Agentic AI Explained
For the last 2 years, it seems like all everyone is talking about is
For the last 2 years, it seems like all everyone is talking about is
Agentic AI is used to describe an AI that isn’t just able to react and respond to prompts, but can actually proactively work on its own. It’s able to work completely autonomously, without needing much human intervention.
With ‘agentic’ referring to the AI having ‘agency’, the AI is able to act, work and improve independently, without needing constant prompting. Because of this, it behaves more like an independent digital agent than a generative AI like (LLM), the AI then evaluates the information and forms a plan to help it achieve the goal. This basically includes breaking it down into small, manageable tasks.
Act: It then goes ahead and completes the tasks autonomously, which can involve anything from updating documents and software to executing code.
Learn: The AI looks at the output and evaluates it, working out how to improve.
This loop is then repeated across multiple platforms and tasks.
Agentic AI, whilst sounding like something straight out of a sci-fi movie, actually already exists in many sectors. Some examples include:
Customer services: Where agentic AI agents can handle conversations, bookings and organising services where needed.
Cybersecurity: AI agents constantly monitor for network vulnerabilities, triggering responses when needed.
It’s able to work completely autonomously, without needing much human intervention.
Finance: Trading agentic AI is able to analyse financial data and manage risk throughout the day.
Healthcare: Agentic AI agents are able to schedule treatments, take notes and provide doctors with useful insights.
Supply chain: AI agents can forecast demand, track shipments and manage inventory 24/7.
Like all AI, agentic AI has its drawbacks too. Having an AI that is completely autonomous can be great for business, but also comes with a lot of security and safety concerns.
For example, hallucinations can occur, which, if left untapped, can have seriously negative effects on a business.
Agentic AI also needs a lot of testing. In many businesses that are more complicated, training the AI might be more expensive and time-intensive than is worth it.
Agentic AI also comes with some security risks, as the agent moves independently. These can be hard to keep up with, raising important questions about data safety and security.
Agentic AI vs Generative AI: What’s The Difference?
So you might be reading this and thinking: what is the difference between agentic AI and generative AI? The truth is the two are regularly grouped together, but actually serve very different purposes.
Generative AI focuses on creating content based on a prompt. That might be text, images, code or videos.
Agentic AI on the other hand goes a step further by using that prompt to take action without needing much more human intervention. Generative AI might be able to write an email, but agentic AI is able to track opens, adjust the tone and repeat the process itself.
In that way, where generative AI is more like an assistant, agentic AI is like a whole new colleague.
The rise in agentic AI is definitely exciting. AI no longer just needs human prompts to work, but is able to process, adapt and work autonomously.
For startups, it opens up a lot of possibilities. AI no longer just helps do the work, but is actually able to do it itself.
So, the world has gone from generative AI to agentic AI. Now, it will be interesting to see what comes next.
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Based on reporting by techround.co.uk.