AI Agents in the Enterprise: Definition, Rules, and Risks
The AI agents are entering companies with ever greater frequency, transforming processes, workflows and organizational models. AI agents They bring operational autonomy and adaptability, but require a clear regulatory and safety framework.
It's essential for organizations to understand how these solutions fit into the AI Act and data protection regulations. Solid governance allows them to leverage the potential of AI agents while reducing legal, reputational, and operational risks.
A useful reference is the "Model AI Governance Framework for Agentic AI," published on January 22, 2026, by the Infocomm Media Development Authority (IMDA) of Singapore. The document offers practical guidelines for the responsible adoption of AI agents in complex business contexts.
In this article, we reorganize key insights from the IMDA framework and other authoritative sources, adapting them to the context of businesses using automation, AI, and digital channels like WhatsApp Business to manage customers and internal processes.
AI Agents: What They Are and When Can a Solution Be Defined as Such?
The term AI agents It's often used generically to describe very different technologies. However, scholarly literature, authorities, and market participants are beginning to converge on some common distinguishing features.
According to the definition published by Google in 2026, and echoed in various specialist analyses, AI agents are software systems that leverage artificial intelligence to pursue goals and complete tasks on behalf of users. They demonstrate reasoning, planning, and memory capabilities, with a level of autonomy sufficient to make decisions, learn, and adapt over time.
These capabilities largely stem from the evolution of generative AI and multimodal foundation models, capable of processing text, voice, video, audio, code, and other types of data in parallel. AI agents They can converse, reason, learn iteratively, and support end-to-end business transactions and processes.
Autonomy, planning and ability to act
From a functional point of view, the AI agents They are distinguished by several key characteristics. The first is the ability to independently pursue complex objectives, even those not fully defined in advance, through long-term, adaptive, and contextual planning.
The second is the ability to take operational actions, not only in digital environments (information systems, CRM, ERP, databases, cloud applications), but potentially also in the physical world through integration with IoT, robotics, or industrial control systems.
Finally, agents can collaborate with other AI agents to coordinate and execute complex workflows, such as orchestrating customer support, logistics, billing, and compliance activities within the same process.
AI Agents and the AI Act: Scope and High-Risk Systems
Having clarified what is meant, in general terms, by AI agents, The first issue to address concerns the application of the European AI Act. It is necessary to understand whether and when these solutions fall within the definition of AI systems under the Regulation and, above all, in which cases they can be classified as high-risk systems.
The correct framework isn't just theoretical: it determines specific obligations for developers, vendors, and users. These include documentation requirements, risk management, data quality, transparency, human oversight, and post-deployment monitoring.
In various business usage scenarios, especially when the AI agents If they impact decisions that significantly impact individuals (e.g., credit ratings, HR management, access to public or healthcare services), they may fall into the high-risk categories outlined by the AI Act. Therefore, legal and compliance departments must be involved from the design stages.
For a general overview of the European Regulation on AI, it is useful to refer to the dedicated entry on Wikipedia and the institutional insights available on the European Union website.
AI Agents and Personal Data: Privacy Risks and Protection Measures
In many cases the AI agents They process personal data, often continuously and automatically. This makes it crucial to analyze compliance with the GDPR and national privacy regulations, integrating data protection impact assessments (DPIAs) and appropriate technical and organizational measures.
The UK Information Commissioner's Office (ICO), in its report "ICO tech futures: Agentic AI," published on January 8, 2026, highlights how many critical issues facing AI agents coincide with those already known for generative AI, but with the risk of being amplified by the agentic component. Greater autonomy means more scope for unsupervised action.
Among the risks cited by the ICO are the increase in fully automated decisions, an overly broad definition of processing purposes, and access to personal data that isn't strictly necessary. All of these factors, if left unchecked, could lead to violations of the data minimization principle and unlawful processing.
Too broad purposes and unnecessary access to data
A common mistake in designing AI agents This involves defining extremely broad processing purposes, designed to "cover" any future use of the system. This tends to grant the agent access to very large archives and databases, with the risk of including unnecessary categories of data.
To reduce this risk, it is essential to rigorously apply the principles of privacy by design and by default. This involves, among other things, limiting the datasets accessible to the agent, carefully managing logs and histories, and introducing obfuscation or pseudonymization mechanisms when possible.
In this context, the DPO can play a decisive role, supporting the company in defining the purposes, in choosing the appropriate legal bases and in continuously monitoring the AI agents In production. Further guidance on personal data and AI is available on the European Data Protection Board website: edpb.europa.eu.
AI Agent Governance: Operating Principles and Controls
The IMDA Singapore's “Model AI Governance Framework for Agentic AI” proposes a set of practical principles for governing AI agents in a structured manner. At its core are two fundamental axes: the actions the agent is authorized to perform and the information it can access.
The first dimension includes, for example, maximum financial thresholds for transactions, the need for prior human approval for sensitive operations, and automatic blocking in the presence of potentially illegal or anomalous activity. The second dimension focuses on data classes, access limits, and logging rules.
