Agentic AI, explained

Agentic AI is essentially a smart type of AI that’s designed to take initiative and act independently, making decisions on its own.

Think of it this way: unlike regular AI that simply follows instructions or crunches data, agentic AI can actually set its own goals and figure out how to achieve them. It’s all about giving AI a sense of purpose and the freedom to pursue it with minimal hand-holding from us.

It’s quite different from the usual AI we’re familiar with, which needs us to constantly tell it what to do or relies on very strict sets of rules. Agentic AI is more self-reliant; it can adapt to situations in real-time and make choices based on what’s happening around it and what it’s trying to accomplish.

How agentic AI works

Agentic AI’s magic comes from blending advanced machine learning, smart decision-making processes, and a system of learning from its own experiences.

Imagine it like a robot that not only performs tasks but also learns from every step it takes and uses those lessons to guide its future actions. It generally operates through a few key steps:

• Goal-setting: Agentic AI starts by figuring out what it needs to achieve. This could be based on its initial programming or by understanding new information from its environment. For example, it might aim to streamline a supply chain or boost how users interact with a website.
• Decision-making: Next, it dives into available data and uses clever algorithms to determine the best way to reach its goal. It’s like choosing the smartest route to a destination using all the information at hand.
• Learning and adapting: Just like any AI, agentic AI is a student of its own journey. It learns from both the good and bad outcomes of its actions. It’s constantly tweaking its strategies and refining how it makes decisions to get better over time.

The real game-changer here is its ability to chart its own course. Agentic AI can react to live information and act with greater independence compared to traditional AI systems. So, naturally, this raises the question: what’s all the buzz about agentic AI?

Agentic AI benefits

Agentic AI is a powerhouse for boosting efficiency, cutting down on mistakes, and effortlessly scaling operations, making it perfect for industries that need constant improvement.

• Increased efficiency: Because agentic AI doesn’t need constant direction, it can work tirelessly, 24/7, continuously learning and adapting as new data comes in.
• Reduced human error: Agentic AI makes decisions based on facts and algorithms, which means it’s less likely to be swayed by human biases or make judgment errors.
• Scalability: Agentic AI can handle massive datasets and complex tasks across different industries and grow its decision-making capabilities to match the expansion of a business.

These advantages make agentic AI incredibly attractive for sectors like logistics, healthcare, finance, and customer service, where staying ahead means constantly finding ways to improve and optimize.

Agentic AI applications

Agentic AI is revolutionizing industries like healthcare, supply chains, finance, and customer service by making smart, independent, goal-driven decisions.

• Healthcare: In medical research, agentic AI can independently explore patient records, suggest treatment options, and even uncover promising paths for new medications.
• Supply chain optimization: Imagine AI systems that can set their own targets, optimize delivery routes, and manage inventory levels without direct human commands. They’re already making global supply chains much more efficient.
• Finance: Agentic AI powers algorithmic trading by setting financial objectives and making split-second decisions based on market data to reach those targets.
• Customer service: Forget basic chatbots – agentic AI is enabling virtual assistants to truly solve customer problems and personalize interactions, making judgment calls without needing to ask for permission every step of the way.

Crypto-specific examples of agentic AI applications

• Crypto trading and DeFi: Agentic AI can automatically analyze crypto market trends, adjust trading strategies on the fly, and optimize how you earn yield in DeFi, all without human intervention.
• Fraud detection and compliance: Agentic AI can act as a digital detective, tracing suspicious crypto transactions, spotting potential money laundering, and ensuring compliance with regulations on the blockchain.
• Smart contract security: It can become a vigilant guardian for smart contracts, identifying weaknesses, auditing code, and proactively preventing exploits by spotting unusual activity in real-time.
• NFT and metaverse asset management: For NFTs and virtual world assets, which require careful valuation and management, agentic AI can predict market trends, estimate asset values, and recommend the best times to buy or sell your digital collections.

Agentic AI vs. autonomous AI

While both are independent, agentic AI takes the lead by setting and changing its own goals, whereas autonomous AI operates within boundaries set by us. These terms often get mixed up, but there’s a real difference.

Autonomous AI is about AI systems performing tasks without us constantly stepping in, but typically within a pre-set game plan or objective we’ve defined. Think of a self-driving car – it can navigate on its own, but the rules of the road and the destination are still programmed in.

Agentic AI goes further. It’s not just about operating independently; it’s about having the smarts to set and adjust its own goals as it learns more about its environment. If autonomous AI is like a car following a GPS, agentic AI is like a car that can decide the best route to take and even change its destination based on live traffic updates or new information it picks up.

Here’s a quick summary of the key distinctions between agentic AI and autonomous AI:

Agentic AI vs. AI agents vs. generative AI: Which one is more powerful?

To put it simply, generative AI creates content from prompts, AI agents carry out tasks on command, and agentic AI is the self-starter – it sets its own course, makes decisions, and adapts based on real-world outcomes.

Generative AI is all about creation – whether it’s writing text, crafting images, composing music, or even producing videos. It’s not designed to make choices or come up with goals; it’s purely focused on generating content based on what you feed it.

ChatGPT is a perfect example of generative AI. Ask it to write an article, a poem, or some code, and it will amazingly generate it. However, it won’t decide on its own what to write about or what topics are important.

On the other hand, we have AI agents, which are built to accomplish specific tasks based on our instructions. Unlike generative AI that produces creative content, AI agents are action-oriented – they’re about getting things done, like finding information, automating processes, and handling your requests.

Think of Siri or Alexa as AI agents. Tell Alexa to set an alarm, play music, or switch off the lights, and it will get it done smoothly. But it won’t spontaneously decide you need an alarm or pick out a playlist to match your mood unless you explicitly tell it to beforehand.

Agentic AI takes this to the next level. It’s not just about creating content like generative AI or following commands like AI Agents – it has the capacity to define its own goals, make judgment calls, and evolve based on feedback from the real world.

For example, imagine an investment bot powered by AI. A regular AI agent might buy cryptocurrencies when you instruct it to, but agentic AI can go much further. It can analyze market conditions, develop investment strategies, and adjust its approach over time, all without needing constant human direction. It’s about proactive thinking, learning from experience, and constantly improving its strategy.

Consider agentic AI as a self-motivated entrepreneur: it not only works on tasks but also decides which tasks are most valuable and how to optimize them. It really is the future of AI independence.

Here’s a quick comparison of generative AI, AI agents and agentic AI, looking at their purpose and how much autonomy they have:

So, which one is more powerful?

Well, it really depends on what you need it for. If your priority is creating content, generative AI is your go-to. If you need tasks executed efficiently, AI agents are incredibly useful.

However, if you’re looking for an AI that can truly think, strategize, and adapt, agentic AI is likely the most powerful choice.

The real game-changing moment will be when we can bring all three together into a unified system — an AI that can create, execute, and refine its decisions autonomously. And it looks like that’s precisely the direction AI is heading!