Edu García
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Service · Applied AI

AI agents and internal copilots

Assistants wired into your data and tools: they query documentation, run steps and leave a trail. With rules, limits and control.

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Difference

An AI agent is not just a chatbot.

It becomes useful when the assistant can do things, not only write.

01

Queries real data

Connects to your documentation, database or API and answers with your information.

02

Uses tools

Can call specific functions: look up an order, generate a document, open a ticket.

03

Chains steps

Breaks a request into steps and runs them in order, checking the result.

04

Has limits

Allowed actions, validation and human confirmation where mistakes are expensive.

05

Leaves a trail

Every run is logged: what was asked, what was queried, what was answered.

06

Can be measured

Cost per operation, accuracy and time saved, so you can decide if it pays off.

FAQ

Questions about AI agents.

Is an AI agent just a chatbot?
No. A chatbot answers. An agent can query data, call tools, chain steps and take actions within defined limits, leaving a record of what it did.
What do I need for an agent to work?
A clear process, enough documentation and defined limits. Without those three, an agent just amplifies existing mess.
Can it read our internal documentation?
Yes, through RAG: your documentation is indexed and the agent answers citing that information instead of improvising.
What if the agent gets it wrong?
That's why limits are part of the design: allowed actions, output validation, human confirmation on sensitive steps and a log of every run.

Got a repetitive process?

Tell me what your team looks up a hundred times a day. That's usually the first agent worth building.

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