Agentic AI is the hottest topic in enterprise IT right now. Many companies have recently launched agentic AI offerings and talked about the potentially huge benefits this technology can deliver for customers, including powerful AI capabilities from ServiceNow.
Why all the excitement? Simple: because agentic AI represents the next leap forward in artificial intelligence, moving significantly beyond the capabilities of generative AI (GenAI) systems like ChatGPT and Claude.
While GenAI generates content based on a user’s input (and is a key element of agentic AI), agentic AI actively performs tasks on a user’s behalf. These AI Agents analyse information, make decisions, and execute actions, offering businesses a powerful way to streamline operations and enhance efficiency.
Agentic AI vs GenAI
A helpful way to differentiate between agentic AI and generative AI is through the example of an email:
- Generative AI: GenAI can draft an email for you based on your input, but you still need to review and edit it, attach files, and send it manually.
- Agentic AI: AI Agents, on the other hand, can go further by autonomously analysing the context, composing the email, attaching the right files, selecting recipients, and sending it without requiring human intervention.
GenAI draws on its training on large models of text-based information, enabling it to understand what someone’s saying, and summarise it in an easily digestible way and even give you an appropriate answer.
But agentic AI takes that same information, understands it, and then recognises what actions to take next. In essence, it has the capability to do work for you. That can help reduce the workload on a human employee, freeing them up to focus on other tasks.
How is agentic AI different from complex workflows?
Many businesses already have complex, established workflows that don’t rely on AI. It’s likely they’ve built these flows over years and have thought carefully about how to make them both efficient and secure.
So how does agentic AI help these businesses?
The key benefit is that current flows work really well with structured requests, where a user asks for defined information or wants to perform a defined task. This kind of situation is perfect for automation through existing digital tools.
However, agentic AI shines at handling unstructured requests – for instance, when someone doesn’t know exactly what they’re looking for, or uses natural language in an email or chatbot message.
More broadly, we see four big benefits of agentic AI compared to existing complex workflows:
- Adaptability: Unlike rigid workflows that follow pre-defined steps, AI Agents can handle the more unpredictable ways that humans communicate in real life and adapt dynamically.
- Autonomy: AI Agents can take action with very minimal human intervention, making decisions based on real-time information (for instance, outside of normal office hours).
- Problem-solving: Workflows execute pre-defined actions, while AI Agents analyse situations and determine the best course of action to get an issue resolved.
- Continuous improvement: Workflows typically require manual updates, but AI Agents can learn and optimise themselves over time.
Take the example of an IT help desk. A structured workflow would require users to fill out a form for a service request. If their problem isn’t covered in one of its options, there will probably be a box that says, “I can’t find what I’m looking for”, asking them to type out the details in natural language. This text is what then gets sent to a human agent on the IT service desk to resolve.
In a world of agentic AI, however, that same natural language text is what the AI Agent can understand and act upon. It can even contact the person to ask them for more information if needed. Because AI Agents can adapt to unstructured interactions like this, they can remove many of the burdensome requests that currently clog up service desks’ inboxes.
How do AI Agents ‘learn’?
Importantly, AI Agents aren’t static. They improve their decision-making abilities through two key learning mechanisms:
- Reinforcement learning through human feedback: Users can review AI outputs and provide positive or negative feedback, helping AI Agents to refine their accuracy levels.
- LLM contextual understanding: The large language model (LLM) that sits behind the AI Agent is constantly training itself on the huge amounts of structured and unstructured data fed into it.
This ongoing process of ingesting more data and receiving human feedback creates a positive loop that means the AI gets more accurate all the time.
For example, if an AI Agent handles customer service queries, it can learn from previous resolutions and analyse patterns in common issues. Over time, it becomes more efficient, reducing the human workload.
Could AI Agents help you?
There’s no question that agentic AI is a transformative upgrade from generative AI and traditional workflows.
AI Agents can offer businesses a way to automate tasks intelligently. These AI Agents reduce manual intervention, increase efficiency, and operate 24/7, making them ideal for any industry, from IT support to customer service and beyond.
If your organisation wants to leverage the agentic AI capabilities of ServiceNow, UP3 can help. We bring deep expertise in the ServiceNow platform, and a track record of deploying AI-driven solutions in public and private sector organisations. If you want to learn more about how we can help you capitalise on agentic AI to unlock your organisation’s potential, check out our ServiceNow and AI page.
