How can agent-based AI be implemented in a business ?

Key takeaways :

• AI agents create value for the company when they are applied to specific use cases such as document research, administrative tasks, data processing, and business process automation.
• The first step is to prepare your data. By identifying useful sources and removing obsolete information, the company must classify sensitive content and review access rights.
• Deployment should begin with simple, controlled integration steps. The goal is to measure the gains achieved. The production rollout of an AI agent must be based on testing. To be validated, the quality of responses, adherence to the functional scope, and compliance with access rules must align with the initial specifications.
• With Angèle, Rcarré offers a sovereign and phased approach to transition from experimentation to a controlled deployment, including monitoring of usage, access, and results achieved.

In their quest for productivity gains, companies are increasingly turning their attention to AI agents and how to integrate them into their organizations.

While expectations are high, adopting agent-based AI is not simply a matter of technical integration.

By default, an AI agent cannot independently access a company’s documents, applications, or data. On a day-to-day basis, it interacts only with resources that are connected to it, authorized, and properly configured.

The deployment of agent-based AI therefore goes beyond a purely technological issue. It also concerns work organization, document quality, compliance, access management, and user support. In other words, you must define the resources with which your agent must interact within your company, and to what extent you trust it and allow it to work with full autonomy.

Following a previous article on local and sovereign AI agents, this article addresses a new challenge: how can agent-based AI be practically deployed and integrated into the company’s work processes in a controlled manner?

Analysis and methodology with the experts at Rcarré.

The adoption of AI must be done gradually

Deploying AI agents in a business setting is not simply a matter of connecting a public LLM (large language model) to your information system (IS) tools. Their effectiveness depends on a set of interrelated criteria. Therefore, the data used, access rights, use cases, and operational rules must be defined in advance.

At Rcarré, the adoption of agent-based AI is primarily driven by use cases. Without a clearly identified need, teams may turn to unvalidated consumer-grade tools. This practice falls under the category of Shadow IT, where digital tools are used without IT team approval, or Shadow AI, when this usage involves artificial intelligence tools.

These behaviors reduce IT and security teams’ visibility into the tools used, the data shared, and the actions taken. They can also increase the risks of data leakage or non-compliant use.

This is the logic behind the development of Angèle, Rcarré’s sovereign artificial intelligence offering. It enables organizations to automate identified processes based on specific business needs. The company thus retains control over its data while managing the use of agents deployed in its digital environment and ensures a return on investment that has rarely been seen before.

The more your data is categorized and contextualized, the more relevant your agent’s actions will be

Deploying AI agents requires access to actionable data. An AI model can reason, rephrase, or generate a response, but if it is unfamiliar with the company’s contextual information, the resulting output will be of poor quality. Therefore, to provide accurate responses, the agent must rely on internal sources that are verified, accessible, and properly structured.

In most cases, business tools do not communicate with one another, and data from different departments often remains siloed. This situation limits deployment possibilities and the actions that future AI agents will be able to perform.

The company has invested in implementing the solution and pays for tokens every month, but the results are not materializing.

Mapping data flows is therefore necessary. By ensuring data reliability, that is, by identifying useful files and removing duplicates and obsolete or contradictory information, the company will significantly improve the quality delivered by the AI agent.

Your company must also classify its data. Assigning confidentiality labels allows the agent to distinguish between public, internal, confidential, and sensitive information. This classification makes it easier to include or exclude certain information in interactions with AI agents.

Finally, access rights must be reviewed. A user should only have access to the data necessary for their tasks. This principle becomes even more important when an AI agent is connected to a digital work environment.

A frequently cited anecdote illustrates the risks of poorly controlled document access. During the launch of Microsoft Copilot, some companies observed that users were querying the tool about sensitive information in order to find out their supervisor’s salary. The problem stems not only from the tool but also from how it is used.

This is one of Angèle’s core principles. An AI agent must not have indiscriminate access to the company’s entire document repository. It must rely on identified sources relevant to its mission and respect predefined rights.

This approach enables the generation of context-specific responses, grounded in the company’s internal knowledge, without unnecessarily expanding the scope of data exposure.