Effective governance also requires clear processes for vendor selection, software license management, periodic model reviews, and defining internal responsibilities across IT, legal, compliance, marketing, operations, and HR.
Principle of least privilege and access limits
One of the pillars indicated by the IMDA is the rigorous application of the principle of least privilege. AI agents They should have access only to the tools, systems, and datasets strictly necessary to achieve their defined goals, nothing more.
This involves detailed mapping of the agent's integrations (APIs, databases, third-party systems) and configuring granular roles and permissions. Any expansion of the access perimeter should be subject to a risk assessment and a formalized authorization process.
For agents who work on customer communication channels, such as email or messaging platforms, it's also essential to control which templates, scripts, and content they can use to avoid unapproved communications or those that don't comply with company policies.
Autonomy levels, workflows for critical tasks and shutdown mechanisms
IMDA suggests graduating the autonomy of the AI agents Based on the criticality of the tasks performed. For low-impact tasks, broader decision-making latitude can be granted; for high-risk operations, it's advisable to require human confirmation or insert the agent into structured workflows.
Another key issue is containment: precisely defining the agent's impact area, providing isolation, rapid shutdown, and containment mechanisms in the event of anomalies or malfunctions. This is particularly true when agents can perform automatic external actions (e.g., sending messages, authorizing payments, modifying critical data in company systems).
Complete traceability of the operations carried out by the AI agents It is essential to ensure accountability, internal audits, and incident response capabilities. Detailed logs, periodic reports, and monitoring tools should be an integral part of the architecture.
External suppliers, contracts and user training
When the AI agents When they are provided or managed by third parties, the contractual dimension becomes strategic. Contracts should clearly regulate the distribution of obligations, security guarantees, liability in the event of damage, data management methods, and regulatory compliance requirements.
Alongside the technical and legal aspects, the IMDA framework emphasizes the importance of transparency and internal literacy. Users must know what the agent can do, what data it uses, when human intervention is required, and what limitations cannot be exceeded.
Continuous training programs on AI, security and data protection help employees and managers use the AI agents consciously, reducing operational errors and cultural resistance.
AI Agents: Impact on Marketing and Business
The adoption of AI agents It has a direct impact on marketing, sales, and customer experience. Specifically, it allows for the transition from static automation to dynamic flows, where the agent considers the context, personalize messages, and decides the next step in the conversation with the customer.
In digital marketing, the AI agents They can orchestrate multi-channel campaigns, optimize delivery timing, dynamically segment audiences, and adapt content based on previous interactions. On channels like WhatsApp Business, this means more relevant conversations, less spam, and higher conversion rates.
From a business perspective, agents can support pre-sales, support, onboarding, debt collection, order management, and post-sales processes. Integrations with CRMs, ticketing systems, and analytics tools allow for the seamless integration of data, actions, and measurable results.
Customer experience, automation and control
The challenge is to balance the potential of the AI agents with the needs of control, compliance, and brand consistency. On the one hand, you want to automate as much as possible to reduce microtasks and increase productivity; on the other, you need to avoid inappropriate messages, pricing errors, or violations of internal policies.
A good practice is to define guidelines regarding tone of voice, permitted content, escalations to human agents, and specific quality metrics for agents interacting with customers. This way, marketing and customer service can work together to leverage AI without losing control over the experience offered.
For companies that use WhatsApp as a strategic channel, the integration between AI agents, official APIs and marketing automation platforms become a key competitive factor.
How SendApp Can Help with AI Agents
To exploit the AI agents To operate compliantly and securely on WhatsApp Business, you need a reliable platform that integrates with the official APIs. SendApp Official provides access to the official WhatsApp API, creating the ideal infrastructure for connecting AI agents to a scalable and regulated messaging channel.
Through SendApp Official, you can manage approved templates, opt-ins, transactional notifications, and conversations in real time, while maintaining control over flows, permissions, and logs. This is especially important when AI agents send messages autonomously or perform actions based on behavioral triggers.
For companies that need to coordinate teams of human operators and automated agents, SendApp Agent It allows you to distribute conversations among multiple users, set routing rules, define escalations, and monitor performance. AI agents can handle standard requests, while complex cases are transferred to operators, maintaining quality and compliance.
Those who want to push automation even further can rely on SendApp Cloud, the ideal cloud solution for integrating AI agents, advanced workflows, and external systems such as CRM and management software. In this scenario, the company can build complex conversational journeys with business logic, security rules, and integrated traceability.
By combining SendApp Official, SendApp Agent, and SendApp Cloud, organizations can design, test, and deploy customer-centric AI agents, while complying with AI, privacy, and commercial communications regulations. The next step is to define clear governance, as recommended by the IMDA framework, and launch a gradual adoption roadmap.
To get started, it is advisable to start with a limited use case (e.g. post-sale notifications or automated FAQs), measure results and risks, and then progressively extend the scope of the AI agents. The SendApp team can support this evolution with dedicated WhatsApp Business consulting, pilot testing, and AI integration setup.
Visit the site SendApp to request a demo and evaluate how to integrate AI agents into your marketing, sales, and customer service processes in a compliant, scalable, and secure way.
