Junyr Agents™: delegating business processes to AI with a clear mandate

McKinsey’s global survey published in November 2025 shows that the move from trial to scale remains limited: 23% of respondents say their organisations are scaling an agentic system in at least one function and a further 39% are experimenting, figures that describe the surveyed organisations rather than French SMEs specifically [1].

• The 62% total combines scaling and experimentation; it does not mean that 62% of companies have autonomous agents in production.
• Scaling is usually limited to one or two functions among respondents who report it.
• The practical issue is therefore delegation: deciding what an agent may do, with which data, under what control and with what evidence.

Junyr Agents™ formalises this delegation as a product configuration and operating method based on a mandate, permissions, approvals and a log, whose effectiveness must then be established on each real process.

• The mandate defines the expected outcome and boundaries of action.
• Permissions translate those boundaries into accessible tools and data.
• Human approvals protect sensitive or difficult-to-reverse decisions.
• The log makes execution understandable, recoverable and measurable.

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Assistant or agent: action marks the boundary

An assistant responds within an interaction led step by step, whereas an agent receives an objective, plans or sequences several operations and may call tools that have an effect in the information system.

• Interaction: an assistant proposes text, analysis or a command that the user takes forward.
• Execution: an agent may read an order, prepare a quotation, write a status or trigger a transmission.
• Risk: an incorrect answer can often be corrected before use; an incorrect action may reach a customer, database or financial workflow.
• Control: acceptable autonomy depends on impact, reversibility and the quality of the available signals.

This distinction gives teams a simple scoping test: as soon as a system can act beyond the conversation, its scope, technical identities, stop conditions and escalation routes must be written before deployment.

• Read-only access does not confer the same power as creation or deletion.
• Preparing an invoice is different from issuing it.
• Drafting an email is different from sending it outside the organisation.
• Making an HR recommendation is different from deciding an outcome for a person.

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Mandate, supervision and evidence: the three Junyr controls

The explicit mandate turns a vague objective into a versioned execution contract, approved by a business owner and precise enough to determine afterwards whether the agent remained within its role.

• Objective: expected result, recipient and completion criterion.
• Scope: authorised sources, accessible tools and permitted actions.
• Limits: amounts, data categories, operating times and excluded operations.
• Escalation: ambiguity, errors, conflicts or thresholds that return control to a person.

Documented human supervision allocates approvals according to risk instead of mechanically applying the same check to every step.

• Read-only actions and reversible drafts may be pre-authorised.
• External communications, business-record writes and payments need an explicit approval rule.
• Decisions involving people require review suited to the legal context and the rights concerned.
• The approver needs sufficient context, time and authority to challenge the output.

The auditable log should connect the objective, mandate version, identity used, tool calls, approvals and outcome so that the team can explain an execution and improve the system.

• Operations: turnaround times, failures, rework and cost per case.
• Investigation: data consulted, action taken and person responsible for approval.
• Improvement: recurring causes of escalation and rules that need changing.
• Compliance: evidence that may support particular duties, without presuming the system’s classification or replacing the applicable assessment.

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The stated architecture, and what it can actually control

Junyr’s public presentation describes a suite hosted in France, connected to Junyr Mail and an integrated ERP, with agent orchestration, permissions and email routing; these are architectural choices and configurable controls, rather than a universal guarantee of security or compliance [3].

• Channel: a mission can be triggered and followed from email.
• Business context: agents can work with the suite’s stated sales, production, HR, finance, marketing, ESG, administration and dashboard modules.
• Access: roles and permissions limit the data and tools assigned to each agent.
• Traceability: the Enterprise offer advertises an audit log; its coverage should be checked against the mandate and tested scenario.

“Night Reflections” are presented as scheduled cycles for consolidating and checking work outside production hours, but another model pass does not prove that the original result has become correct.

• The cycle can compare an output with rules or reference data.
• Detected differences can feed a rework or escalation queue.
• Deterministic checks remain preferable for formats, amounts, identifiers and calculable rules.
• Human sampling retains a role until the rate and severity of errors are established in the real context.

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Five agent patterns, configured for each process

Junyr presents five agent families as examples of missions designed around a company’s own mandate and data, rather than as a fixed catalogue capable of producing the same results everywhere [3].

