Agentic AIs in energy systems In 2026: what's new?

2025 was certainly the year when Agentic AI took off. Instead of having to prompt systems, artificial intelligence gained agency all by itself, allowing it to do things automatically for businesses on their behalf.

This change is also affecting how companies manage their energy systems. Key developments mean that it's easier for firms to manage their operations, enhance their resilience, and optimise their costs. In this guide, we look at some of the key developments in Agentic AI for energy systems this year.

Rapid market adoption

One of the most exciting developments for agentic AI in 2026 is the rapid adoption of these systems. According to estimates, companies are spending around $897 million per year, with the market expected to explode to more than $15 billion by the mid-2030s.

This is being driven by the need for efficiency and rising renewable energy system integration. Agentic AI has the ability to make on-the-fly decisions based on sensor data input and demand forecast, allowing companies to better manage their energy input.  

Shift to production-scale deployments 

The reason for this rapid growth is down to a shift to production scale deployments. 2026 is the year when Agentic AI will move into real operational environments and be responsible for managing and reporting complex systems to businesses. Earlier Generative AI was only really useful for suggesting and analysing. However, Agentic AI is able to perform autonomously, as long as it has the right guardrails in place.

Core applications for businesses 

The core applications for businesses of the technology are extensive. As long as they work with the right solar energy company, they’re likely to see significant results.

For example, a Gentic system is able to monitor data in real-time for grid optimizations and even self-healing of energy systems. It can orchestrate multiple energy resources, including wind, batteries, and solar, and take corrective action during disruptions or power outages. It's also able to perform well in data center and high end business energy management.

While AI has massive power needs, it's also able to provide enormous efficiencies to businesses that have high energy draws. This means that these systems can optimise consumption for large facilities by demand forecasting and resource orchestration. Companies can benefit from greater stability and greener energy flows than if they relied on standard technologies from 10 years ago.  

Examples and momentum

There are many examples of companies using genetic artificial intelligence systems to manage their energy supplies. For example, Siemens uses its "industrial AI" to manage many parts of its energy system while also using data models to simulate how energy flows and functions in its business.

For companies, the key message is to use AI as a core infrastructure, not just a nice add-on. New agent-based systems are able to control far more of the process and reduce reliance on humans who may make mistakes.

To start this process, you'll need to build the foundations like adding real-time data layers and also multiple agents that can orchestrate energy systems together to achieve complex objectives. AI is power hungry by itself, so if you are using a data centre you'll want to monitor energy consumption and look for efficiencies.