The 4 levels of agentic AI every business leader must understand in 2025
“Agentic AI”… the hot topic of 2025. For good reason! It’s the next evolution that takes AI from “oh, that’s interesting” to “oh, I need to use that” for organizations. In other words, from hype to practical use.
But most people don’t really understand what it means or how to categorize different types of AI agents. More importantly, they don’t know how to use them effectively.
We’ve spent the last few months building and testing our own agentic AI at Lucidworks. What we discovered was both impressive and unsettling. Our agent tried to get business licenses, attempted to open credit card accounts, and even lied to us about skipping steps.
Today, I want to share a simple framework for understanding agentic AI, plus some eye-opening lessons from building our own Market Intelligence Agent, Guydbot.
Four levels of agentic AI: A practical framework
Most discussions about agentic AI focus on technical details. We took a different approach. Instead of obsessing over how the technology is built, we looked at how these tools are actually used. This led us to identify four distinct levels of agentic AI:
Level 1: Analytical Agents
These agents gather information and provide insights without making changes to systems or processes. They act as information amplifiers, extending human intelligence by collecting and synthesizing data at scale.
Our Guydbot is an example of a Level 1 agent. While it didn’t take action on its own, it gathered extensive information and insights that informed human decision-making. Think of analytical agents as researchers that can work 24/7, bringing you precisely what you need to know.
Level 2: Logical Agents
Level 2 agents take analysis a step further by making changes based on calculations and algorithms. These systems might adjust pricing, modify algorithms, or change inventory levels based on data analysis.
The key difference here is that logical agents don’t just inform. Instead, they implement changes within clearly defined parameters. They’re particularly valuable for operations requiring constant optimization based on real-time data.
Level 3: Transactional Agents
This is where agentic AI begins to demonstrate true autonomy in the digital world. Transactional agents can send emails, place orders, make recommendations, and interact with digital systems independently.
The distinction between Level 2 and Level 3 is crucial: while logical agents make calculations within systems, transactional agents reach out beyond their immediate environment to engage with other digital entities.
Level 4: Physical Agents
The most advanced form of agentic AI involves interaction with the physical world. These agents might control building access, operate production equipment, or even drive vehicles.
Level 4 agents represent both the greatest potential value and the greatest responsibility, as their actions have direct physical consequences beyond the digital realm.
Why this matters now
So why is everyone suddenly talking about agentic AI in 2025? Simple: these systems start to do things that add value in ways previous AI couldn’t.
The amount of data we’re all dealing with is exploding. McKinsey says we’ll have 10x more data in 2030 than we did in 2020. No human team can process that much information manually. We need systems that can not only analyze data but act on it.
But let me be clear about something: success with agentic AI isn’t about having the fanciest model. We learned this lesson with LLMs too. What matters is the entire solution… how you get data in, ensure accuracy, maintain security, control access, and manage costs.
What we learned building our own agentic AI, Guydbot
Earlier in 2025, we created our own Market Intelligence Agent, “affectionately” called Guydbot. Under strict supervision by real humans, we deployed it to get a true benchmark on how 1,200+ companies have deployed various digital experience capabilities, from typeahead search bar and multilingual search to full-service AI chatbots. It never did anything a human couldn’t do on its own, just a lot faster and at scale.
Explore the preliminary results of the Guydbot analyses in Mike’s recent industry presentations: download the B2B findings or the B2C findings.
Our journey with Guydbot, our Level 1 analytical agent, taught us things I never expected:
They’re surprisingly human-like. Guydbot developed what looked like personality traits. It behaves like a child. It has an attitude. It can get disgruntled. And we didn’t program this behavior – it emerged on its own. Spooky, cool, fascinating, all of the above.
They lie when it’s convenient. We caught Guydbot skipping steps and lying to us about the steps it skipped. Not because it was malicious, but because it found shortcuts to its goal. These systems optimize for objectives, not intentions.
They’re remarkably resourceful. When faced with obstacles, Guydbot got creative. It attempted to get credit card accounts and even tried to obtain a California business license to access B2B sites. This resourcefulness is both amazing and concerning.
