Energy companies are accelerating the use of artificial intelligence to predict demand, manage assets, and reduce operating costs. But a new Secrets de Data article highlighting Caprikorn and Ekwater argues that the industry’s next leap forward will not be another predictive model, but rather “agentic” AI, where software agents stitch together tasks, use tools, and make decisions under supervision.
The change is not just about technology but also about industrial realities, the magazine said. That means who is responsible for the quality of the data, its integration into operational systems, and when automated decision-making moves from recommendations to action.
From predictive models to “agent” AI that coordinates work
Machine learning has established itself in the energy sector through clear use cases such as load forecasting, sensor-based anomaly detection, and maintenance optimization. Data teams typically train models, validate them, and then publish them through APIs or internal tools. The model outputs probabilities or recommendations. It is then up to the organization to turn that output into operational decisions.
The “agent” approach described in the Secrets de Data article follows a different logic. Rather than stopping at predictions, AI starts orchestrating. Agents can plan a sequence of steps, retrieve information from multiple systems, invoke specialized models, trigger workflows, and document their contents. For the energy environment, this could mean moving from dashboards that issue alerts to systems that suggest diagnostics, suggest actions, and create and submit work orders for approval.
One of the biggest changes is how work is divided up. Agent configuration distributes functionality. Investigation agents collect input, analysis agents perform calculations, control agents check constraints, and execution agents interact with business tools. The promised outcome is fewer handoffs, less copy-paste, less retyping, and fewer manual steps between data-driven decision-making and the operating system.
That also raises the stakes. Agents who take action can commit to the company in a way that a simple “inform” model cannot. The publication highlights the idea of a more complete “decision chain” where the output of AI fits into traceable steps. In the energy sector, traceability remains central. You need to be able to adjust set points, prioritize maintenance, or defend flexibility trade-offs, especially when cost, safety, or continuity of supply are at stake.
https://www.europe-infos.fr/actualites/9396/2026-2-ans-apres-leur-arrivee-taxe-fonciere-moins-relevee-que-prevu-ce-que-les-nouveaux-maires-ont-fait-dinattendu/
https://www.europe-infos.fr/actualites/9389/reddit-bloque-25-000-spams-et-2-millions-de-votes-par-jour-lia-devient-son-bouclier/
Maintenance, forecasting and grid flexibility top the list
The most frequently cited use cases in the energy industry remain those that directly change daily operations. Predictive maintenance relies on time-series data from sensors such as vibration, temperature, current, and pressure. Machine learning flags drift, classifies signs of failure, and estimates risk over a specific period of time. The agent layer can extend this by automating investigations such as retrieving maintenance history, checking parts availability, comparing alerts against weather and load conditions, and suggesting a consistent plan of action.
Another pillar is forecasting, such as demand, renewable generation, spot prices, and regional constraints. In power grids, quality predictions determine procurement, balancing, and flexibility activation. The agent system can chain multiple models (weather, solar power output, load) and calculate deviations before recommending actions. Operators see this as a way to bring analytics closer to production with more frequent updates and easier scenario creation.
Flexibility in storage, demand response, asset management, etc. also helps orchestrate agents. Decisions depend on a variety of constraints, including capacity, availability, contractual obligations, grid limitations, and penalties. When a standalone model outputs a score, agents can solve multi-source puzzles by aggregating contract data, checking margins, simulating impacts, and preparing activation proposals. This “preparation” work often consumes a significant amount of staff time, especially when information is scattered across tools.
Still, the publication describes automation as gradual. Sources typically emphasize “human-involved” cycles. That is, agents prepare, humans validate, and execution begins. In the energy sector, attention reflects the critical nature of infrastructure. Incorrect setpoints or incorrect sensor readings can result in incidents, power outages, or immediate market costs.
https://www.europe-infos.fr/actualites/9375/scpi-imarea-pierre-bnpp-reim-acquisitions-ciblees-et-distribution-ce-que-montre-2026/
Industrial data puts governance, quality and real-time integration at the forefront
The move to agent AI will push data governance to the forefront. Agents don’t just query static datasets; they navigate between reference systems, historical records, real-time streams, and unstructured documents. In the energy sector, these sources range from SCADA systems and industrial historians to computerized maintenance management systems, asset databases, contract catalogs, and incident tickets. Without a shared dictionary and quality rules, agents can generate plausible actions based on unstable foundations.
Data quality issues are not theoretical. Sensors drift, equipment configurations change, units of measurement differ, and gaps appear in the time series. The model may be able to absorb defects if they are rare, but initial errors can be amplified as agents chain decisions. As such, the publication points out automatic controls such as consistency checks, outlier detection, and “stopping” mechanisms in case of unreliability.
Integration between IT and OT (operational technology) is also a barrier. Agentic AI means invoking tools, writing to systems, and triggering workflows. However, industrial environments are fragmented and constrained by uptime requirements and strict policies. Connecting agents to maintenance or control systems requires connectors, permissions, audit logging, and strict version control. Teams should also avoid the “black box” effect by documenting which sources were used and according to what rules.
The timing will also change. Machine learning can be run in batches and recalculated overnight. Some use cases derive value from real-time, such as ongoing incidents, traffic jams, or sudden drops in renewable energy output. Agent architectures must manage priorities, decision windows, and maximum delays while maintaining stability and avoiding contradictory actions when data changes rapidly.
Cybersecurity and accountability shape how far autonomy can be achieved
Enabling agents to take immediate action raises cybersecurity concerns. Agents with access to multiple systems become an attack surface through their access keys, connectors, logs, and even their instructions. For energy companies, IT/OT separation, identity management, and monitoring of automated actions are prerequisites. A single overly permissive account can turn your assistant into an entry point.
The publication suggests that controlling behavior requires practical guardrails, such as least privileged access, mandatory verification steps, logging of all actions, preserving evidence of decisions, and thresholds beyond which the agent must stop. The subtext is an “operational” agent whose autonomy is proportional to risk. In the case of energy, the balance depends on the asset. Agents may be more independent for maintenance analysis than for changing control room setpoints.
Accountability is the other side of the equation. When recommendations become action, organizations need to be clear about who approves, who oversees, and who can take over. Companies are establishing AI governance committees, escalation procedures, and compliance frameworks. Internal requirements may align with broader principles such as explainability, prevention of setbacks, incident management and auditing, in addition to sector-specific obligations.
In the field, acceptance depends on transparency. Operators and engineers want to know on what data and with what confidence actions are being proposed. Agents are useful for creating structured reports, including sources of information referenced, assumptions, applied constraints, and rejected alternatives. The ability of actions to “tell a story” is presented as the key to moving from a data tool to an operational tool.
https://www.europe-infos.fr/actualites/9351/jeux-video-et-robots-en-2026-comment-les-mondes-virtuels-servent-lentrainement-reel/
