From Generative AI to Agentic AI: A Training Roadmap for UK Teams

Key takeaways

• Generative AI creates content such as text, summaries and code. Agentic AI goes further: it plans, uses tools and completes multi-step tasks.
• Most teams get better results by building skills in stages rather than jumping straight to agents.
• Claude is a practical place to start, because staff can use it for real work from the first session.
• Understanding AI’s limits, including hallucinations and bias, should come before automation.
• Security and governance should be added as soon as AI connects to company data or acts on its own.
• JBI Training offers a joined-up pathway, delivered live online, in London or onsite.
  
  Published by Max Dezh - AI Training expert - 7 minutes read. 

Generative AI and agentic AI: what’s the difference?

The terms are often used interchangeably, but they describe different capabilities.

Generative AI produces content from a prompt: a draft email, a summary of a report, a block of code, a set of Excel formulas. You ask, it answers, and you decide what to do with the result.

Agentic AI builds on that. An agent is given a goal, breaks it into steps, uses tools and data sources, and carries out the work with limited human input. It might research a topic, update a system and report back, rather than just suggest what to do.

For a business, this changes the questions to ask. Generative AI raises questions of quality and trust. Agentic AI adds questions of control, permissions and risk.

Stage 1: Build confident everyday users

Before building anything, your people need to be comfortable with the basics: writing clear requests, giving context, and checking what comes back.

The Claude training courses are designed for this:

• Claude Essentials covers effective prompts, summarising documents, drafting emails and privacy settings.
• Claude Skills, Memory and Projects shows regular users how to personalise Claude so it follows their style and format every time.
• Claude in Microsoft Office brings Claude into Excel, Word and PowerPoint for people who spend their day in those apps.

These courses use real tasks: cleaning data, building reports, drafting documents and preparing presentations.

Stage 2: Understand what AI gets wrong

Fluent answers are not always correct ones. Generative AI can invent facts, reflect bias in its training data and sound certain when it shouldn’t. Teams that understand why can build habits for checking outputs before they are shared.

This is the focus of our Generative AI for Data Analysts course, which explains how these tools work behind the prompt and teaches practical ways to test AI-generated analysis against source data. It is particularly useful for analysts, researchers and managers who rely on AI-assisted summaries. See our AI training range for current courses.

Stage 3: Move from assistants to agents

Once your team is confident with generative AI, you can start building systems that act. Our Agentic AI training courses cover both technical and low-code routes:

• Build AI Agents with Python, RAG and MCP teaches the agent loop, retrieval and tool integration.
• Model Control Protocol (MCP) covers the open standard for connecting models to tools and data.
• Agentic AI Coding with Claude Code shows how to work alongside an AI coding agent on real projects.
• Create AI Agents in Copilot Studio gives business and IT teams a low-code way to build agents without deep programming.

Stage 4: Secure and govern what you build

The more an AI system can do, the more carefully it needs to be designed. Agents that can use tools, connect to other systems and act on a user’s behalf create attack paths that conventional applications don’t have.

Our AI Security training courses teach threat modelling for agentic AI, including Rapid Threat Model Prototyping, so teams can identify and address risks before deployment:

• Threat Model for Agentic AI (2 days)
• Threat Modeling Agentic AI Using Rapid Threat Model Prototyping (1 day)
• Secure Web App Development: AI Security, Privacy and Trust (Top-Up Day) (1 day)

For policy and compliance, add our AI Ethics, Governance and the EU AI Act course.

The roadmap at a glance

Stage	Goal	Where to start
1. Everyday use	Confident, consistent use of an AI assistant	Claude training
2. Critical thinking	Spot errors, bias and unsupported claims	Generative AI for Data Analysts
3. Building agents	Create systems that plan and act	Agentic AI training
4. Security and governance	Protect and govern AI systems	AI Security training

You don’t need to take every stage. Many teams start at stage 1 or 2 and add the others as their use of AI grows.

How JBI delivers AI training

Courses run as live online sessions, at our London training centre, or onsite at your organisation. Content can be tailored to your tools, sector and experience level, and we can combine courses into a multi-stage programme for your team. Contact us on 020 8446 7555 or enquiries@jbinternational.co.uk to plan your pathway.

Frequently asked questions

What is the difference between generative AI and agentic AI?
Generative AI creates content in response to prompts. Agentic AI uses generative models to pursue goals, plan steps and use tools to complete tasks with limited human input.

Should we start with Claude or with agentic AI?
Most teams should start with everyday use. People who understand how to prompt, check and refine AI outputs build better agents later.

Do we need to be able to code to learn about agents?
Not always. Technical courses use Python and MCP, but low-code options such as Copilot Studio let business and IT teams build agents without deep programming.

When should we think about AI security?
Early. Basic privacy and data-handling awareness matters from the first day of use, and threat modelling becomes essential once AI connects to company systems or acts on its own.

Can JBI tailor a pathway for our team?
Yes. We can combine courses, adapt examples to your sector and deliver onsite or online.