Gatien
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Gatien
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AI agent
An AI agent is not an autonomous employee you hand everything to. It is a system that receives a limited objective, uses authorized tools, and produces a verifiable action or recommendation. Gatien can help you design that kind of agent without turning your workflow into a black box.
What can be built
Every project starts from a real workflow, real examples, and an output the team can check.
Read a request, identify the context, suggest a category, and prepare the next action.
Find information in your sources, cite references, and flag uncertainty.
Trigger authorized actions with logging, thresholds, and human review.
Prepare drafts, reports, summaries, or files before a person reviews them.
Use cases
These examples are a starting point. The right scope depends on your data, your users, and the business risk.
Prioritize, summarize, and prepare a reply without automatically sending sensitive messages.
Point employees to the right procedure, the right source, or the right owner.
Spot missing fields, inconsistencies, or next steps in a file.
Create a task, open a ticket, or update a record when the rules are clear.
How a mission runs
Clarify the business problem, the users, the available data, the constraints, and the expected result.
Build a first testable version on real examples, without overbuilding the architecture.
Check answers, errors, edge cases, permissions, and how much human review is needed.
Connect the tool to existing systems: internal app, CRM, Slack, Teams, email, document store, or API.
Hand over a maintainable base: code, configuration, known limits, success criteria, and next steps.
Stack
Deliverables
Scope
FAQ
Considering an AI agent? Start by listing the actions it could prepare, suggest, or execute.
Contact GatienA chatbot mostly answers messages. An agent can also use tools or prepare actions, so it needs much tighter guardrails.
Only on low-risk, well-defined actions. For everything else, it is better to prepare a recommendation or ask for approval.
With real scenarios, edge cases, missing data, different permissions, and a review of the logs.
Read next
LLM integrations treated as software product work: useful, testable, observable, and maintainable.
An assistant that answers from your documents, with visible sources and stated limits.
A page so you know what to expect before handing over an AI project.