Asynchronous work: in the age of AI agents, everyone becomes a manager

In-depth version, June 2026. This text extends and broadens my article “Junyr Agents™: delegating AI without losing control” (1 June 2026). Where the first described how to delegate to an agent, this one sets out the broader thesis that such delegation establishes: when the agent works in deferred time, work becomes asynchronous. Everyone, from the intern to the business leader, then inherits a job they did not choose: that of manager.

The idea, in one page

An AI agent does not work like a piece of software you drive from the keyboard, nor like a colleague you wait for in a meeting. You give it an objective, it goes off to work on its own for a few minutes, sometimes a few hours, and it comes back with a result to review. This simple change of tempo has a consequence that few organisations saw coming: work stops being synchronous. You no longer “do” the task in real time; you entrust it, you track it, you validate it. And entrusting, tracking, validating are not the acts of a doer. They are the acts of a manager.

This is the real upheaval of 2026, and it runs deeper than the badly framed question of job “replacement”. As agents absorb execution, every employee finds themselves at the head of a small team that does not sleep, does not unionise, and does not wait until Monday to deliver. Microsoft, which surveyed 20,000 workers across ten countries including France in 2026, puts it plainly: the human role shifts “from producing answers to evaluating, refining and owning them” [1]. Everyone becomes a manager. The good news is that it is a promotion. The less good news is that being a manager is a job. No one ever taught it to us.

From the synchronous assistant to the asynchronous agent

Let us recall the distinction, because everything follows from it. An assistant operates in a synchronous loop: you ask it a question, it answers, you keep control at each exchange. This is the classic conversation with a chatbot. An agent, on the other hand, operates in an asynchronous loop: you give it an objective, and it strings together the actions on its own, across several tools and several steps, until it reaches it. The difference is not a matter of power, but of nature: the agent acts in the real world, and it acts while you are doing something else.

This temporal gap is not a technical detail: it is the heart of the matter. When the answer is instantaneous, you remain an assisted doer. When it arrives in deferred time, you necessarily become something else: someone who has launched a piece of work, who awaits it, and who will have to answer for it. The researchers who study asynchronous software agents observe as much: the issue is no longer the speed of a request, but the ability to formulate a clear mandate, to break it down, and to verify what comes back [2]. Exactly the work of a supervisor.

The phenomenon is no longer marginal. Across the Microsoft 365 ecosystem, the number of active agents has multiplied by 15 in one year, and by 18 in large enterprises [1]. On maturity, the picture stays honest: according to McKinsey, 62% of companies are experimenting with AI agents in 2026, but only 23% scale them in at least one function [3]. The gap between these two figures is not down to the technology: it works. It is down to an organisational skill that few yet master: knowing how to make agents work in deferred time without losing the thread. In other words, knowing how to manage.

The shift: from doer to orchestrator

Let us put it simply. For two centuries, technical progress consisted in giving better tools to the doer. The AI agent does something different: it takes over execution, and hands back to the human the role that sat above. Microsoft calls this “the new agency equation”: as agents take on execution, the human gains latitude: “more room to direct the work, make the calls, and carry the results” [1]. The practitioners who already orchestrate several agents in parallel describe the same tipping point: you move “from the conductor, one musician guided in real time, to the orchestrator of an entire ensemble, coordinated asynchronously”; the mental model is no longer that of pair programming, it is that of managing a team [4].

This shift has a name that analysts have ended up popularising: everyone becomes an agent boss, a “boss of agents”. And it is not merely a turn of phrase. In the Microsoft survey, the most advanced workers (those who use agents for multi-step tasks and build multi-agent systems) still represent only 16% of the panel, but they give a glimpse of the job to come: they redesign their processes around intent and review, and 80% of them report producing work they could not have produced a year earlier [1]. Academic research on parallel agent loops reaches the same point by another route: performance depends on two factors, the genuinely parallelisable structure of the task, and “the delegation capacity of the central manager” [5]. The bottleneck is no longer the machine. It is the manager.

