AI agents acting within your systems,not just in a chat
GVISION designs and deploys AI agents capable of executing complete business processes across your CRM, ERP, and internal tools, thanks to the Model Context Protocol (MCP) — the standard now adopted by Anthropic, OpenAI, Google, and Microsoft to connect AI to your business data.
Secure MCP integration, multi-system Proof of concept in 2 to 4 weeks AI Act Governance & Compliance
Every sensitive action remains subject to human validation and full logging.
What is an AI agent and the MCP protocol?
An AI agent is a system capable of understanding a goal and executing a sequence of actions within your tools (CRM, email, ERP, databases) rather than simply answering a question. The Model Context Protocol (MCP) is the open standard, introduced by Anthropic in late 2024 and now supported by OpenAI, Google, and Microsoft, which allows this agent to be securely and standardly connected to your systems – often compared to a USB-C port for AI.
The MCP has become the de facto standard in just over a year
Since its launch in late 2024, the protocol has been adopted by almost all major AI providers – a rare consensus in the tech industry.
business applications will integrate an AI agent by the end of 2026, compared to < 5 % in 2025 (Gartner)
Monthly downloads of the MCP Python & TypeScript SDKs
Servers and MCP versions listed in the official register (May 2026)
companies surveyed already have AI agents in production
Sources: Anthropic (Dec 2025), Official MCP Register (May 2026), Stacklok 2026 Enterprise Software Adoption Survey, Gartner (2026 forecasts).
Anthropic Claude OpenAI / ChatGPT Google Gemini & Vertex AI Microsoft Copilot Studio GitHub
Two complementary approaches, non-competing
You are already using or considering Microsoft Copilot This is how a bespoke AI agent via MCP distinguishes itself and complements it.
| Microsoft Copilot | Bespoke AI Agent (MCP) | |
|---|---|---|
| Scope | Microsoft 365 applications (Word, Excel, Outlook, Teams) | Your entire ecosystem: CRM, ERP, databases, third-party tools |
| How it works | Assists a user with a task, in real-time | Executes an end-to-end business process, with or without continuous oversight |
| Access method | Per-user licence, immediate activation | Bespoke project, framed according to your internal processes |
| Typical use case | Write an email, summarise a meeting, analyse an Excel spreadsheet | Automatically qualify 200 leads per hour, reconcile financial data between 3 systems |
| Ideal for | Rapid and large-scale individual productivity gains | Automation of repetitive, high-volume processes |
Sources: Anthropic (Dec 2025), Official MCP Register (May 2026), Stacklok 2026 Enterprise Software Adoption Survey, Gartner (2026 forecasts).
according to your department
Every profession has its highest-value use cases. Here's where to start.
Sales and prospecting
Automatic qualification and enrichment of incoming leads, generation of personalised messages.
Customer service
Resolving first-level requests, detecting signs of dissatisfaction before escalation.
Finance
Automated bank reconciliation, data aggregation, and annotated report generation.
Human resources
CV pre-selection, automated technical tests, multi-week onboarding tracking.
Legal
Contract analysis, detection of non-compliant clauses, proposal of counter-proposals.
Operations & IT
Stock monitoring, code and test generation, IT supervision task automation.
From idea to agentIn production, in 5 steps
1
Framing workshop
Identification of the priority use case based on volume, risk, and business value.
2
Proof of concept
A pilot agent tested within a limited perimeter, to validate feasibility and value.
3
MCP integration
Secure connection to existing systems: CRM, ERP, messaging, databases.
4
Deployment & training
Staged deployment and training of teams to collaborate with the agent.
5
Supervision and governance
Performance monitoring, adjustments and ongoing compliance of the agent in production.
The MCP opens up possibilities, mais exige de la rigueur
Prompt injection
Malicious content can attempt to hijack the agent's instructions via the data it consults — the number 1 cause of production incidents.
Our responseFiltering of inputs, human validation on sensitive actions, isolation of untrusted sources.
Excessive access
An incorrectly configured agent may have broader permissions than necessary on your systems.
Our responseStrict application of the principle of least privilege and regular review of granted permissions.
Lack of traceability
Without supervision, it becomes difficult to know what actions an agent has actually performed.
