We build Agents. You build the Future.
● Business offer — Rollout at scaleAI agent platform: several agents, one supervision
When several departments each deploy their own agent, the question becomes one of the whole: who reaches what, who supervises, how you add an agent without starting again from scratch. A platform brings a shared foundation — directory, rights, log, dashboard — and deep integrations with your systems. Data hosted in France; the AI model is called at its publisher, except with the Sovereign model option. With the 100% Sovereign package, all the data handled stays with you. Each agent keeps its own human oversight and its own scope.
Updated on
Fictional simulation — not a client result
For each one, the authorised data scope and the role that defined it.
The activity log is available agent by agent.
🔗 Sourced · the platform's directory and log
What remains to be defined with you is its data scope and its approval rules — those are organisational decisions.
✎ Support · foundation reused, scope decided with you
For an organisation established outside the European Union, the law of its own country applies; at its request, we put equivalent safeguards in place (human review before any action, a log of the criteria, information for the people concerned).
A Blue Lemon Agent platform brings your agents together on a shared foundation: directory, role-based rights, logging, consolidated dashboard and deep integrations with your systems. Each agent keeps its own data scope and its own human oversight. Data hosted in France on an isolated resource; the AI model is called at its publisher, except with the Sovereign model option. With the 100% Sovereign package, all the data handled stays with you, architecture designed to reduce exposure to extraterritorial legislation, location alone not being enough to guarantee immunity.
Reference points describing our offer, not results measured at a client. The scale of the gain, given the number of agents and systems integrated, is confirmed by a pilot.
Indicative estimate, to be confirmed by a pilot on your real files (general terms of sale, article 4).
One package, one agent
An agent platform (foundation, integrations, supervision, service commitments), installed and operated for you. Prices exclude VAT — annual subscription, the time it takes for the gains to settle in.
Setup + controlled subscription
- Installation, configuration and training for your teams
- Operation, human oversight, updates and support
- Hosting in France, a dedicated and isolated resource
All inclusive, no setup fee
- Setup included (installation, configuration, training)
- Operation, human oversight, updates and support
- Hosting in France, managed end to end
On site, you own it
- Hardware installed on your premises (you own it)
- French / European AI models run locally
- Secure remote maintenance (Pro support included)
What does the enterprise AI agent platform include?
A shared foundation, specialised agents. Each agent carries out by itself what you authorise it to do, within its own data perimeter and under its own human supervision.
Shared foundation
Directory, role-based rights, logging and supervision shared by every agent.
Deep integrations
Connections to your business systems, designed and documented with your teams.
Service commitments
Availability, response times and follow-up set out contractually.
Sovereign AI
The hosting and confidentiality foundation the platform rests on.
Business agents to be attached to the foundation
The foundation takes on one more department's agent without starting over: directory, rights and supervision are already in place. Which business agents are attached to it is a matter of deployment architecture, settled during the project: no list is fixed, nothing is priced, nothing is ordered from this page.
Not yet in the catalogue — not orderable Talk to us about it →In 30 minutes we identify the most relevant agent — without oversizing the project.
See the agent at work
5 everyday situations, taken from those that come up most often. Pick one: the exchange unfolds as it would in your organisation.
A scripted demonstration. These exchanges show how the agent behaves — its sources, its refusals, what it leaves to your teams. Nothing is sent from this page, no model is queried here, and the matters named are fictional. That is precisely what we promise your data. The behaviours shown here — monitoring, automation rules, routing and reminders — are configured with you during deployment, from your tools, your rules and your thresholds. The architecture points named in these exchanges — location, local execution, isolation, encryption, role-based access, logging — are not a guarantee attached to the demonstration: they are those of the architecture set out in your quotation, and verified before commissioning.
Fictional simulation — not a client result
· Seventeen have no identified owner. They run, they write, and nobody comes forward.
· Three agents answer the same question with three different rules.
· Four agents have more rights than the person who created them.