This is also, very precisely, what the MATIA Method™ describes when it places an SME on its five-level AI maturity scale: Spectateur, Artisan, Orchestre, Architecte, Pionnier. The move to the agentic is not a leap from Spectateur to Pionnier: it is the accession to the Orchestre level, the one where you stop tinkering with isolated uses and start coordinating a whole. The word was already in the model. Asynchronicity has merely given it its full reach.

Work now happens while you sleep

If the role changes, so does the tempo. The defining feature of the 2026 company is no longer flexibility of place, the legacy of remote work, but flexibility of time. Work unfolds continuously, often invisibly, sometimes outside human hours: one agent drafts a document at night, another monitors an indicator, a third prepares a report for Monday morning [6]. In software development, which often serves as an advanced laboratory, several agents run in parallel on separate branches, each on its own task, and the developer moves from writing to coordination: giving the direction, reviewing, correcting the trajectory [4].

This parallelisation has a virtue and a trap. The virtue: you no longer do one thing at a time. The trap: it is easy to confuse “many agents launched” with “much value produced”. The teams that measure productivity have learnt this at their own expense: the METR institute had to overhaul its entire experimental protocol in 2026, because time spent per task became uninterpretable once a single worker drives several competing agents [7]. The lesson is clear: value does not come from the number of agents, but from the quality of supervision. A manager who opens ten worksites they do not review is not ten times more productive. They are ten times more exposed.

The other side of the promotion: being a manager is a real job

It must be said with the same frankness as the rest, because this is where most projects stumble. Becoming a manager of agents is not merely reaping the time saved. It is inheriting the burdens of management. They are real.

The verification tax. Everything an agent produces must be reviewed by someone who answers for it. The more agents execute, the more the stakes move towards human evaluation: approving a bad result is harmless once, but at scale, the errors that slip through compound [1]. In the Microsoft survey, the two skills judged most important in the age of agents are not technical: they are quality control of AI outputs (50%) and critical thinking (46%); and 86% of advanced users say they treat any AI output “as a starting point, never a final answer” [1]. The manager is not the one who produces. It is the one who reviews, who decides, and who signs off.

The limit of supervision. You do not monitor asynchronous work the way you monitor synchronous work. The review comes after the fact, and it fails when the agent can cause harm before being reviewed, that is, when the time to harm is shorter than the time to control [8]. Recent work points to the “practical impossibility” of real-time, meaningful and continuous human supervision over long autonomous processes [8]. The answer is not to give up on delegating: it is to design the delegation so that no irreversible action escapes a control point. A good manager does not review everything; they know what to review before it becomes irreversible.

The deskilling risk. If the machine does the work, the human can unlearn. The literature speaks of deskilling (the loss of acquired expertise) and, more worrying, of never-skilling: beginners who never acquire the fundamentals because they lean on automation too early [9]. The most clear-sighted workers have understood this and guard against it: in the Microsoft survey, 43% of advanced professionals (against 30% of the others) say they deliberately do part of their work without AI to keep their skills sharp [1]. The good manager does not merely hand out the work: they keep their hand in the craft, otherwise they lose the ability to judge what they hand out.

The burden, finally. Not everyone dreams of becoming a manager. Coordinating, arbitrating, being accountable for others (even if they are agents) is work in itself, which does not mechanically lighten the mental load; it shifts it. This is the paradox Microsoft calls the “transformation paradox”: 65% of workers fear falling behind if they do not adapt quickly, but 45% find it safer to focus on their current objectives than to reinvent the way they work. Only 13% feel rewarded for that reinvention, even when it yields no immediate result [1]. The promotion to manager cannot be decreed. It is organised, or it exhausts.

What sets apart those who succeed: discipline, not the tool

The most useful data point of the year, for a business leader, fits in one sentence. When Microsoft measures what really explains the impact of AI, organisational factors (culture, managerial support, HR practices) weigh more than double the individual factors (67% against 32%) [1]. In other words: value does not come from the employee most gifted with AI, it comes from the environment that turns that talent into results. The same report shows that when managers use AI openly and set quality standards, teams’ trust in the agentic climbs by 30 points [1] [10].

