Trades & automation

AI agents that start from knowledge you can trust.

Give AI agents a precise mission, the right knowledge and clear limits.

What you need

An instruction alone does not make an expert AI agent.

To carry out a trade mission, an agent needs context: your methods, your references and your rules. Narratheque lets you start from a dedicated knowledge base, then define the role expected. The scope is decided before the configuration: what it must know, what it must not see, and who reviews its answers.

Le socle de cet usage

Les connaissances à réunir

Modes opératoires, guides de rédaction, référentiels métier, documentation produit et exemples validés.

Comprendre la base de connaissances
In your day-to-day

What well-framed AI agents change.

Specialise the mission

Frame an agent around one concrete piece of work, with an explicit expected result.

Reuse your method

Attach the references and examples that give its instructions meaning.

Let the uses grow

Look into actions in your own tools when the need goes beyond the platform.

A concrete example

AI agents: from knowledge to answer.

Illustrative example: the real result depends on the sources, the model and the configuration.

Question ou consigne

« Prépare une synthèse de ce compte rendu selon notre méthode. »

L’agent organise les éléments fournis selon votre trame : décisions, questions ouvertes, prochaines étapes. Une personne vérifie la synthèse avant sa diffusion.

Getting started

Your first AI agents: one scope, then the rest.

01

Define the mission

Choose one limited task and a success criterion you can observe.

02

Connect the knowledge

Select the sources, the model and the instructions that suit.

03

Test, then extend

Validate the results before adding tools or external actions.

The framework to plan for. Access to a knowledge base does not automatically permit actions in your software. Every external action needs rights, controls and a named owner. In every case, decide who approves the answers before they go out, how often the sources are refreshed, and what must stay out of scope.
Frequently asked questions

What you want to know about AI agents.

Can an agent act inside my software?

An external action requires a connector, explicit rights and a named owner. It is decided once the answers have been validated, not when you first set things up.

Do we need one agent per task?

It is the easiest thing to keep under control: one mission, one success criterion, one scope of sources. Several agents can share the same knowledge base.

Who reviews what the agent produces?

You do. The platform drafts, a person approves before it goes out — particularly for content meant for a client or an official document.

Let us start simply

Let us look at your AI agents with your own data.

A first conversation to choose the corpus, the users and the scope worth having.

RDV démo

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