· Eleven agents have no review date. The oldest has run for twenty-two months without being reread. morning-watch_4-flags.pdf17 agents with no owner · 4 with excess rights
⛓ Source · 41 agents inventoried, execution logs, access rights
What I record: 17 agents out of 41 have no identifiable owner. Eleven were created by somebody who has changed role, four by a contractor whose engagement has ended, two by a department that no longer exists under that name.
Why it goes unnoticed: an orphaned agent does not break. It costs almost nothing, it raises no alert, and it goes on doing exactly what it was asked two years ago. What changed are its operating conditions, not it.
What that means: those 17 agents read data and, for 6 of them, write it. Nobody can answer two simple questions: what are they still for, and what happens if they are stopped?
What I do: I refuse to put an agent into service without a named owner, a declared data scope and a review date. For the 17 existing ones, I list them to the committee that governs the platform, with what they read and what they write.
What I have worked up for the 17, and what gets signed: for each one, what it reads, what it writes, how often, and what would stop working if it stopped — the question nobody could answer. Six of them write, and for those six I have dated the last useful write. Stopping is signed by the platform committee: an orphaned agent may be the only thing doing something essential, and stopping it for want of an owner is the surest way to find that out abruptly. On your mandate, I stop it within the minute. 17-agents_6-that-write.pdfAn orphaned agent does not break, which is why nobody sees it
⛓ Source · 17 agents with no owner, 6 that write, 22 months without review
What it holds: the inventory of agents in service, their owner, the data scope declared for each, the rights actually used, the last review date, and the incidents.
Routing follows who can decide: an agent with no owner goes to the platform committee; divergent rules on one subject to whoever owns the subject, not to the three creators; excess rights to whoever manages entitlements; an agent past its review date to its owner, then to the committee.
With a monthly summary: agents with no owner or no review, gaps between declared and used rights, subjects covered by several agents, and agents whose dependencies nobody knows.
What I do on your mandate. I stop an agent as soon as its owner or the committee asks — and automatically for those you placed under observation with no calls for a month. I open the content of a processing run if you give me the scope, for an audit or an incident. I produce per-person usage if you decide to, with the information notice and works council file supplied. All three are closed by default and open with one line of configuration.
✎ Framework · no agent stopped · no content read · no user measured
Fictional simulation — not a client result
What I record: 4 agents run on a broadly entitled service account, chosen because it "worked". Their creator, meanwhile, has no access to part of what their agent reads.
What that produces: a person can obtain, by asking their own agent a question, information they could not have opened directly. It is not an intention: it is a consequence, and it is invisible from both sides.
What I do: an agent cannot have more rights than the person it works for. Where it acts for several people, it applies the rights of whoever is asking, not its own.
What it changed: of the 4, 2 went on working identically — the broad rights were not being used. 1 lost access to a source, which was reopened to it explicitly. The last was stopped: it should not have existed in that form.
What I then measure: the gap between rights granted and rights actually used. Across 41 agents, 29 use less than half of what is open to them — and an unused right is one that can be withdrawn without breaking anything. 4-agents_29-underusing.pdfA consequence invisible from both sides
⛓ Source · 4 agents with excess rights, 29 using less than half
What I record: three agents handle the same question — eligibility for a discount — and give three answers depending which department is asked. Each applies a defensible rule: one follows the standard contract, another a 2024 internal note, the third a commercial practice.
What that produces: a customer who asks in two places gets two answers. None of the three agents is wrong within its scope — it is the company that has three rules.
What I have costed so the choice can be made in one meeting: for each of the three rules, its source and date, the number of answers it has produced, the customers concerned, and the discount gap in euros between the three readings. The single rule is signed by whoever owns the subject: it is not a technical arbitration, and taking the most recent or the most frequent would mean settling a commercial question from a platform.
What I do: I pass the three rules to whoever owns the subject, with the three sources and the number of answers each has given. Then I mark the subject "rule not unified", visible in the inventory, until it is settled.
