Training course · AI Agents · Energy & Utilities

AI Agents for the Energy Sector

Agentic AI for business teams: understand AI agents, automate workflows safely and keep control with human-in-the-loop and governance.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

This course connects AI Agents with the everyday processes of Energy & Utilities. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

AI agents go a step beyond conventional chat assistants: they plan sub-tasks, use tools such as search, files or business applications and complete multi-step work largely on their own. Over two days you will learn how agents are built, how they differ from chatbots and traditional automation, and which tasks genuinely justify their use. Widely used platforms serve as practical examples, including Microsoft Copilot Studio, custom GPTs and Claude with projects and tool connections.

In the hands-on sessions you design agent workflows for tasks from your own area of work, from triaging requests through research and drafting to handing results over to business systems. You decide where a person reviews and approves, which permissions an agent receives and how errors are detected. A dedicated block covers governance, data protection and accountability, so that your agents not only work but remain controllable, traceable and defensible within your organisation.

The energy sector is undergoing a deep transformation: distributed generation, grid expansion, sector coupling, new market roles and dense regulation are reshaping municipal utilities, grid operators, suppliers and energy service providers alike. Generative AI helps above all in managing complexity. It summarises regulatory decisions and legislative changes, answers customer enquiries about billing, tariffs or grid connections efficiently, unlocks technical documentation and prepares project documents for permits. AI agents can support market communication processes, pre-sort fault reports or automate reporting. Because critical infrastructure is involved, requirements for IT security, data quality and traceability are especially high.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Staff at municipal utilities and energy suppliers
  • Grid operators in planning, connections and operations
  • Customer service and billing teams
  • Regulatory affairs and energy trading
  • Renewable energy project developers

Typical challenges we address

  • Dense regulation that changes frequently
  • Growing numbers of customer enquiries on tariffs, bills and grid connections
  • Extensive technical and permitting documentation
  • Skills shortages alongside rising investment volumes

What you will be able to do

  • You can explain how AI agents are structured and clearly distinguish them from chatbots and classic automation.
  • You can select a suitable agent platform for your requirements using transparent criteria.
  • You can design an agent workflow for a real process in your area and build a working prototype.
  • You can define human-in-the-loop checkpoints, test cases and escalation rules.
  • Navigate regulatory requirements and technical documents faster and more reliably with AI.
  • Relieve customer service and grid connection processes through AI-supported pre-checks and draft replies.
  • Assess AI use cases with due regard to IT security and critical infrastructure requirements.

Course outline

Day 1

Module 1 What makes an AI agent
  • Agent building blocks: language model, instructions, knowledge, tools and memory
  • How agents differ from chatbots, macros and classic workflow automation
  • Levels of autonomy: from suggestion-making assistants to agents that act independently
  • Realistically judging which tasks suit agents and which do not
Module 2 Platform overview: Copilot Studio, GPTs and Claude
  • Microsoft Copilot Studio: agents grounded in company data and connected to Microsoft 365
  • Custom GPTs: configuring instructions, knowledge files and actions
  • Claude with projects, knowledge bases and tool access via the Model Context Protocol
  • Selection criteria: data access, integrations, administration and licensing
Module 3 Designing and building agent workflows
  • Breaking tasks into steps and defining hand-offs between agent and human
  • Writing system instructions, examples and knowledge sources for consistent output
  • Connecting tools: search, documents, email, spreadsheets and business applications
  • Building a prototype agent for a real process from your own area
Module 4 Human-in-the-loop, quality and error handling
  • Setting approval steps, escalation rules and thresholds for human review
  • Test cases, evaluation criteria and spot checks for quality assurance
  • Common failure modes: wrong tool choice, endless loops and fabricated facts
  • Using structured user feedback to improve the agent over time

