CUse cases

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

These use cases are anonymized. No client is named. They describe typologies of problems encountered in the field and how agentic architecture addresses them - without overpromise, without miracle recipe.

Transparency

Precise numbers (volumes, savings, ROI) are not communicated publicly. They can be discussed in an adapted confidential setting.

Reading map

Connect a problem family to the right intervention level.

The same use case family may call for a diagnostic, a sprint, a blueprint or build & run depending on maturity, risk and execution capacity. This map helps read the patterns without turning them into automatic promises.

Family

Operations

Recurring flows, implicit arbitration, reporting or multi-source synthesis.

Related examples

Multi-source synthesis

Diagnostic

Likely entry point

Compare several candidate workflows and prioritize the one that deserves a first initiative.

AI Sprint

Test a bounded deliverable when sources and output template are already clear.

Blueprint

Structure the architecture if the case spans several teams, data sources or responsibilities.

Build & Run

Industrialize when the workflow must be integrated, measured and improved continuously.

Data

Scattered knowledge, disconnected documents, uncertain access rights and quality.

Related examples

Document mapping

Diagnostic

Clarify sources, priority uses and risk zones before building.

AI Sprint

Test search or synthesis on a limited and controlled corpus.

Blueprint

Likely entry point

Define memory, access, logging, models and data governance.

Build & Run

Operate indexing, integrations, quality monitoring and corpus evolution.

Customer relationships

Qualification, support, escalations, routing and conversation improvement loops.

Related examples

Conversational qualification
Tier-1 support

Diagnostic

Identify high-effect journeys and cases that must remain human.

AI Sprint

Likely entry point

Prototype a short qualification or response path on known intents.

Blueprint

Frame intents, escalations, validations, customer data and responsibility limits.

Build & Run

Likely entry point

Deploy and operate when the agent becomes an operational channel connected to business tools.

Compliance

Sensitive decisions, auditability, GDPR, EU AI Act or human validation obligations.

Related examples

Compliance & traceability

Diagnostic

Map uses, risks and responsibilities before choosing the right perimeter.

AI Sprint

Reserve for very bounded deliverables, without automating a sensitive decision.

Blueprint

Likely entry point

Define governance rules, traces, validations and audit evidence.

Build & Run

Move to production only with monitoring, logging and control points.

Product innovation

Promising AI hypothesis, uncertain adoption, investment decision to inform.

Related examples

Product prototype

Diagnostic

Compare several product hypotheses if the perimeter or audience remain unclear.

AI Sprint

Likely entry point

Produce a short usage proof when the hypothesis and panel are identified.

Blueprint

Prepare target architecture if the prototype must become a durable product capability.

Build & Run

Build and operate after an explicit decision to continue beyond the pilot.

Indicative reading: the final recommendation depends on context, available data, risks and execution capacity on the organization side.

Transformation lens

What these patterns should change in the organization.

A use case is useful only if it improves how the organization understands, decides, produces or learns. The practice therefore looks for the transformation point before choosing the tool.

Clarity

Make visible the processes, responsibilities, data and arbitrations that were previously implicit.

Efficiency

Focus effort where AI can reduce cycles, rework and low-value tasks.

Maturity

Help teams progress through method, documentation and a clear understanding of system limits.

Decision

Replace isolated bets with argued prioritization, readable by leadership and technical teams alike.

Reading method

How to prioritize a use case.

Before launching an initiative, five criteria make it possible to compare two cases and arbitrate without giving in to the novelty effect.

01
Impact
Measurable business effect - revenue, cost, quality, lead time, satisfaction.
02
Feasibility
Technical maturity, available integrations, complexity of the required model.
03
Risk
Compliance, vendor dependency, decision exposure, reversibility.
04
Data
Existence, quality, access, usage rights and sensitivity of the required data.
05
Adoption
Identified sponsor, prepared users, ability to embed in the business gesture.

01Customer relationships

B2B Services

Diagnostic signals

High inbound volume with uneven qualification quality
Sales time absorbed by early discovery conversations
Unclear routing between sales, content and support paths

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.

Likely entry point

AI Sprint

Relevant when the inbound flow is already identified and a first qualification path can be prototyped on a short perimeter.

02Data

Regulated sector

Diagnostic signals

Knowledge scattered across disconnected documents
Internal search dependent on a few experts
Audits made harder by weak traceability and mapping

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

Near-instant access to information that used to be scattered and slow to retrieve.
Continuous mapping of the document base as it grows.
Audit facilitated by native traceability of accesses and answers.

Likely entry point

Agentic Operating Blueprint

Relevant when sources, rights, risks and use cases must be mapped before operational construction.

03Operations

Business leadership

Diagnostic signals

Recurring reports assembled manually from several sources
Synthesis dependent on copy-paste and implicit arbitration
Weak signals hard to detect in the information flow

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

Human time spent producing the report markedly reduced.
Editorial consistency upheld week over week.
Identification of weak signals invisible to sequential human reading.

Likely entry point

AI Opportunity Mapping

Relevant to compare several candidate workflows and prioritize the one that deserves a first agentic initiative.

04Customer relationships

Customer support

Diagnostic signals

Recurring questions saturating support teams
Escalations weakly structured between tier 1 and experts
Knowledge base hard to improve from conversation history

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.

Likely entry point

Build & Run Partner

Relevant when the topic involves integration, human escalation, observability and continuous production improvement.

05Compliance

Regulated sector

Diagnostic signals

Automated decisions difficult to explain after the fact
Human validations not systematically enforced on sensitive cases
Logs, risks and responsibilities scattered across tools

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

Traceability foundation usable for current requirements.
Reduced late-stage refactoring risk around EU AI Act obligations.
Strengthened trust from internal and external control bodies.

Likely entry point

Agentic Operating Blueprint

Relevant to define responsibilities, human validations, logs and governance rules before deployment.

06Product innovation

Product innovation

Diagnostic signals

Promising AI hypothesis with an uncertain product perimeter
Need to test adoption before full product development
Investment decision blocked without usage evidence

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.

Likely entry point

AI Sprint

Relevant when the hypothesis is already scoped and a short deliverable is enough to inform the next decision.

Coverage

Cabinet interventions cover all functional families - operations, data, customer relationships, compliance, product innovation. Each engagement is scoped to the specific context and perimeter of the organization.

Operations
Data
Customer relationships
Compliance
Product innovation

See how these patterns translate into engagement formats

Frame your case

Identify the pattern that matches your situation.

Every organization has its own context. A first exchange identifies the pattern(s) closest to your situation and frames an adapted intervention.

Frame my case with an architect See the method