AI agents that do real work inside your systems

A chatbot answers predefined questions or generates text with an LLM. An AI agent has a goal, reasons about how to achieve it, uses external tools (APIs, databases, email), executes actions, and self-corrects if something fails. It's the difference between an assistant that informs and a digital employee that executes.

It depends on complexity. Production agent (single-agent, 2-3 tools): €25,000-50,000. Agents with ML (classification, predictive analytics): €50,000-100,000. Enterprise multi-agent systems (orchestration, multiple integrations): €100,000-500,000+. Always with a detailed proposal before we begin.

A basic functional agent: 4-6 weeks. A complex agent with multiple integrations and a multi-agent system: 8-16 weeks. We deliver a functional prototype by week 3-5 to validate the approach before building the full version.

We design agents with human-in-the-loop: high-risk actions (payments, external communications, data modifications) require human approval. As the agent proves reliability, you can gradually expand its autonomy. Every decision is logged for audit.

Data is processed on European servers with GDPR compliance by design. Agents access only the data they need (principle of least privilege). Configurable guardrails: what it can do, what it cannot do, and what requires oversight. Full audit trail for every action.

Yes, integrating with existing systems is a core capability. We connect with HubSpot, Salesforce, SAP, Odoo, PostgreSQL, REST/GraphQL APIs, and any system with a programmatic interface. If it doesn't have an API, we evaluate alternatives (controlled scraping, hybrid RPA).

You don't need to train a model from scratch. Agents use pre-trained LLMs (frontier models from OpenAI, Anthropic and Google) and access your data in real time via tools. What you do need is programmatic access to your systems and documented processes. If you use RAG, then yes, you need a knowledge base (documents, FAQs, manuals).

Our architecture is model-agnostic: you can switch between OpenAI, Anthropic, Google, or open-source models without rewriting the agent. We monitor cost per execution with budget alerts. If a provider raises prices, we migrate in hours, not weeks. That continuous monitoring of cost, quality and model versions in production is LLMOps.

Yes. Nexo, our own platform built on Laravel, runs AI agents in production 24/7: they manage project tasks, draft client responses, and log completed work, always with human oversight. The cost-per-execution figures, the failure modes, and the guardrails we recommend come from that daily operation, not from a paper.

It depends on the risk level of the use case. Regulation (EU) 2024/1689 requires transparency, human oversight, and traceability, with fines of up to €35 million or 7% of global turnover (whichever is higher) for the most serious infringements. We design every agent with decision logging and human-in-the-loop from day one, and cover full compliance through our EU AI Act compliance service.

An AI chatbot converses and answers questions. An enterprise RAG system answers using your verified data. An agent goes further and executes actions: it queries systems, creates records, sends communications. They are not mutually exclusive: many agents use RAG as their source of truth. If the use case is not clear yet, a short AI consulting engagement defines it before you invest in development. If the process is linear and involves no judgement, n8n or Make may be enough: we compare them in n8n vs Make vs a custom AI agent.