Choose simple use cases that apply to well-defined processes

Before selecting a solution from the market, the company must identify the use cases that truly justify the use of AI agents.

Rcarré recommends starting with pilot projects that involve limited risk. These projects should build on existing processes that are familiar to and well-managed by the teams.

A collaborative approach can facilitate this decision. For example, a poll conducted via the internal messaging tool can help identify the feature most anticipated by employees.

In most cases, the initial needs involve simple but time-consuming tasks. These may include first-level support, document research, processing administrative requests, or producing internal documents.

Conversely, it is best to avoid overly broad deployment scopes. Generic projects with low added value, or overly ambitious projects with benefits that are difficult to measure, slow down adoption.

Rcarré’s consultants favor a phased approach based on quick, measurable results. This method helps build team buy-in and allows for adjustments to the AI agents before expanding their scope.

Angèle therefore does not offer general-purpose assistants designed to meet every need. Rcarré’s AI agents are specialized. They are tailored to a specific role, task, or process.

Maintain control over your data with a managed and sovereign infrastructure

Many artificial intelligence solutions rely on cloud infrastructures located outside Europe. As a result, the data used in queries, the documents analyzed, or the business information shared with the tool may be transferred outside the company’s digital environment and into a new regulatory framework.

For organizations that handle customer, financial, contractual, and regulatory data, precautions must be taken. Where does the data go? Who can access it? Under what conditions is it processed?

With Angèle, the goal is not to systematically send your company’s data to the AI, but to bring the AI closer to your data, within a controlled environment. This allows you to retain control over your information assets and limit the exposure of your data.

To achieve this, Angèle was designed as a sovereign, local, and hybrid artificial intelligence platform. It leverages the company’s internal knowledge while limiting the exposure of sensitive information.

In particular, Rcarré offers data hosting in Europe, which is not subject to extraterritorial laws such as the U.S. CLOUD Act (Clarifying Lawful Overseas Use of Data Act).

Our clients can also rely on Tier IV-certified data centers in Luxembourg and an end-to-end managed infrastructure. This expertise enables the deployment of AI agents in secure environments, in line with best practices and compliance requirements.

Implement AI governance in your organization

Although the key advantage of AI agents is their ability to operate autonomously, it is essential to establish supervision and human validation procedures to oversee their actions.

Not all uses of AI carry the same level of risk: analyzing your financial results or generating a quote will require more attention than creating summary reports for internal use.

Appointing AI liaisons helps better manage these applications. These liaisons can monitor risks, formalize usage rules, support employees, and contribute to the validation of agents before their deployment.

This role can be filled by a CISO (Chief Information Security Officer), also known as an information systems security manager. As part of their work, Rcarré’s part-time CISOs support companies on operational matters, such as employee training, creating dashboards, monitoring automated processes, and assisting with technology choices related to AI agents.

Angèle integrates governance from the very design phase of AI agents, notably through differentiated access levels: users have access only to authorized agents, administrators have a more comprehensive view, and technical teams can adapt the contexts and relevance of AI agents.

Test, monitor, and measure the use of your AI agents before rolling them out

Before being deployed in production, an AI agent must be tested under conditions that closely resemble its future use cases.

A preliminary phase prior to production deployment should verify the quality of responses, compliance with access rights, the agent’s ability to reject requests outside its scope, and the level of human oversight required for its supervision.

This phase must involve technical teams, business teams, and future users to validate that the agent can be integrated into a real-world process without creating operational risks, information leaks, or an excessive verification burden for the teams.

Once deployed, its use must be monitored over time. The traceability of actions performed, datasets used, and users involved allows for the adjustment of access rights, the measurement of gains achieved, and the generation of a return on investment.

With Rcarré, stay in control of AI in your business

With 25 years of expertise in IT asset management, Rcarré is committed to ensuring the security and digital sovereignty of its clients.

To meet your performance needs and compliance requirements, our teams integrate, design, and deploy artificial intelligence solutions hosted in Europe on servers and architectures that we maintain full control over.

Contact our teams for an initial assessment of your needs.