An agent that deletes data, approves a refund, modifies a contract or sends a critical communication should instead require human oversight.

The human-in-the-loop model does not mean that a person must approve every step. It means placing human intervention at the points where judgment, accountability or context are truly indispensable.

To calibrate the autonomy of AI agents it is useful to provide for progressive levels:

1. the AI agent analyzes and suggests
2. the AI agent prepares the action, which is approved by a person
3. the AI agent acts within predefined thresholds and rules
4. the AI agent operates autonomously, with monitoring and escalation by exception.

The right level of autonomy does not depend on how technologically advanced an AI agent is, but on the risk of the action entrusted to it.

Observability, costs and ROIObservability, costs and ROI

An AI agent is not deterministic like traditional software. The same input can generate different results, and an error can look plausible, making it harder to detect before it produces operational consequences.

For this reason, observability is an integral part of AI Governance. It is not enough to know that an AI agent is online: you must be able to reconstruct which data it consulted, which steps it performed, which output it generated and when it handed the activity over to a person.

Monitoring should include at least:

• quality and accuracy of outputs
• error and escalation rate
• actions blocked by guardrails
• execution times
• cost per transaction
• consumption of models and infrastructure
• impact on process metrics.

In a RevOps approach, ROI cannot be measured solely in hours saved. A sales agent should also be evaluated on conversion, pipeline velocity and CRM data quality. A customer service agent on first-contact resolution, reopenings, satisfaction and retention opportunities.

Governance thus turns control into a performance tool: it makes it possible to understand which agents generate value, which need to be corrected and which should not be scaled.

AI Act and compliance by designAI Act and compliance by design

The current regulatory landscape makes this step even more urgent. As of 2 August 2026, the AI Act has entered a new phase and, among others, the transparency obligations of Article 50 have become operational. These include requirements to inform users when they interact directly with certain AI systems and to make certain types of artificially generated or manipulated content recognizable.

When AI agents process personal data, it remains necessary to assess compliance with the GDPR: legal basis, minimization, purpose, retention, anonymization, management of rights and responsibilities of the parties involved.

Compliance by design means integrating these checks during the design of the AI agent, not adding them after go-live. The detailed assessment must of course be carried out with the competent legal and privacy functions.

Where to start, without blocking AIWhere to start, without blocking AI

An AI Governance framework does not need to be born complete in every detail. It must start from the organization’s real risks and processes.

A minimum governance can be built through five actions:

1. inventory the AI agents and AI automations already in use
2. assign each system a business owner and a technical owner
3. create a cross-functional committee with clear decision criteria
4. classify the risk and autonomy of each agent
5. define access, human controls, logs and performance metrics.

The ideal starting point is not necessarily the most innovative project, but a well-defined, measurable process with sufficiently reliable data. In Customer Engagement, it could be, for example, the classification of support requests, the preparation of sales notes or the controlled enrichment of information in the CRM.

From there, governance can be progressively extended to marketing, sales and customer service, creating shared rules across the entire customer journey.

Do you want to understand which AI agents are already active in your company and how to govern them without slowing down innovation?

Impresoft Engage helps companies build AI Governance frameworks starting from the most relevant processes, data and use cases, all the way to the creation of secure AI agents integrated into business processes.