Our responseComplete logging of actions and supervision dashboards for your IT teams.
References: OWASP MCP Top 10, NSA security recommendations on the Model Context Protocol, agentic AI security analyses (2026).
An AI that complies, not just performs
Your AI agents must comply with GDPR and anticipate the European AI Act. A political agreement on the "Digital Omnibus" (May 2026) pushed back the main obligations for high-risk systems from August 2026 to December 2027 — GVISION is closely monitoring these developments and integrating these requirements from the design phase of your agents.
Three formats, depending on your maturity level
Each project starts small and expands once its value is demonstrated.
Discovery & PoC
On request
- Use case scoping workshop
- Technical feasibility analysis
- Pilot agent on a limited perimeter
- Value report & recommendations
Deployment
On request
- Secure MCP integration with systems
- Agents in production on the validated use case
- User team formation
- Documentation and control procedures
Scale and governance
On request
- Continuous supervision of agents in production
- Extension to new use cases
- Periodic security and compliance review
- Priority technical support
Indicative pricing, adjusted according to the number of systems to connect and the complexity of your processes. Pricing provided after the scoping workshop.
clients supported
servers monitored
devices managed
years of expertise
Everything you need to know about AI agents and the MCP
What is an AI agent?
An AI agent is a system capable of understanding an objective, consulting multiple sources of information, and executing a series of actions within your tools (sending an email, updating a CRM, generating a report, querying a database) without a human performing each step manually. It goes beyond a conversational chatbot, which is limited to answering questions.
What is the MCP and why is it different from a classic API?
The Model Context Protocol (MCP) is an open standard, introduced by Anthropic in late 2024 and now adopted by OpenAI, Google, and Microsoft, which defines a unique way to connect an AI agent to your tools and data, rather than developing a bespoke integration for each application. It's often described as the USB-C of AI: a single connection, compatible with an entire ecosystem of tools already available.
What is the difference between Microsoft Copilot and a custom AI agent built with MCP?
Microsoft Copilot is an assistant integrated into your Microsoft 365 applications (Word, Excel, Outlook, Teams) that enhances individual productivity within an established framework. A tailored AI agent connected via MCP acts more autonomously, beyond the Microsoft ecosystem: it can orchestrate a complete business process across your CRM, ERP, internal databases, and third-party tools, with human oversight for sensitive actions. Both approaches are complementary and can be combined.
What are the security risks associated with MCP and how are they managed?
The main risks identified by the security community (OWASP MCP Top 10, NSA) are prompt injections, excessive access, and lack of traceability. Prompt injection remains, as of mid-2026, the leading cause of incidents in production according to agentic AI security analyses. GVISION applies the principle of least privilege, logs agent actions, and maintains human validation on sensitive actions.
Which use case should we start with?
We recommend starting with a high-volume, low-risk use case: lead qualification, first-level customer support responses, or financial data reconciliation. A scoping workshop can help identify the most relevant use case for your organisation.
How long does it take to get a first agent in production?
A proof of concept within a limited scope can be delivered in 2 to 4 weeks. Full production implementation, including secure MCP integration with existing systems, generally takes between 2 to 4 months depending on the complexity of the required connections and the number of systems to be linked.
Will an AI agent replace my employees?
The objective is to automate repetitive, low-value tasks to free up time for high-value assignments. Feedback indicates a reduction in the volume of first-level tasks rather than the replacement of teams, who remain responsible for complex decisions and the validation of sensitive actions.
How is an AI agent project priced?
GVISION offers three packages: a package for discovery and proof of concept, a project package for MCP deployment and integration, and a support subscription for ongoing monitoring and governance. The exact price depends on the number of systems to be connected and the scope involved – this will be communicated after the scoping workshop.
Are these projects compliant with the AI Act and GDPR?
Yes. Each AI agent project is designed with strict access and data management, anticipating the obligations of the European AI Act — whose main obligations for high-risk systems have been postponed to December 2027 by the political agreement on the Digital Omnibus — and the GDPR. GVISION relies on its expertise in ISO 27001, NIS2, and GDPR compliance to secure your AI deployments.
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