Recurring patterns — six typologies of problems agentic architecture can address.

Situation

A B2B services organization receives a large volume of inbound requests, heterogeneous in maturity and relevance. The sales team spends most of its time on pre-qualification, at the expense of high-potential opportunities.

Cabinet intervention

Deployment of a conversational qualification agent that structures the initial conversation, collects key business information and routes to the right path (sales meeting, documentation, escalation). The agent runs as chat or voice depending on visitor preference, with human validation for out-of-frame cases.

Observed effects

• Reduced sales time spent on pre-qualification.
• Better routing of qualified prospects to the right teams.
• Complete traceability of interactions for downstream analysis.

Situation

An actor in a regulated sector has thousands of internal documents (procedures, contracts, minutes) without semantic mapping. Knowledge transfers poorly, and regulatory audits are expensive.

Cabinet intervention

Construction of a structured document memory layer: semantic indexing of documents, key entity extraction, concept linking, exposure via a conversational search agent. All accesses are logged, models used are hosted in Europe.

Observed effects

• Access in seconds to information that previously required hours of search.
• Continuous mapping of the document base as it grows.
• Audit facilitated by native traceability of accesses and answers.

Situation

A business unit must produce a weekly synthesis report from multiple sources (CRM, ERP, external platforms, emails, meeting notes). The exercise mobilizes several people for an entire day.

Cabinet intervention

Deployment of an agentic weekly synthesis workflow: automated data collection from sources, weak signal extraction, structured synthesis generated against a validated template, human review before distribution. Agent arbitration choices are documented.

Observed effects

• Report production divided by three in human time.
• Editorial consistency upheld week over week.
• Identification of weak signals invisible to sequential human reading.

Situation

A support service handles a high volume of tier-1 requests (recurring questions, standard problems), saturating teams and degrading response times on complex issues.

Cabinet intervention

Deployment of a support agent that autonomously handles requests matching known patterns, escalates to a human for out-of-pattern cases, and systematically logs conversations. Improvement loops gradually expand the agent's scope based on past escalations.

Observed effects

• Significant reduction in tier-1 volume handled manually.
• Improved response times on complex topics (teams refocused).
• Qualitative data enriched on recurring customer questions.

Situation

An organization subject to strong regulatory requirements (GDPR, healthcare/finance sectors, upcoming AI Act) must prove that every automated decision is traceable, explainable and reviewable. Existing tools do not natively provide this traceability.

Cabinet intervention

Construction of a governance layer that wraps existing agents: systematic logging of decisions, risk-based case classification, human validation checkpoints on high-stakes decisions, compliance dashboards accessible to control bodies.

Observed effects

• Full coverage of current traceability requirements.
• Anticipation of EU AI Act obligations without later refactoring.
• Strengthened trust from internal and external control bodies.

Situation

A product team wants to test a new value proposition relying on generative AI, without engaging full product development. The goal: validate adoption and business relevance within weeks.

Cabinet intervention

Construction of a functional but targeted agentic prototype, deployable to a panel of internal users or pilot customers. Adoption metrics, interaction quality and qualitative signals collected continuously. At pilot end, decision to continue, adjust or stop — without major technical debt.

Observed effects

• Fast validation of a product hypothesis with real data.
• Continuation or stop decision argued and documented.
• Learning usable for the next innovation cycles.