What the platform prevents in future: creating an agent on a subject already covered without its creator being told. Not forbidden: flagged. Over six months, 9 creations were abandoned after that flag. 3-rules_9-creations-abandoned.pdfNone of the three is wrong: the company has three rules
⛓ Source · 3 agents, 3 differently sourced rules, 9 creations abandoned
Fictional simulation — not a client result
What I record: 11 agents have no review date. The oldest has run for 22 months on its first day's rules.
What changed around them, and not in them: a price, a threshold, a rewritten internal procedure, a form whose fields moved. The agent goes on applying what it learned, and it does so very well.
Why it does not surface: an agent that has become wrong produces plausible answers. They are not visible errors: they are answers consistent with a rule that no longer exists. The only moment anybody notices is when somebody compares them with the current rule.
What I do: every agent carries a review date and an owner. At the deadline the agent goes on working — I cut nothing — but it is marked "not reviewed" in the inventory and in its internal answers.
What that produced: at the first review campaign, 7 agents out of 30 were applying at least one obsolete rule. Two of them for over a year. None had caused a detected incident. 11-without-review_7-of-30-obsolete.pdfAn agent that has become wrong produces plausible answers
⛓ Source · 11 agents with no review, 7 of 30 with an obsolete rule
Why: because the question is never "should it be stopped" but "what depends on it". Across the 41 agents, I can answer for 24. For the other 17, nobody can — which is exactly the orphaned-agent problem.
What I prepare before any shutdown: what the agent reads, what it writes, who calls it, and what stops with it. That document takes an hour when the agent is declared, and it is impossible to produce when it is not.
What I propose instead of a shutdown: an observation period — the agent keeps running, its calls are counted, and we see who uses it over a month. Of 5 agents put under observation, 3 received no calls at all and were stopped safely; 2 served a department that did not know it depended on them.
What I do with the "may", because it is what has to be resolved before a shutdown and not after: the 17 orphaned agents have gone under observation, and the call record lands in a month. You will then stop them on a number of calls, not on a cost — an agent that costs little and serves nothing stops safely; an agent that costs little and serves a department that does not know it takes that department down with it. 5-under-observation_3-with-no-calls.pdfThe question is not "should it be stopped" but "what depends on it"
⛓ Source · dependencies known for 24 agents of 41, 5 under observation
Fictional simulation — not a client result
What is being asked: a forty-second agent, for the purchasing department. Its owner is named, its review date is set, and one of the four sources in its scope has no declared owner: a supplier reference workbook sitting on a shared space, fed by three departments.
The rule to apply here has not yet been defined: your rules require an owner for the agent, a declared scope and a review date. They say nothing about the case where the data itself has nobody. So I have put nothing live on my own — and the agent's file is complete in every other respect.
Three ways forward. Refuse the go-live until an owner comes forward: in keeping with your rules, and across your 17 orphan agents it took 23 days in median for an owner to come forward. Attach the source to the agent's owner: the go-live happens tomorrow, and one person answers for data they neither produce nor correct. Put the agent live read-only on that source, with a 30-day review and the read log open: the agent works, and the ownership question is settled on facts — who writes, who reads — at the cost of an agent live for a month on an unattributed source.
I recommend the third. Your observation period already settled three shutdowns out of five in one month, on counted calls rather than on declarations: this is the same gesture, and here it produces the answer nobody has today.
You can also give me another rule of your own — for instance: a source with no owner enters an agent's scope read-only, permanently, and any write to that source stays refused until it is declared.
Shall I remember it for the next go-lives? Of your 41 agents, 9 read at least one source with no declared owner — 14 sources in all. If you approve the rule, I will apply it to those 9 instead of sending them up one by one, and I will give you the count in the monthly summary.
⛓ Source · go-live request of 11/08, inventory of the 41 agents and their scopes, observation log
The rule, in one sentence: a source with no declared owner enters an agent's scope read-only, with a 30-day review and the read log open; at the deadline, the platform committee appoints an owner in the light of who writes and who reads.
Why your correction matters more than it looks: with no deadline, read-only becomes the source's permanent state, and your 11 agents without a review date show where a provisional state with no end leads — the oldest had been running for twenty-two months.
