Document agent
Query an approved knowledge base, synthesise and cite useful passages.
DOSSIER 401
Private AI agents
Specialised assistants operating within a defined perimeter, using known sources and explicit human control.
Operational briefPRIVATEclosed perimeter
HITLhuman control
LOGGEDtraceable actions
Operational brief
Automate without surrendering command.
A useful agent is not a spectacular demo. It is a restrained system that knows what it may read, what it may produce, when it must stop and who validates its action.
AR / DOSSIER 401INDEPENDENT ANALYSIS
CONTROLLED SYSTEM
Automation remains a responsibility.
Sources, permissions, evaluation, validation, logs and stopping conditions are designed with the system. Performance matters only while data, decisions and reversibility remain governable.
02 / Intervention areas
Intervention areas
Monitoring agent
Collect, deduplicate, qualify and report only meaningful changes.
Editorial agent
Prepare structures, variants, metadata and checks without publishing alone.
Audit agent
Check pages, data, rules and anomalies against an explicit framework.
Business orchestrator
Coordinate tools or agents with escalation rules and centralised validation.
Decision copilot
Prepare options, sources, contradictions and consequences without replacing the decision-maker.
03 / FIELD NOTES
High-value automation
An agent is relevant when a process is frequent, documentable and important enough to deserve controls.
01DOCUMENTATION
Query a corpus
Retrieve a rule, compare versions and produce a synthesis with citations.
02MONITORING
Reduce noise
Absorb source volume and report only changes matching precise criteria.
03PRODUCTION
Industrialise with control
Prepare variants, checks, metadata and files without publishing alone.
04COMPLIANCE
Verify systematically
Apply a stable framework to pages, files or data and explain each anomaly.
04 / OUTPUTS
System components
The agent is only part of the system. Rules, tests, logs and interfaces matter as much as the model.
Mission specification
Objective, users, limits, measurable success and prohibited actions.
Source register
Authorised data, freshness, ownership, confidentiality and quality.
Tool matrix
Available capabilities, permissions, costs and invocation conditions.
Validation policy
Free, supervised and prohibited actions.
Evaluation set
Normal cases, traps, expected failures and success criteria.
Operational log
Inputs, decisions, tools, outputs and human interventions.
05 / Method
Security by design
Least privilege, bounded sources, logging, validation and stop strategies are designed before automation.
- 01Measurable business objective
- 02Authorised sources and data
- 03Strictly necessary tools
- 04Human validation
- 05Decision log
- 06Tests and known limits
- 07Design a safe stop
- 08Monitor cost and drift
06 / FAQ
Frequently asked questions
Clear answers on scope, limits, methods and engagement conditions.
Can an agent publish alone?
Technically yes, but it is not always desirable. Sensitive external actions remain validated.
Must a model be trained?
Rarely at first. Good context and tool architecture is often sufficient.
Can it run internally?
Local, private or hybrid architecture can be considered according to constraints.
How is this different from a chatbot?
An agent pursues a bounded mission, uses tools, keeps useful state and follows an action policy.
How are hallucinations limited?
Bounded sources, citations, validation, tests, explicit refusal and separation between generation and action.
Which data should not be provided?
Any data whose use, hosting, retention or recipient is not controlled.
How is return on investment calculated?
Actual time saved, errors prevented, processing speed, achieved quality and total operating cost.
08 / CONTACT
Open a private channel
State the context and objective. You will receive a clear initial reading.