What is the difference between an AI agent and a simple chatbot?

A chatbot answers questions. An AI agent takes action: it queries your databases, calls APIs, makes bounded decisions, triggers actions (create a record, send an email, update a CRM) and knows when to hand off to a human. The agent is focused on a business outcome, not just conversation.

How much does it cost to develop a custom AI agent?

A first bounded agent (support, triage, extraction) typically starts between €5,000 and €15,000 in setup, plus API costs and a maintenance plan. A multi-agent system connected to several tools is scoped after an audit. ROI is measured in hours saved and shorter turnaround times, often reached within 2 to 4 months.

How long does it take to deploy an AI agent?

For a scoped use case, expect 2 to 4 weeks: one week of audit and design, two to three weeks of development and testing in pre-production, then go-live with monitoring. The first measurable results generally appear within 30 days.

Which model do you use: Claude, GPT or Mistral?

We choose the model based on the task and your constraints. Claude for document analysis and bounded agents, GPT for versatility, Mistral for sovereign or embedded needs. A single agent can combine several models. We stay agnostic: the right model in the right place, no imposed lock-in.

Is our data safe with an AI agent?

Yes, with rigorous design: European hosting of sensitive components, disabling training on your data, logging, minimisation and pseudonymisation. We sign a DPA and provide the documentation needed for your GDPR compliance. It is a prerequisite, not an option.

Custom AI Agents: development for businesses

Definition

What is a custom AI agent?

A custom AI agent is autonomous software powered by a language model, built for a specific task in your business — support, case triage, extraction, follow-up. It knows your rules, your data and your tools, and it acts inside your systems. Lumyniq designs these agents from audit to deployment.

The difference with a generic solution comes down to three things: the agent is connected to your data (CRM, documents, business database), it follows your rules (what it can and cannot do), and it integrates into your existing tools. That is what makes it genuinely useful rather than impressive in a demo.

For the general definition, with ten tasks sorted into agent, chatbot and automation, see our guide to AI agents in business: examples and ways to build one.

Distinction

AI agent or chatbot: what is the difference?

A chatbot answers questions; an AI agent takes action. The agent queries your databases, calls APIs, makes bounded decisions, triggers concrete actions (create a record, send an email, update a CRM) and knows when to hand off to a human. It is focused on a business outcome, not just conversation.

Capability	Classic chatbot	Custom AI agent
Answer a question	Yes	Yes
Access your internal data	Rarely	Yes (RAG, API)
Trigger actions	No	Yes (create, send, update)
Follow business rules	Limited	Yes, bounded
Escalation to a human	Basic	Contextual
Objective	Conversation	Business outcome

Chatbot vs agent — Lumyniq, 2026.

If what you need is a chatbot, an off-the-shelf platform may be enough. Botpress has billed per conversation since 14 May 2026. We worked out what Botpress costs at 1,000 conversations a month, with the same calculation for Voiceflow, Chatbase and the other alternatives.

Examples

AI agents we deploy

Support agent. Answers customers from your knowledge base, escalates complex cases — see our business AI chatbots comparison.
Phone agent. Answers calls, qualifies callers and books appointments (see AI phone receptionist).
Extraction agent. Reads contracts, quotes and invoices and fills your system automatically.
Qualification agent. Scores and follows up with leads (see intelligent CRM).
Onboarding agent. Prepares documents, access and journeys (see HR onboarding agent).
Internal RAG assistant. Answers your teams' questions about your documentation, with sources.
Agent built from your website. For common needs, our tool Agent Studio sets up an agent from your pages, waitlist open.

The same agent is not built the same way in every industry: the data, compliance and tempo constraints change completely. We break down what that means sector by sector for real estate automation, for law firms, for healthcare providers and for HR teams.

Method

How we build your agent

We build your agents with the same four-stage method as the rest of our projects:

Audit (1 week). Choosing the quick-ROI use case, framing the rules and the data.
Build (2–3 weeks). Developing the agent, connecting the tools, guardrails, evaluation.
Deployment (1 week). Go-live with monitoring, logs and human validation.
Optimisation (ongoing). Performance tracking, prompt tuning, scaling up.

We often orchestrate these agents with n8n and power them with Claude, GPT or Mistral depending on the task.

Our guides and comparisons document the technical trade-offs behind each of these stages. Teams that want on-site working sessions in the Paris region work with our Paris AI agency.

FAQ

Frequently asked questions about custom AI agents

A custom AI agent is an autonomous piece of software, powered by a language model, built to accomplish a specific task in your business: handling support, triaging cases, extracting data, following up with prospects. Unlike a generic chatbot, it knows your rules, your data and your tools, and it acts inside your systems.