Connecting new energy projects to the grid takes more than available capacity. It requires engineering studies that assess how connections will affect the network. On September 17, 2026, Amazon Web Services announced Agentic Grid Planning on AWS, a program designed to help utilities accelerate those studies using specialized AI agents managed by AWS.
The announcement includes a concrete result: collaborating utility Duke Energy has reduced data preparation tasks from two weeks of manual effort to hours. That improvement applies to data preparation, rather than the entire interconnection process, but it illustrates where automation can free engineers to concentrate on complex planning decisions.
The need is substantial. Lawrence Berkeley National Laboratory reports that 2,061 gigawatts of generation and storage capacity were seeking US transmission interconnection at the end of 2025. Although the backlog declined from 2024, connection timelines remain lengthy. For projects reaching commercial operation in 2025, the median time from an interconnection request exceeded five years in regions with available data.
AWS agents coordinate work through the simulation software, grid models, scripts, and standards utilities already use. They support preparing study cases, running power flow and contingency analyses, and repeating studies when inputs change. The simulation software performs the underlying deterministic calculations, while engineers review recommendations and make final decisions.
The program also records workflow steps and versions study artifacts. This creates a traceable basis for reviewing simulation evidence, agent reasoning, and engineering decisions. Engineers can approve reusable procedures, helping teams preserve practical knowledge and apply established methods across subsequent studies.
Agentic Grid Planning on AWS is available to qualified utilities and grid operators, with AWS Professional Services available for integration support. Utilities interested in the program can contact their AWS account team.
For utility leaders, the potential business value extends beyond reducing repetitive work. We assess that stronger preparation workflows could give planners more time to compare options and investigate constraints. However, faster studies represent one part of a broader connection journey that also depends on project development and infrastructure delivery.
A practical starting point is to identify a recurring planning bottleneck and establish a baseline. Measure preparation time, rework, review effort, and the completeness of supporting records. A focused pilot can then demonstrate whether automation improves throughput while maintaining engineering oversight. This approach gives decision makers evidence for expanding adoption. Include experienced planners in the evaluation so the pilot reflects actual study requirements, review practices, and the exceptions teams encounter in daily work.
Prolifics brings complementary capabilities in AWS cloud engineering, AI, data platforms, and governance. Its energy and utilities services also cover IT and operational technology integration, analytics, and infrastructure modernization. Together, these capabilities provide a relevant foundation for organizations assessing how to prepare their systems and data for AI adoption.
Ready to explore where AI could reduce manual effort across your utility operations? Connect with Prolifics to discuss your data readiness, integration requirements, and a focused modernization roadmap. Start with one measurable use case and define what success should look like for your engineering teams.
Media Contact: Chithra Sivaramakrishnan | +1(646) 362-3877 | chithra.sivaramakrishnan@prolifics.com
Frequently asked questions
1. What is Agentic Grid Planning on AWS?
It is an AWS program that provides managed AI agents to help utilities execute interconnection study workflows, including preparing cases, coordinating analyses, and repeating studies.
2. Why do interconnection studies matter?
They assess the impact of connecting proposed projects to the transmission grid and identify equipment or upgrades needed before connection, including associated costs.
3. Do AI agents replace power systems engineers?
No. Engineers direct the work, review recommendations, and make final engineering decisions. Existing simulation software performs the technical calculations while agents coordinate workflow tasks.
4. What improvement has Duke Energy reported?
According to AWS, Duke Energy reduced data preparation from two weeks of manual work to hours. The announcement does not present that result as the duration of a complete interconnection study.
5. How can Prolifics support utilities exploring AI?
Prolifics offers AWS engineering, AI and data services, governance, and integration capabilities that can help utilities assess readiness and plan modernization. Contact Prolifics to discuss a suitable starting point.