Day 2

Module 5 Governance, security and roll-out
  • Roles and responsibilities across business units, IT, data protection and works council
  • Least-privilege permissions, logging and protection against prompt injection
  • Positioning under the EU AI Act and GDPR: transparency, documentation and data flows
  • From pilot agent to production: operation, maintenance and measuring success
Module 6 Practice lab: Energy & Utilities
  • Making regulation understandable
  • Relieving customer service
  • Pre-checking connection requests
  • Unlocking technical documentation
  • Permitting and project documents
  • Reporting and market analysis
Module 7 Data protection, quality & transfer to daily work
  • As operators of critical infrastructure, utilities and grid operators face heightened IT security requirements, for example under NIS2 and sector-specific security catalogues; grid and asset data must not flow uncontrolled into external AI services. Consumption and metering data is personal data under the GDPR and metering regulations, and unbundling rules must also be observed.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Making regulation understandable

Regulatory decisions, legislative changes and industry guidelines are summarised, their impact on internal processes derived and prepared as task lists for departments – reviewed by regulatory affairs.

Relieving customer service

Enquiries about instalments, annual bills, tariff changes or registering wallboxes and PV systems are classified, draft replies created and missing documents requested specifically. Recurring issues feed into FAQs and self-service content.

Pre-checking connection requests

Incoming applications are checked for completeness, technical details extracted and passed to grid planning in a structured format, with follow-up questions to installers drafted. Processing times shrink noticeably.

Unlocking technical documentation

Operating manuals, maintenance logs and excerpts from standards are made searchable so teams quickly get answers to technical questions, including a reference to the source.

Permitting and project documents

For wind, solar or grid projects, application documents are structured, comments from authorities and the public analysed and project reports prepared for boards. Deadlines and conditions are tracked clearly.

Reporting and market analysis

AI helps write commentary on sales, generation and grid KPIs, summarises market reports and drafts mandatory disclosures and internal reports. Figures always come from verified source systems, never from the AI.

Prerequisites

You should already use an AI assistant such as ChatGPT, Copilot or Claude on a regular basis. No programming skills are required. Professional experience in an energy sector company; no programming skills required.

Technical requirements

You will need a laptop with an up-to-date browser. Ideally you have access to at least one agent platform such as Copilot Studio, ChatGPT with GPTs or Claude; practice accounts can be provided if needed.

Data protection & compliance

As operators of critical infrastructure, utilities and grid operators face heightened IT security requirements, for example under NIS2 and sector-specific security catalogues; grid and asset data must not flow uncontrolled into external AI services. Consumption and metering data is personal data under the GDPR and metering regulations, and unbundling rules must also be observed.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Wed, 9 December 2026 – Thu, 10 December 2026 Berlin Request
  • Mon, 14 December 2026 – Tue, 15 December 2026 Online Request
  • Wed, 13 January 2027 – Thu, 14 January 2027 Stuttgart Request
  • Thu, 21 January 2027 – Fri, 22 January 2027 Online Request
  • Wed, 3 February 2027 – Thu, 4 February 2027 Hanover Request

Frequently asked questions

Do I need programming skills to build AI agents?

No. The platforms used on the course rely on visual interfaces, plain-language instructions and ready-made connectors. You learn to specify agents precisely from a business perspective, connect tools and test workflows. Where deeper technical integration would be needed, you will know which requirements to put to your IT team.

Which platform will we use during the course?

The course is vendor-neutral and uses Microsoft Copilot Studio, custom GPTs and Claude as examples. You build your prototype on the platform that is available or planned in your organisation. The principles of workflow design, control and governance you learn carry over to other agent solutions as well.

How do I stop an agent from acting without control?

Through deliberate design: agents receive only the minimum permissions they need, critical actions such as sending, booking or deleting require human approval, and every step is logged. In addition, you define test cases and escalation rules. These are exactly the control mechanisms you develop for your own use case during the course.

May a grid operator use cloud AI services?

In principle yes, but only for suitable data and after approval by IT security and data protection. Operational and grid data from critical assets does not belong in external services. In the course we carefully distinguish non-critical use cases such as text work from sensitive areas that require special safeguards.

Can AI reliably interpret regulatory changes?

AI summarises regulatory decisions and legislation well and helps identify affected processes. Binding interpretation, however, remains the job of regulatory affairs or the legal department. The course shows how to back up AI summaries with source references and avoid misinterpretation.

How much does “AI Agents for the Energy Sector” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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