What is agentive AI? A paradigm shift.
For thirty years, automation has performed predefined tasks: a scenario, a trigger, an action. With generative AI, the user remains the conductor who asks a question, gets an answer, and decides on the action. Today, agentics reverses this relationship: agents reason, decide, and act autonomously on an entire process, with a human checkpoint positioned where it adds the most value.
This rupture necessitates rethinking the processes and business actions themselves. The central focus shifts from technological choice to the redesign of the business process itself, and the accompanying organization of work.
Single-agent or multi-agent?
A single, well-defined agent is often enough for a first, limited use case. But an isolated agent quickly reaches its limits. Overloaded with tools, it gets lost in selecting the right action, its limited context reduces reliability as the history lengthens, and the multiplication of task types dilutes its relevance.
The market response is multi-agent: several specialized agents (analysis, writing, verification, action) cooperate under the supervision of an orchestrator, who only escalates to humans when necessary. It becomes relevant when the complexity of the process exceeds what a generalist agent can reliably handle.
Why 2026 is a tipping point
Three pieces of data highlight the magnitude of the change:
- The duration of tasks that an AI can reliably complete double every four months since 2024 (McKinsey / METR, 2025)
- 2 to 5 people can supervise an «agent factory» of 50 to 100 specialized agents McKinsey, The Agentic Organization, 2025)
- But More than 40 % agency projects will be discontinued by 2027, due to a lack of clear business value and adequate governance (Gartner)
This last figure is worth remembering for any decision-maker. Between a project that lives up to its promises and one that is stopped after the pilot, deployment discipline carries more weight than the technology itself.
Five beliefs for success
- An agency project is about operational excellence, not IT. Decisions are business-related before they are technical.
- Automation can cause disorganization : work and teams are as important as the target process, and governance is considered from the design phase
- Foundations matter more than tools The «tool» layer is fluid, while the data and architecture are not.
- Not everything is agentic Traditional AI, classic automation, and agent-based systems are complementary levers.
- Discipline must be as strong on the business side as on the technical side. governed roadmap, demanding prioritization, change management from the design phase
A deployment that lasts also follows a precise order: business strategy, priority businesses with demonstrated ROI, governed roadmap, process and organization redesign, stack selection, and finally, agent build. All supported by data, security, and compliance foundations laid from the start, not added as an afterthought.
Govern from the framing, or fail
An IT project, on average 24 % delay and 27 % budget overrun, one in six goes over budget more than 200 % (Flyvbjerg & Budzier, Cornell University), et seven out of ten IT projects do not reach their initial objectives (Standish Group, Chaos Report 2024Agent-based systems are no exception to this reality if they are approached as a classic technological project. Furthermore, the risk is higher, because an agent in production with access to systems and persistent memory can produce an incorrect action on a large...
That's why three families of actors need to be involved from the initial framing: the Business teams (functional scope and level of autonomy of each agent), the IT and security teams (dedicated identity, granular permissions, immediate kill switch) and the compliance / DPO (classification under the AI Act, register of deployed agents).
We understand that an ungoverned agentic system is one that will be shut down at the first incident.
Measurable results
| Domain | Use cases | Results |
| Finance | Automated committee notes | Reduced production time 50 % / improved standardization |
| RH | Recruitment by specialized agencies | Reduced by several weeks |
| Assurance | Self-managed processing of simple claims | Turnaround time from several days to under 48 hours / Human supervision for high-risk cases |
| IT | End-to-End Test Writing and Execution | Recipe cycle divided by 4 to 8, without regression |
In any case, humans focus on arbitration, more than on repetitive execution. Business teams see their value shifting towards arbitration, relationships, and oversight.
How to approach one's own journey
Whatever the stage of maturity – Explorer (test impact without production constraints), initializer (a first agent under real-world conditions, within a controlled perimeter), Transformer (full trajectory with scaled governance) or industrialize (Accelerating and structuring a Center of Excellence) – it all begins with a diagnostic of organizational and technological maturity, followed by the identification of a priority scope with high ROI potential.
First, it is necessary to define which process deserves to be rethought, and with what level of delegation to AI one is willing to live. The choice of the agent will come next.
Silamir supports its clients from all sectors throughout this entire journey, from maturity assessment to industrialization. To objectify the agency potential of your processes, schedule a meeting with our teams..
