The pace of Artificial Intelligence’s (AI) evolution is near light-speed. As an observer or casual AI user, it can be hard to keep up with the incredible rate of change as AI technology is developed and businesses rush to implement it.

Over the past couple of years, we’ve become familiar with Generative AI (GenAI), with the likes of ChatGPT, Perplexity and Google Gemini used day-to-day by many. More recently, we’ve seen a new type of AI emerging - Agentic AI. With much of the hype around Agentic AI focussed on how autonomous it is and the ability for the AI to specialise in certain tasks, let's take a look at the differences between Generative AI and Agentic AI.

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What is Generative AI (GenAI)?

Generative Artificial Intelligence (GenAI) is a type of AI that can create new content and ideas, including written word, audio, images and video, as well as draw upon and reuse its known data to solve new problems. GenAI models can also learn new things, like languages, programming languages and complex subject matter.

Some examples of GenAI systems include:

• OpenAI’s ChatGPT
• Anthropic's Claude
• Google Gemini
• DeepSeek
• DALL-E
• Midjourney
• Servicely

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What can Generative AI do?

Think of Generative AI as “the creator”, with the core focus of GenAI being the creation of various forms of content. Some examples of this content creation are:

• Text generation – writing blog posts, social media captions, cover letters for job applications, poems, scripts and many other types of content
• Image generation - create a range of images, from photorealistic images to stylised animations, and even product images and adverts
• Video generation – creating product explainers, animations, greeting messages, and more.
• Audio generation – music, speech, podcasts can all be created. You may have heard some AI generated or seen headlines about Celine Dion warning fans about AI-generated music.
• Code generation – AI can create code in a range of coding languages (including HTML, CSS, JavaScript.

Beyond the creation of new content, GenAI can also draw on its knowledge to carry out other tasks, including:

• Conversational AI - Chatbots and AI virtual assistants use generative AI to provide dynamic, context-aware responses.
• Data analysis and insights – GenAI can explore and analyse complex data sets to uncover trends and patterns
• Code completion and debugging – find and fix issues in your code
• Language Translation
• Brainstorming and idea generation – quickly generate new ideas

Generative AI is ideal for augmenting human creativity and reducing repetitive tasks, but it still requires human intervention to guide its outputs. It can produce remarkable results, but it lacks reasoning and autonomous action.

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What is Agentic AI?

Agentic AI, sometimes referred to as autonomous AI or AI agents, are AI systems that can act autonomously with intent, make decisions and execute tasks to achieve specific goals, with minimal human intervention. Unlike generative AI, which focussed on creation of content, Agentic AI uses deep learning and real-time data processing to make decisions, plan actions and execute them.

Agentic AI has some unique points of difference compared to generative AI. Here are some of the key aspects:

• Autonomy – Agentic AI can operate independently, without requiring constant human input to complete its assigned tasks. Predefined objectives are setup and the AI agent decides on the best way to accomplish these objectives.
• Contextual understanding – contextual understanding enables agentic AI to interpret information based on the surrounding context, instead of just interpreting the information in isolation. This can include the situation, emotional cues and even history with the user or task.
• Decision-making frameworks – Agentic AI combines insights from machine learning, natural language processing and contextual understanding to make decisions. It can operate with a rule-based system, a probability-based model, or a hybrid of the two.

Some examples of Agentic AI tools and platforms include:

• Open AI's ChatGPT-4o
• Google's Project Astra
• Manus AI
• Microsoft Copilot Agents
• UiPath
• Moveworks
• Servicely

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What can Agentic AI do?

In contrast to GenAI being “the creator”, think of Agentic AI as “the doer”. Agentic AI is focused on completing tasks autonomously. Here are a few examples of what Agentic can do:

• Personalised and proactive service management – By interpreting varying contexts and understanding a range of IT service or customer service processes, Agentic AI can be used to proactively and autonomously handle requests and issue resolution. For example, an agentic AI virtual agent could field an end-user’s issue with logging in to a system, and navigate to that system to reset their password, creating and updating a ticket with this information, and resolving the issue.
• Cybersecurity threat detection – Through continuous learning, agentic AI can identify unusual patterns and emerging vulnerabilities, flagging these and deploying countermeasures without human oversight.
• Act as a first-level filter for HR during recruitment – HR teams are inundated with hundreds of applications for roles. Using agentic AI, HR teams can have AI agents assess incoming applications, check for compatibility for the open role, score applicants, and even reach out to applicants to ask any questions, field questions or set up a first-round interview.
• Automate problem identification – As end-users experience IT incidents, Agentic AI can assess these incidents, as well as monitor external systems, to identify any underlying problem that may be causing these incidents. Depending on the severity of the problem, AI agents may resolve this autonomously, or triage it to a human service agent.

Agentic AI is not just an assistant – carrying out delegated tasks – it’s a decision maker, capable of critically assessing data and context, automating workflows and triggering actions based on real-world conditions, without the need for human oversight.

While these example use cases will provide a business with major productivity gains, it's worth noting that this is merely a taste of the power of Agentic AI. AI agents can be trained to do almost anything! And considering how new the technology is, the pace of development and the level of investment into AI, we're yet to see the level of transformation that Agentic AI brings to businesses globally.

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The key differences between Agentic AI and Generative AI

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Agentic AI vs Generative AI: What should your business be using?

In short, Agentic AI and Generative AI each serve a distinct purpose and aren’t a “one or the other” choice. Both Agentic AI and Generative AI can deliver measurable transformational impact on a business’s productivity and operational efficiency.

As many businesses have experienced over the past couple of years, GenAI can be an extremely useful tool in creating a range of content, debugging code and doing research or brainstorming. GenAI speeds up these processes and helps users get answers, content or ideas easier. The caveat is that users will often need to do a great deal of prompt engineering or post-generation to get to the desired result.

Agentic AI has yet to permeate businesses to the same degree as Generative AI, but it is a bright opportunity for businesses that are looking to develop autonomous processes and increase productivity across the business. The ability to deploy specialised AI agents to handle complex workflows and operate proactively can take a lot of operational burden from your existing service teams and enable them to focus on higher-value work, or be looped into the picture when an AI agent needs a human touch.

When it comes to what AI models your business should use, the simple answer is… both. With their different capabilities and use-cases, using Agentic AI in combination with Generative AI will likely yield the best results. For example, you can have an Agentic AI running proactively to identify and diagnose IT problems, and as a part of it’s workflow, it can assign a task to Generative AI to write a knowledge article on the steps to fix the problem to add to your knowledge base.

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Looking to implement Agentic AI into your Service Management?

There’s a lot of noise in the Agentic AI space right now, and it can be hard to know what is actionable and what is hype. Servicely’s enterprise service management platform is built  with AI at the core – giving businesses the ability to leverage the power of AI to accelerate service across the enterprise.

We’re working alongside customers to implement Agentic AI agents into their service management processes, to proactively resolve issues, identify problems autonomously, and handle end-to-end incident resolution for end-users, all with low-to-no human interaction.

If you’re interested in learning how you can implement Agentic AI into your service management – you can book a demo with our team here.

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