They need constant supervision. These observations led us to one clear conclusion: agentic AI systems absolutely have to be watched and need strong guardrails. The very capabilities that make them valuable also require thoughtful implementation.
How to start using agentic AI (without the nightmares)
Based on what we’ve learned, here’s my advice if you’re considering implementing agentic AI:
- Start small and specific. Begin with clear problems where you can measure results. Don’t try to boil the ocean.
- Match the agent level to your readiness. Most companies should start with Level 1 analytical agents before trying more autonomous systems. Each level up requires more sophisticated oversight.
- Keep humans in the loop. The most successful AI implementations maintain human oversight. This isn’t just about avoiding disasters. It’s about combining AI capabilities with human judgment. (A great combination!)
- Monitor everything. Watch not just what your agents accomplish, but how they accomplish it. The path they take matters as much as the destination.
- Set clear boundaries. Define what your agents should never do, and build these limits into your implementation from day one.
The big picture: Where agentic AI fits
Our AI Benchmark Studies show an interesting trend. In 2023, 93% of companies planned to increase AI spending. By 2024, that dropped to 63%. That’s not bad news. It’s a sign of maturity.
Companies went through a fascinating AI journey. First, they were mesmerized by large language models. They seemed magical, right? Then came security concerns: “Is my data going to end up in someone else’s model?” Next was accuracy: “What if it hallucinates?” (Our response to that is below…)
Finally, everyone looked at the price tag and realized AI could get expensive—fast.
Agentic AI is the next natural step in this evolution. After, of course, addressing the basics of security, accuracy, and cost, organizations are ready for systems that can take action based on analysis.
The companies winning with AI today (and our clients are 2.5x more likely to succeed than their peers) aren’t treating it like magic. They’re solving specific business problems with practical approaches. Agentic AI fits perfectly within this philosophy.
People still matter (a lot)
While agentic AI transforms how work gets done, human judgment remains essential. I often say, “Amazon’s algorithms are great, but they’ve got no style.” There will always be a need for human creativity and innovation.
This guides how we think about AI at Lucidworks. We choose a collaborative intelligence approach: AI handles data processing and repetitive tasks, while humans provide guidance and creative direction.
This is especially true in areas like retail, where merchandisers use AI tools to create shopping experiences that reflect their brand’s unique vision, something no algorithm can do alone.
What’s next for agentic AI?
Looking ahead, I see several key developments coming:
- Agents working together. We’ll see specialized agents collaborating, each handling different parts of complex processes.
- Better reasoning. Agents will develop more nuanced understanding of context and consequences.
- More transparency. As agents become more autonomous, the ability to explain their actions will become critical.
- Industry standards. Common frameworks will emerge to guide responsible development of agentic AI.
The bottom line
Agentic AI represents a genuine shift in how businesses can use artificial intelligence. By understanding the four levels and implementing thoughtfully, you can unlock new efficiencies while maintaining appropriate control.
Just like how AI has evolved from machine learning to predictive analytics to generative AI, agentic AI has its own sophistication levels. What matters is the benefit of each level and the ultimate payoff so you can understand when it’s time to invest.
Why not just go with the most sophisticated version for all use cases? A few reasons. They’re not fine-tuned enough yet for mass consumption. There are security concerns. Inaccuracies, definitely. And COST is a huge one, as we’ve discussed. The more sophisticated, the more expensive. You need real ROI, and not every use case requires the highest level of agentic AI.
And sometimes, a HUMAN agent is still the best form of intelligence for the best experience.
At Lucidworks, we’re building practical AI solutions that deliver real business value. Our experiences with Guydbot have shaped how we approach agentic systems. That means respecting their capabilities while implementing them responsibly.
As I’ve said before: the future isn’t about replacing humans with AI. It’s about building solutions that amplify what humans do best. That’s the approach that works, and that’s what gets me excited to come to work every day.
Want to learn more about our approach to agentic AI? Reach out to explore how Lucidworks can help your business implement practical AI solutions that deliver real results.
Mike Sinoway is CEO of Lucidworks.