In several technical sectors, artificial intelligence begins to go beyond human performance. This discreet tilting, still limited to a few targeted use cases, is carried by a new generation of IA agents called verticaldesigned to excel in very specific tasks, with an increasing level of autonomy.
This dynamic could permanently modify the way in which companies perceive expertise and structure their production.
Agents designed to solve a task, not to do everything
In reverse of general models, the vertical agents focus on a single workflow, in a well -defined sector or function. They do not claim to reason like a generalist human, but learn to execute a precise mission as repair network infrastructure,, Conduct a simulation of cybersecurity attack,, Write a legal requestOr DEBOK A PIPELINE OF DATA.
Several companies have already reached a measurable performance threshold ::
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- Xbow claims to have led to an agent capable of surpassing the best priests humans.
- Crossingin the DEVOPS field, deploys an AI assistant specializing in system troubleshooting, with shorter resolution times than those of expert engineers.
- In network cybersecurity, Meter demonstrates that diagnostic tasks can be automated with more constant precision than in manual mode.
In all cases, the model is optimized not for versatility, but for control of a case of close use but criticalwhere repetitiveness, combinatorial complexity or variability can disrupt human performance.
Tailor-made training, with synthetic data and real examples
These vertical agents rarely come from gross foundation models. They are refined from data specific to the domainoften via learning by strengthening on synthetic or historical data, combined with iterative test sessions. This method aims to make the agent not only efficient, but also predictable and controllable.
The objective is double:
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- Reproduce good business reflexes (and avoid hallucinations)
- Integrate language, standards and limits specific to the sector
This specialization is the necessary condition for integration into demanding production environments, such as legal firms, SOCs or regulated environments, where error is expensive.
Industrial implications still underestimated
If the dominant discourse around the AI remains focused on the fundamental models, the rise in power of vertical agents could actually have an impact more direct and faster on the operational structure of companies.
Some anticipated effects:
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- Reduction of tasks with high cognitive intensitybut routine
- Redistribution of skills : fewer general practitioners, more supervisors or IA integrators
- Revision of business tools : SaaS platforms could evolve towards environments piloted by agent
- New performance indicators : efficiency by task, minimum human supervision, impact on global productivity
This evolution is still marginal, but the first valid proofs of concept encourage the seriousness of this trend.
Towards a hybrid model: humans stay in the loop, but backs up
It is not, at this stage, a complete substitution. Vertical agents remain supervised by humans, as for validation, final decision -making, or climbing non -standard cases. But there skill border between the tool and the expert moves.
For companies, this supposes Rethinking human/AI collaboration not as a simple increased assistant, but as an autonomous co-actor in a business process. This dynamic, if it is confirmed, could change not only tools, but also organizational and training models.
The vertical agent, a new efficiency lever in specialized value chains
THE vertical agents illustrate a silent but strategic shift towards an AI which ceases to be generic and experimental to become specialized, operational, and sometimes superior to humans in a limited setting.
If their deployment remains limited, their rise in power could point out a major inflection in the digital transformation of the trades. It is no longer simply a question of integrating AI into the company, but of imagining how certain functions could now be built around her.