• Quotations: qualify the request, prepare a proposal from the catalogue and submit it to sales.
• Invoicing: prepare and check an invoice from an accepted order before approval and transmission; the function is publicly described as under development.
• Reporting: compile ERP indicators on an agreed schedule and flag missing data.
• Market monitoring: follow a defined source list and produce a note with links and collection dates.
• Level-one customer service: classify requests, answer from an approved knowledge base and transfer uncovered cases.

Two internal engagements provide field observations, but their figures are neither a published benchmark nor a contractual promise for another customer.

• For a B2B distribution engagement, internal tracking reports quotation lead time falling from 4.2 to 1.1 days and conversion rising by 24%.
• For a B2B ecommerce engagement integrating five assistants with the ERP, internal tracking reports 58% less order-processing time, capacity estimated at 1.5 FTEs released and payback reached in nine months.
• Without a published case volume, period, indicator definitions and account of concurrent changes, these results describe those engagements only.
• A new pilot must therefore establish its own baseline, full cost and success criteria before extrapolating any benefit.

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Prohibitions matched to impact, law and reversibility

The proposed Junyr governance baseline excludes legally binding decisions without human approval, irreversible financial writes without dual control and unlogged external actions, then needs to be extended for the relevant sector and use case.

• Contracts, sanctions and HR decisions remain subject to an identified human authority.
• Payments, deletions and critical changes require separation of duties or dual approval.
• Every external transmission should retain the content, recipient, time, technical identity and applicable approval.
• Secrets, personal data and sensitive data need least-privilege access and treatment-specific retention rules.

The AI Act does not prohibit every form of scoring people: Article 5 targets defined social-scoring practices and resulting unfavourable treatment, while some uses for employment assessment, access to essential services or credit may fall within the high-risk regime depending on their purpose [2].

• Classification depends on the system, its intended use and the actor’s role, including provider or deployer.
• Article 14 governs the design of human oversight for high-risk systems; Article 26 specifies duties for their deployers.
• Article 12 requires logging capabilities for high-risk systems, with a scope appropriate to their intended purpose.
• A mandate, approval and log support governance but do not by themselves demonstrate compliance with the GDPR, AI Act or sector rules.

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A pilot built around four measurable decisions

The soundest starting point is to isolate a frequent, reversible sub-process, measure its initial state, run a bounded pilot and decide whether to expand it from the observed results.

• Choose: map expensive processes, then select a qualification, preparation or transmission task with a business owner.
• Scope: write the mandate, access rights, prohibitions, approval thresholds, reference data and stop procedure.
• Test: over a stated period and volume, compare quality, turnaround time, rework, incidents, satisfaction and full cost with the baseline.
• Decide: amend, stop or industrialise; before expansion, make logging, version control, monitoring and recovery part of normal operations.

The Junyr Scale™ describes this progression from Spectateur to Pionnier, but moving up a level characterises practice maturity and does not certify either an agent’s performance or a particular system’s compliance.

• Spectateur: understand the opportunities and risks before organised use.
• Artisan: experiment individually under elementary rules.
• Orchestre: coordinate uses and agents within shared processes.
• Architecte: industrialise data, integrations, governance and operations.
• Pionnier: develop new business models while maintaining the controls already established.

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Locate the first project before choosing the agent

The Junyr AI maturity audit is a free, no-commitment 30-minute video call that provides a position on the Junyr Scale™, the main obstacle, the first relevant project and a one-page follow-up.

• The diagnosis covers current uses, data, governance and internal skills.
• It may point towards training, information-system modernisation or hands-on support.
• Adopting Junyr Suite remains a later decision and is not a prerequisite for the audit.

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Sources consulted on 6 September 2026

[1] McKinsey & Company (5 November 2025), The state of AI in 2025: Agents, innovation, and transformation.

[2] European Union, Regulation (EU) 2024/1689 on artificial intelligence, especially Articles 5, 12, 14 and 26, Article 6 and Annex III.

[3] Junyr, public presentation of Junyr Agents™ and Junyr Suite presentation.

[4] Croissance et Transitions, internal tracking for B2B distribution and B2B ecommerce engagements, 2024–2025; unpublished data, cited as internal observations without generalisation.

Article written by Paul-Antoine TUAL, AI Transformation Leader, creator of the Junyr Method™.