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An enterprise AI agent is an autonomous system combining large language models (LLMs) and contextual understanding to analyze your business data, make decisions, and act directly within your systems. Unlike a static tool, it learns from each interaction to optimize repetitive internal processes. In a multi-agent architecture, several specialized agents collaborate to handle complex tasks end-to-end.

A chatbot answers questions; an AI agent acts. It integrates into your business workflows (CRM, ERP, internal portals) to execute tasks: filling forms, classifying cases, generating responses, triggering actions. This is the evolution toward intelligent RPA: no longer a rigid script, but an agent capable of understanding context and tracing every decision for audit.

A full local AI runs directly on your internal servers, without depending on external cloud APIs, ensuring security, confidentiality and technological independence.

Sovereign AI guarantees that your sensitive data never leaves your infrastructure: native GDPR compliance, independence from US cloud providers, and protection against extraterritorial jurisdictions like the Cloud Act. It's a digital sovereignty issue for businesses handling regulated data (health, finance, defense) or strategic information.

Sovereign AI agents deployed on-premise automate repetitive tasks, reduce operational costs with predictable fixed pricing, improve team productivity, and guarantee that your business data never leaves your infrastructure.

AI agents automate processes with high volume and explicit business rules: back-office automation (cross-portal data entry, document OCR, reporting), intelligent RPA on legacy applications, multichannel conversational agents (phone, email, chat), claims classification, compliance checks, contextual response generation. Any process with documentable decision rules is a candidate.

No. AI agents augment your teams by absorbing repetitive low-value tasks, freeing your staff for strategic missions. It's a progressive enterprise AI transformation, agent by agent, process by process, not a brutal substitution. Humans stay in the loop for sensitive decisions and continuous validation.

An agent team operates through multi-agent orchestration: a supervisor agent receives the request, classifies it, then delegates to specialized expert agents (understanding, decision, action). Each agent verifies the previous one's result and can query existing information systems (business APIs, databases, RAG document retrieval, MCP protocol for standardized integrations). If confidence is insufficient, human escalation is automatic. The choice of orchestration frameworks and LLMs (open source or proprietary) depends on the client context: sovereignty, latency, cost.

Unlike usage-based cloud pricing, our full local on-premise model relies on fixed infrastructure costs. Your expenses are predictable and controlled over time, regardless of call volume.

The full local fixed-cost infrastructure model enables fast and predictable ROI. Expenses don't depend on volume processed, unlike usage-based cloud APIs. Mature use cases reach payback in 3 to 6 months (Deloitte, 2024). Beyond that, each automated process becomes a recurring saving.

Yes. All data stays on your internal servers, with no transfer to external services. The architecture is designed to comply with GDPR, the European AI Act, and ISO 27001 compatible standards. We apply OWASP LLM Top 10 recommendations (prompt injection mitigation, supply chain, model output) and each agent maintains a complete audit trail: who did what, when, with which decision.

Personalization combines two mechanisms: RAG (Retrieval-Augmented Generation) connects the agent to your knowledge bases in real time, and targeted fine-tuning adapts it to your business vocabulary. The LLMs used are open source (Mistral, Llama, Qwen) running locally, allowing training on your data without exposing it. Workflows are configured collaboratively with your teams to match your existing rules.

An AI agent deployment follows 4 stages: scoping priority processes and AI audit of the existing setup (week 1), designing the multi-agent architecture adapted to your IT (weeks 1-2), building agents and training on your data (weeks 2-3), going into production with monitoring and lasting support (weeks 4-6). A first use case is operational in 4 to 6 weeks, then continuous iterations.

Sovereign AI agents primarily serve regulated and demanding sectors: insurance, health mutuals, finance, ACPR compliance, back-office automation and AI managed services. Any sector with sovereignty, traceability or regulatory deadline constraints.

TheNewAgent.ai stands out with 100% sovereign and local AI (on-premise), proprietary multi-agent architecture, deep customization on each client's business processes, and end-to-end support from audit to sustainable deployment.