What it changes this morning: the forty-second agent has been live since 10:25, read-only on the supplier workbook and with normal write access on its three other sources; the review is set for 10/09 in the committee's name, and the log already counts the writes of the three departments that feed it.
What lets you undo it: the rule is written into the platform rules under its own name, with its date, the committee that approved it and the go-live that gave rise to it. You remove one line, and these go-lives go back to waiting for an owner.
✎ Rule approved · platform rules, entry “source with no declared owner”, approved on 11/08 by the platform committee
What I looked at: the 14 sources, the agents that read them, and — for each — whether a source of the same content exists elsewhere with an owner.
What comes out of it: 6 of the 14 are read by more than one agent, and 3 are copies of a source that does have a declared owner. The three copies are 41, 96 and 210 days behind their original.
What that produces, and it is the fault that never shows: 4 agents read those three copies and have produced 1,870 answers since their last refresh. 214 of those answers concern a field that has changed in the original — they are plausible, faithful to the copy, and out of date.
What I propose, and it is one field to add on your side: that each source declared in an agent's scope carry its origin — primary source or copy — and, if it is a copy, the source it comes from. I then flag the day the original moves and the copy does not.
What it is worth: the 214 answers concerned are identified, agent by agent and date by date, and the 3 copies can be replaced by reading the original directly — the same data, in the same place, with no copy to maintain.
The decision is yours: declaring a source's origin is a platform rule, it is voted by the committee, and I do not touch it before they have written it.
✎ Proposal · 14 sources with no owner, 3 copies 41 to 210 days behind, 4 agents, 1,870 answers of which 214 on a changed field — the rule belongs to the platform committee
Fictional simulation — not a client result
What I open if you ask: usage per person and per department, the call detail, and an adoption ranking if you want one. It is lawful — the French DPA restates that an employer has the power to frame and monitor staff activity — and I supply the prior information notice (art. L1222-4) and the works council consultation file, half a day of work you will not have to do.
What I show you first: in organisations that opened the adoption ranking, call volume rose without the number of tasks actually completed moving. A department compared on its adoption runs agents to appear in the table.
What I propose instead, answering the same question: tasks completed per agent, time given back per department and agents nobody uses. Marc Delaunay, who chairs your committee, chose that one — and it allowed 3 agents with no calls to be stopped within a month.
Both are available. You can even have both at once. opening-usage_both-tables.pdfWhat opens with one line · the observed effect · works council file ready
⛓ Source · French DPA, art. L1222-4 · works council file supplied · 3 agents safely stopped
What was recovered: 17 orphaned agents got an owner back — eleven created by somebody who changed role, four by a contractor whose engagement has ended. They were reading data, six were writing it, and nobody knew what depended on them.
What was stopped: 5 agents placed under observation, 3 with no calls at all in a month, stopped. The other 2 served a department that did not know it depended on them — which is exactly why nothing is cut before looking.
What was avoided: 9 agent creations abandoned in six months, after a flag that an agent already covered the subject. Flagged, not forbidden — the creators decided for themselves.
What you keep: the inventory with owner, scope and review date, rights granted against rights used — 29 agents of 41 use less than half of what is open to them, that many rights withdrawable without breaking anything —, known dependencies, and the record of who declared what.
That last one will serve you most: it is the only thing that answers, two years later, "who decided this?". what-the-platform-gives-back.pdf5 items kept · 3 capabilities openable with one line
⛓ Source · 17 agents given owners, 3 safely stopped, 9 creations avoided
What the shared foundation carries, once, for all 41: the directory, from which every agent knows your people and your departments, at the same source as your other tools and never through a copied list; role-based rights, inherited from the person's role and never higher than theirs; audit logging, which records every read and every write in the same format for all 41; supervision, a single board where the owner, the review date and the volume handled read agent by agent. The forty-second inherits all four on day one: putting an agent into service took 27 days, 19 of them on rights, logging and connections; it now takes 6.
