Agentic AI is shifting from concept to implementation.
But for leaders and teams tasked with bringing this technology to life, there’s a critical next step: understanding how agentic AI actually works.
This is the second part of our two-part series on agentic AI. In part one, we explored what sets agentic systems apart from traditional AI agents, and why that shift matters strategically.
In this article, we’ll go deeper into the architecture of agentic AI: the components, learning methods, and decision models that power it.
We’ll cover how these systems gather and process data, learn through reinforcement, make autonomous decisions, and stay aligned with human oversight.
For enterprises planning to adopt or integrate agentic AI, gaining this understanding is the foundation.
Data ingestion and preprocessing
At the heart of every AI system is data that’s raw, messy, and unorganized. For agentic AI to work well, this data needs to be cleaned and prepared for the system to learn from it.
Agentic AI collects data from various sources like company databases, sensors, customer interactions, and external data streams. It’s built to gather and update information in real time.
Before the data can be used, it has to be processed. This involves fixing missing values, adjusting numbers, or converting the data into a format the system can understand.
Data preprocessing ensures the AI works with clean, organized data, which is crucial for making accurate and reliable decisions.
Reinforcement learning at its core
The real strength of agentic AI lies in its ability to learn and adapt on its own, and that’s where reinforcement learning (RL) comes in.
In RL, the AI functions as an agent that makes decisions within an environment, learning through rewards for positive outcomes and penalties for negative ones. For example, an AI running an online ad campaign tests different ads for various groups and tracks which ones get the most clicks. It’s rewarded for good results and penalized for poor ones, typically through numerical scores that guide its learning, allowing it to continuously improve its strategy over time.
A big part of RL is finding the right balance between exploration (trying new actions) and exploitation (sticking with what’s already proven to work). This balance helps the AI avoid ineffective strategies while also allowing it to experiment with potentially better ones.
Reinforcement learning allows agentic AI to improve over time, making it more adaptable and capable of solving new problems.
Neural networks and deep learning architectures
While reinforcement learning is about making decisions, deep learning helps AI recognize patterns and understand data. At the core of agentic AI, you’ll often find neural networks that spot complex patterns in large amounts of data.
For example, convolutional neural networks are great for detecting patterns like edges and shapes, making them perfect for tasks involving images or scanned documents.
On the other hand, when the AI needs to handle data in sequences like text, speech, or trends, it uses recurrent neural networks. These networks remember past information, making them excellent at tasks like speech recognition, language processing, and predicting trends.
By using these neural networks, agentic AI can process complex data and make smart decisions quickly.
Decision-making algorithms
Once the AI has learned from the data, it needs to make decisions on its own. To ensure those decisions stay responsible and within the right limits, decision-making algorithms come into play with safety nets and rules.
The goal is to improve performance, whether it’s increasing profit, boosting customer satisfaction, or hitting business targets. To identify the optimal solution, the AI employs optimization algorithms. These algorithms adjust decisions based on data to achieve the most effective outcome. Two common methods are:
- Gradient descent: Imagine adjusting the temperature on your thermostat. You make small changes, check how it feels, and keep tweaking until you reach the perfect temperature. That’s how gradient descent works. The AI makes gradual changes to its decisions, learning from each step, until it finds the best solution.
- Genetic algorithms: Think survival of the fittest. Instead of creating just one prototype, the AI makes several different versions. It tests them, picks the best ones, and uses them to create the next round of prototypes with improvements. This process keeps evolving until the best models emerge, similar to natural evolution.
While these methods help the AI make decisions, there are constraints to ensure it doesn’t go too far beyond the oversight of the humans it works for.
Human-AI collaboration
Agentic AI is designed to work independently, but it still needs to collaborate with humans. That’s where Human-in-the-Loop systems come into play. By using explainable AI (XAI), the AI’s decision-making process becomes transparent, making it easier for humans to understand how it arrived at a conclusion. For example, if the AI suggests a price change, XAI shows the data and reasoning behind it, so humans can review and approve the suggestion with confidence.
Continuous feedback
The design of agentic AI doesn’t end once it’s up and running; it keeps improving through continuous feedback and retraining. As it interacts with the real world, it learns from each decision it makes. For example, a customer service AI might resolve a ticket and use feedback about customer satisfaction to improve its future responses.
Regular updates and retraining ensure the AI stays current, adjusting to new data, business changes, or shifts in the environment. This ongoing learning helps the AI stay flexible and continuously improve, allowing businesses to adapt to changing needs and conditions.
Final thoughts
Agentic AI isn’t just smarter. It’s more curious, more capable, and more independent. And that means businesses can’t just upgrade their tech. They have to rethink how they build, lead, and let AI make decisions.
This shift isn’t just about better models; it’s about new mindsets. About knowing when to hand over control and when to draw the line. It’s about designing systems that don’t just follow instructions, but take initiative and still stay aligned with your goals.
The companies that move fastest won’t just adopt agentic AI—they’ll collaborate with it and grow because of it.
That brings us to the end of this series on agentic AI. From understanding the difference between AI agents and agentic AI to exploring the architecture that powers these autonomous systems, we hope this series has helped you start defining your vision and role in this evolving space.
