Agentic AI Governance: Securing Your Agents

The more autonomously an AI agent acts, the more concrete the need to oversee it becomes. At Jeausserand Audouard, a consulting firm specializing in high-end real estate transactions that manages a dense Microsoft Dynamics CRM covering company records, contacts and opportunities, every write action performed by the deployed AI agent goes through validation before execution, with complete traceability. This level of rigor is not optional as soon as the agent touches sensitive data. Here is why governance has become essential, how Qolaig set it up at this firm, which risks to anticipate and how to structure a solid framework without slowing down your AI projects or turning them into an administrative exercise.

Why governance has become essential

An AI agent that answers a question does not carry the same level of risk as an agent that modifies data, sends an email on behalf of the company or writes directly into a CRM. The more autonomy increases, the more costly the consequences of an error or unexpected behavior can be. Governance serves exactly this purpose: defining in advance what an agent is allowed to do, and how you make sure of it.

For a firm like Jeausserand Audouard, which handles sensitive data on high-stakes transactions, this rigor was non-negotiable from the scoping stage of the project.

The Jeausserand Audouard case: turning a general-purpose GPT into a governed CRM copilot

The firm had already adopted Custom GPTs for certain tasks, but these tools remained disconnected from the CRM, with no access to real business data. The challenge identified upfront was not only about the technical connection: the AI had to be connected directly to CRM data without bypassing security or governance, traceability and validation had to be guaranteed before any write into Dynamics, and low-value tasks still had to be automated.

Qolaig designed and deployed a secure MCP (Model Context Protocol) server, connected to Microsoft Dynamics and to the Kaspr enrichment tool, directly pluggable into the firm's existing Custom GPTs. Every write action, whether a creation, an update or an import, is subject to validation before execution. Rights are controlled by role, and every operation is logged.

The results: Dynamics accessible in natural language from the company's Custom GPTs, lead enrichment in a few seconds without leaving the work environment, zero unvalidated writes with complete governance and traceability on every CRM action, and deployment by day 5, reusable across all of the firm's internal GPTs.

This case illustrates a central principle: governance did not slow the project down, it turned it into durable, extensible infrastructure rather than a one-off tool to be rebuilt for every new need.

The three pillars of solid governance

The scope of action. Each agent must have precise permissions: which data it can read, which systems it can modify, which actions remain reserved for human validation. At Jeausserand Audouard, rights are controlled by role, a principle that can be transposed to any organization.

Traceability. Every decision and every action of an agent must leave a trace that can be consulted. Without this trace, it is impossible to understand an error after the fact, or to demonstrate compliance in the event of an audit.

Human oversight. An autonomous agent does not mean an agent without control. The principle of validation before writing, applied at Jeausserand Audouard, is directly reusable on any project connected to a critical business system.

An example of an agent sheet

In practice, governance takes the form of a one-page sheet per agent, written before going into production. It states the agent's objective, the data it can read, the actions it can trigger on its own, those that require human validation, the person responsible and the date of the last review. This sheet serves as the reference in the event of an incident, an audit or a change of team. It takes an hour to write and avoids weeks of vague discussions later.

Who should own governance in the company

Governance fails when it is entrusted to a single function. Management sets the acceptable level of risk, the business team describes what the agent must actually do, the technical team configures the rights and traceability, and a referent (often IT or the legal department) validates sensitive cases. In an SME, one person can hold several roles, provided their decisions are written down.

The concrete risks to anticipate

Cascading hallucinations. An agent that chains several steps can propagate an initial error across the whole chain of subsequent actions, unlike a chatbot that is limited to a single answer. Anchoring the agent's answers in real data rather than in its general knowledge alone greatly reduces this risk. Our article on the enterprise chatbot shows this principle on a simple scope.

Overly broad access. An agent connected to several systems with poorly configured rights can access data that does not concern it, or modify sensitive information without any malicious intent, simply through excessive permissions.

Dependence on a single provider. Relying entirely on one model or platform without a fallback plan exposes the company in the event of changed terms or an outage.

Lack of user training. A well-governed agent remains vulnerable if employees do not know what it can do or how to report an anomaly. A short onboarding session and a clear contact address are enough to reduce this risk.

How to structure governance without blocking projects

1. Classify agents by level of risk. An agent that answers an internal question does not need the same oversight as an agent that writes into a CRM of high-stakes transactions.

2. Document each agent before deployment. Objective, scope, data used, possible actions: this sheet must exist before going into production.

3. Plan an alert and validation circuit. The model applied at Jeausserand Audouard, where every write goes through validation before execution, remains the reference for any system connected to sensitive data.

4. Reassess regularly. An agent's real uses can drift from what was planned at the start. A periodic review checks that the initial scope remains appropriate.

5. Plan a stopping point. For each agent, define in advance how to suspend it quickly (cut an access, disable a function) if unexpected behavior appears. This procedure should be tested, like a fire drill.

According to the Institut Montaigne, in its June 2026 action note, French companies must move beyond the simple injunction to adopt AI and see governance as a lever for the lasting transformation of their organization, rather than an administrative constraint added after the fact.

The sectors where the demand for governance is strongest

Firms that handle sensitive data with high financial or legal stakes (real estate transactions, consulting, finance) are naturally the first to formalize these rules, as the Jeausserand Audouard case shows. But the principle is spreading: as soon as an AI agent has access to customer, financial or personal data, the question of governance arises, whatever the size of the company or its sector. For the technical connection to systems, the Model Context Protocol used in this project offers a standard way to give an agent controlled access to business tools.

FAQ

Does AI governance slow projects down?
Poorly applied, yes, if it becomes a bureaucratic layer disconnected from the field. The Jeausserand Audouard case shows the opposite: the validation and traceability framework set up from the start enabled deployment by day 5, then reuse on other internal GPTs without redeveloping the connectors. A lighter example is described in our article on the AI copilot in the enterprise.

Does an SME really need formal governance?
Yes, but proportionate to its size. An SME does not need a dedicated committee, but it does need a minimum: knowing who validates what, and keeping a record of the actions of deployed agents. On the regulatory side, the European AI Act also sets transparency and oversight obligations that depend on the level of risk of each use.

At Qolaig, our Workshop → Build → Run methodology frames this type of project before any technical investment. Let's talk about your project.