What the best do, concretely, is nothing mysterious. They do not seek to “do more things faster”; they redefine their value around what only a human brings: setting a clear intent (the expected result and the required standard) and designing the division of work between humans and agents [1]. They refuse to outsource their judgement. And they impose on themselves an execution discipline that we, at Junyr, formalise in the form of a triptych applied to each step of an agent: Plan → Execute → Verify. The agent first announces what it is about to do and the success criteria; it executes; then a distinct step checks the result before continuing. Verification becomes a native step of the process, not a control added after the fact, which is what makes the delegation auditable, and therefore sustainable. This is the principle on which Junyr Agents™ rests: a written mandate, documented supervision, a log of every act.

For an SME business leader: you are not buying agents, you are building a team

The practical consequence is simple to state and demanding to hold to. Deploying agents is not buying one more piece of software; it is taking charge of a team. Everything we know about management becomes relevant again. Three decisions follow.

First decision: write the job descriptions. An agent without a clear mandate is an employee without a job description: you can neither steer it nor audit it. Each agent receives an objective, an authorised scope of action, explicit limits and escalation cases. This looks bureaucratic; it is exactly the opposite: it is what makes it possible to delegate more, because you delegate with confidence.

Second decision: build the review infrastructure. As agents execute, the question is no longer “can the machine do it?” but “who answers for what it does?”. Microsoft sums up the task in three questions every organisation will have to settle: who reviews the performance of the agents? who has authority to modify the process they execute? how does a local gain spread to the whole organisation? [1]. An SME that can answer these three questions builds what Microsoft calls a “proprietary intelligence”, an in-house know-how that capitalises and that a competitor cannot copy [1].

Third decision: treat agents as entities to be governed. The software vendors themselves are switching to this logic: in May 2026 Microsoft rolled out a dedicated control platform for managing deployed agents, with identities, permissions and traceability [11]. For an SME, the point is not to have the same factory as Microsoft; it is to apply, at its own scale, the same rigour: an agent has an identity, a scope, a log. The rest is only a matter of method. It is precisely the move from the Artisan level (isolated uses) to the Orchestre level (a coordinated team), then Architecte (processes redesigned around agents) of the MATIA Method™.

The French context makes this milestone all the more accessible in that it is still early: 26% of French micro-businesses and SMEs declared they used at least one AI tool in 2025 (France Num) [12]. The window is not closing; it is wide open for those who methodically structure their trajectory rather than piling up tools. And employment is not disappearing in silence: LinkedIn counts 1.3 million AI-related opportunities created in two years, in jobs that did not exist five years ago [1] [13]. Work does not evaporate. It moves up a notch.

Three questions to ask this very week

To turn this observation into action, three questions (to put to your AI champion, your IT department or your provider) are enough to reveal your real maturity, with no tool and no budget:

1. Which processes have we genuinely entrusted to an agent that works in deferred time, and who, by name, reviews the result before it produces an effect? If the answer is “no one in particular”, you have a team without a manager.

2. Before which action must an agent absolutely stop for human validation? If the list does not exist, your supervision arrives after the harm, not before.

3. What are we doing so that our teams keep the skill to judge what the agents produce? If the answer is “nothing”, you are preparing a silent deskilling.

Conclusion: to lead, rather than to execute

The industry is turning a page without always noticing. The word “productivity” will survive, as “prompt” still survives. But the practice has already tipped over: in the age of asynchronous agents, you no longer do the work, you direct it. It is a promotion offered to everyone (from the intern to the business leader) and it is, at the same time, a demanding job that must be learnt: writing mandates, reviewing what matters, keeping a hand on judgement, organising trust.

The question is neither fear nor urgency; it is mastery. The agents provide the tireless arms; they do not provide the manager. That manager, from now on, is you. The only decision that matters this year is to take it on with method rather than to endure it. Everyone becomes a manager. It remains to learn how to be one.

