The technological trends of 2026 are not just a list of keywords. Behind each announcement, gaps widen between what analyst firms highlight and the real constraints that companies face when deploying these technologies. Comparing the priorities identified by Gartner, Forrester, or Capgemini allows us to measure where analyses converge and, more importantly, where they diverge.
Convergences and Divergences in the 2026 Tech Reports
| Theme | Gartner | Forrester | Capgemini |
|---|---|---|---|
| Agentic and Multi-Agent AI | Yes (AI orchestration) | Yes (agentic commerce, multi-agent systems) | Yes (intelligent operations) |
| AI Security and Governance | Yes (reliability, governance) | Yes (AI Security and Trust) | Not prioritized |
| Energy Constraints of Data Centers | No | No | No |
| Hybrid/Sovereign Cloud | Yes (AI platforms) | No | Yes (Cloud 3.0) |
| Physical Robotics and Embedded AI | No | Yes (Physical AI & Robotics) | No |
The consensus is clear on one point: agentic AI spans all analytical frameworks. All three firms place multi-agent systems among the structuring trends. The most striking divergence concerns energy. None of the major trend reports treat electrical constraints as a standalone technological issue, while the International Energy Agency (IEA) reports a significant increase in the consumption of AI-specialized data centers in 2025.
The articles published in the tech category of Only Internet allow for tracking these developments over time, with coverage that goes beyond the announcements of major firms.
Agentic AI and Autonomous Software: What the Reports Really Measure
Forrester distinguishes several layers in the rise of agentic AI. Agentic commerce describes agents capable of placing orders or negotiating prices without human intervention. Agentic software development repositions developers: they oversee code generated by agents rather than writing it line by line.
Gartner approaches the same phenomenon from the perspective of orchestration, separating AI infrastructure platforms from application layers that coordinate multiple agents. This distinction has a direct consequence for CIOs: the choice of infrastructure conditions the ability to orchestrate agents.
The difference in vocabulary between these two firms masks a fundamental agreement. Both describe a shift where AI no longer responds to isolated requests but chains actions in a workflow. The focus is no longer on model performance, but on the reliability of the execution chain.
Identified Risks in Multi-Agent Systems
Forrester dedicates an entire category (AI Security and Trust) to agent security. The central issue: an agent interacting with other agents can propagate an error or manipulation throughout a decision chain. Gartner discusses governance and reliability, but Capgemini does not position this risk among its stated priorities.
The security of AI agents becomes an architectural issue, not just a compliance one. Companies deploying multi-agent systems without a dedicated control layer expose themselves to cascading errors that are difficult to trace.
Energy Constraints of Data Centers: The Missing Angle in Trend Reports
According to the IEA, the consumption of AI data centers significantly increased in 2025, even though efficiency per task improved. Reasoning, video generation, and multi-agent orchestration consume much more energy than simple text queries.
This data puts the promises of efficiency into perspective. Each unit gain is absorbed by the multiplication of uses. And the constraint is not limited to the electricity bill: the availability of the electrical grid now conditions the establishment of data centers. Connection to the grid, not fiber optics, becomes the limiting factor in deciding where to build AI infrastructure.
None of the three analyzed firms treat this issue as a technological trend. However, it appears in the operational decisions of hyperscalers and cloud operators.
European Regulatory Timeline: What Changes in 2026
Two European texts modify the framework in which these technologies are deployed:
- The AI Act enters a phase of gradual implementation. The European Parliament has updated its timeline via the Digital Omnibus, making it necessary to distinguish between rules that are already applicable and deferred deadlines. Companies using multi-agent systems must check which risk category their uses fall into.
- The Cyber Resilience Act imposes a reporting obligation for vulnerabilities in connected products. This requirement directly affects IoT devices, embedded software, and cloud solutions that integrate AI components.
- The Data Act regulates the sharing of data from connected products. It creates new obligations for manufacturers and digital service providers that exploit this data.
These three texts overlap. A company deploying a multi-agent system on IoT data in Europe must simultaneously comply with the AI Act, the Cyber Resilience Act, and the Data Act. The compliance burden is increasing faster than the maturity of governance tools.
Post-Quantum Cybersecurity and Sovereign Cloud
The post-quantum challenge remains prospective, but companies handling sensitive data are beginning to migrate to resistant encryption algorithms. The sovereign cloud responds to a growing demand for data localization and control that is intensifying with the European regulatory framework.
The convergence of security, sovereignty, and compliance creates a field of constraints that trend reports treat separately. In practice, these three dimensions are managed together, often by the same team.
The technological trends of 2026 are better understood by cross-referencing analytical frameworks than by following a single report. Agentic AI is a consensus, energy constraints remain under-documented, and the European regulatory framework adds a layer of complexity that trend lists do not capture. The true indicator of maturity is not the adoption of a technology, but the ability to manage its side effects.
