AI Agent

An AI agent goes beyond a simple chatbot: it chains multiple actions, uses external tools (API, databases, search) and reasons to complete a mission. Some typical use cases:

• Customer service - qualify the request, formulate a response, create a ticket, escalate if necessary.
• Response to calls for tenders - assemble the parts, write the technical paragraphs, verify the specifications' constraints.
• Accounting and back-office - extract data from an invoice, feed the ERP, trigger a payment.
• Internal Operations - audit a database, correct inconsistent data, generate a summary report.

Some key principles:

• Narrow scope - an agent who does one thing well is better than a generalist agent who does everything poorly.
• Well-defined tools - each action the agent can take is exposed as a documented function (read, write, search...).
• Safeguards - deterministic rules to block critical actions, human validation on sensitive decisions.
• Observability - trace each reasoning step, measure the success rate, identify infinite loops.

• Cost - an agent that loops can consume a huge amount of tokens. Cap and monitor the cost per task.
• Security - an agent acting on the IS must have limited rights, never those of an administrator.
• Adoption - it's safer to start with internal use (ops teams, N1 support) before exposing an agent to clients.