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Going further

• AI Express Audit & Roadmap (MATIA Method™): 60 minutes over video call to place your SME on the maturity scale (Spectateur → Pionnier) and identify the two or three processes to delegate as a priority, at croissance-transitions.fr.
• The 5-level AI maturity scale: understanding the Orchestre level and those that follow, at paulantoinetual.fr/blog.
• Junyr Agents™: delegating operable, supervised and auditable AI agents, on the Junyr ERP (junyr.app).

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Sources: verifiable, June 2026

[1] Microsoft, 2026 Work Trend Index Annual Report, “Agents, human agency, and the opportunity for every organization” (5 May 2026). Survey of 20,000 workers, 10 markets including France; M365 telemetry. https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization

[2] “Effective Strategies for Asynchronous Software Engineering Agents”, arXiv 2603.21489, 2026. https://arxiv.org/pdf/2603.21489

[3] McKinsey, The State of AI, the agentic era (2026), 62% of organisations are experimenting with AI agents, 23% scale them in at least one function. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

[4] Addy Osmani, “The Code Agent Orchestra, what makes multi-agent coding work” (2026). https://addyosmani.com/blog/code-agent-orchestra/

[5] “Self-Manager: Parallel Agent Loop for Long-form Deep Research”, arXiv 2601.17879, 2026. https://arxiv.org/pdf/2601.17879

[6] “2026: The Year Work Goes Asynchronous, and AI Agents Take Over”, The Fast Mode (2026). https://www.thefastmode.com/expert-opinion/47211-2026-the-year-work-goes-asynchronous-and-ai-agents-take-over

[7] METR, “We are Changing our Developer Productivity Experiment Design” (24 February 2026). https://metr.org/blog/2026-02-24-uplift-update/

[8] “Practical challenges of control monitoring in frontier AI deployments”, arXiv 2512.22154, 2026 (NIST framework). https://arxiv.org/pdf/2512.22154

[9] “From de-skilling to up-skilling: How artificial intelligence will augment the modern physician”, PMC (2026). https://pmc.ncbi.nlm.nih.gov/articles/PMC12955832/

[10] Microsoft People Science, “Research drop: Empowering managers for an AI-first future”, study of 1,800 workers. https://techcommunity.microsoft.com/blog/microsoftvivablog/research-drop-empowering-managers-for-an-ai-first-future/4468191

[11] Microsoft Agent 365, agent control platform, GA May 2026; coverage by Usine Digitale. https://www.microsoft.com/fr-fr/microsoft-agent-365 · https://www.usine-digitale.fr/big-tech/microsoft/microsoft-generalise-agent-365-pour-aider-les-entreprises-a-reprendre-le-controle-des-agents-ia-deployes-sans-supervision.5MGNTRBAVNAFXHLTSNZBIUQP7M.html

[12] France Num (2025), ~26% of French micro-businesses and SMEs declare they use AI. https://www.francenum.gouv.fr/

[13] LinkedIn, 2026 Labor Market Report (January 2026), ≥ 1.3 million AI-related opportunities created in two years. https://economicgraph.linkedin.com/content/dam/me/economicgraph/en-us/PDF/linkedIn-labor-market-report-building-a-future-of-work-that-works-jan-2026.pdf

[14] Deloitte Insights, “Agentic AI is scaling faster than guardrails” (2026), only one organisation in five has a mature model for agent governance. https://www.deloitte.com/us/en/insights/topics/emerging-technologies/ai-agents-scaling-faster.html

[15] Gartner, 40% of enterprise applications will embed specialised agents by the end of 2026 (vs < 5% in 2025); by 2029, ≥ 50% of knowledge workers will develop skills to work with, govern or create agents. https://joget.com/ai-agent-adoption-in-2026-what-the-analysts-data-shows/

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Paul-Antoine Tual is an AI Transformation Leader. He runs Croissance et Transitions (SAS) and operates the Junyr™ suite, MATIA Method™ (a 5-level AI maturity methodology), Junyr Agents™ (AI agents for SMEs, junyr.app). He supports the leaders of French mid-caps and SMEs in their AI transformation, 60-minute diagnostic: croissance-transitions.fr.