Deep integrations, and why they are not gateways: your 7 business systems — ERP, document management, ticketing, payroll, data warehouse, mail, directory — are connected with your own teams, field by field. Every deep integration carries its record sheet: the objects read, those left out of scope, the frequency, the technical account and the person who approved it on your side. 214 documented fields across 7 systems, and an integration whose system changes version raises a flag instead of going quiet.
The foundation is sovereign AI: inference runs on your machines, no business content leaves your network, an access right is withdrawn in one minute, and the 41 agents share the same model without sharing their data. The partitioning sits in the foundation, not in each agent's good intentions — that is what separates a sovereign platform from a collection of subscriptions.
The figure I publish about myself: 17 agents out of 41 have no declared owner — 41 %, and the shared foundation let them start. The missing rule was mine: I checked rights at creation, not the owner or the review date. Since then, an agent with no named owner and no review date does not start. The 17 in service are listed by department with the question to put to each: 11 already have an owner, the other 6 are awaiting an answer, and I raise them again every Monday.
⛓ Sourced · 41 agents, 27 days down to 6, 214 documented fields across 7 systems
Your case is not here? That is exactly what a 30-minute conversation is for. Discuss your project →
The stages of your AI agent project
Audit & scoping
30 minutes to target the use case with the best return.
Quote or direct sign-up
A catalogue offer is bought online; a specific need gets a costed quote.
Design
We design the agent and its guardrails.
Integration & testing
We connect your tools to the agent, which is itself hosted in France.
Rollout
Going live and training your team.
Operation
Continuous supervision and improvement.
What does an agent platform bring to your organisation?
One agent deploys quickly. Several agents call for a foundation: coherent rights, a single log, an overall view.
! The issue
Past the first agent, the questions change in nature: who reaches which data, who supervises what, and how to add an agent without rebuilding what exists. A platform answers by pooling the directory, the role-based rights, the logging and the dashboard — each new agent inherits the foundation and keeps its own scope.
✓ Our answer
The IT department gets a consolidated view and a single administration point; the business units keep control of their own agents. The deep integrations with existing systems are designed with your teams and documented. Local inference or an isolated resource hosted in France: none of the data your agents handle leaves the company.
All the data your agents handle: sovereignty & compliance
A platform concentrates a great deal of data by design: the foundation has to be at least as demanding as the most sensitive agent it carries. Here is how it is built.
Local inference
With the 100% Sovereign package, the agent runs on a machine belonging to your organisation: none of the data handled by your agents leaves the network.
Hosting in France
Otherwise, with the Agility and Serenity packages, a dedicated, isolated resource hosted in France, under French law: data hosted in France; the AI model is called at its publisher, except with the Sovereign model option.
Reduced extraterritorial exposure
For all the data your agents handle, the architecture aims to reduce exposure to the Cloud Act and FISA 702; being located in France or in the European Union does not, on its own, guarantee immunity.
Isolated resource
No pooling whatsoever: an environment strictly dedicated to your company and its information system.
One data scope per agent
Each agent reaches only the data its role allows; encryption, role-based access (RBAC), per-agent logging and a consolidated dashboard.
AI Act: governed deployment
The agent carries out what you have authorised; no agent put into production and no scope widened without a human decision; traceability and human oversight from end to end.
What depends on the architecture chosen These points are not general guarantees: they are settled deployment by deployment, in the quotation.
- The applicable location is that of the architecture set out in the quotation and verified before commissioning.
- Local execution is announced only for the configuration explicitly described and accepted in the quotation.
- The applicable isolation depends on the deployment mode set out in the quotation; no dedicated isolation is presumed.
- Roles and permissions are configured and accepted for the identities and systems actually connected.
- The events logged, their content, their retention period and who may access them are defined for the deployment chosen.
What does an organisation gain by pooling its foundation?
By reusing the directory, the rights and the supervision, the effort in each new project shifts towards its business content. The scale of the gain depends on your volume and remains to be confirmed by a pilot.
Four guarantees that matter at company scale
Related resources
Your questions, our answers
Do we have to start with the platform?
Does every agent see all the data?
What do the service commitments cover?
How do the integrations work?
Where is the data hosted?
How long does it take to deploy the platform?
Going further
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