Sep 29, 2025Understanding agentic architecture is the first step toward unlocking the potential of AI-powered systems. You can say that agentic architecture refers to a system which constructs and shapes the virtual environment and workflow in order to automate AI models. These automated AI models can help you in many ways, like being your personal assistants, aiding you in robotic control systems or in enterprise automation.
Hence, it’s safe for us to surmise that these agentic architectures produce AI systems which work like “agents” for us.
Meanwhile, Agentic AI is a system which employs AI agents to perform tasks. If you’re wondering how they do this, they basically sense their environments, process data or information, and act upon a certain decision, without your assistance.
So, when you say, “agentic architecture”, you refer to systems which allows LLM (language learning modal) driven AI agents to perform tasks.
Example of agentic architecture:
Those self-driving cars that you see roaming on social media nowadays are actually one of the practical examples of agentic architecture and smart agent design. And while these cars often give some people a fright, they leave others completely baffled and wondering how do they even function.
Here’s how they work:
So, these cars observe their environments with the help of sensors, such as radar and cameras, and next they process the collected data with advanced AI algorithms to deduce a digital map, and control the brakes, steering and acceleration of the car.
Is agentic architecture another branch of AI?
Not at all! You don’t have to confuse agentic architecture as being a branch of AI solutions, because it is not. It only brings together different techniques of AI to build a system.
Foundation AI Agents: The AI foundation of smart agents
What Are Foundation AI Agents?
Before hurrying on to learn about foundation AI agents, it is highly important for you to understand foundation models.
What are foundational models?
In easier terminology, you can relate foundational models to a very smart brain, such as Google’s Gemini and GPT-4, which has gained a ton of knowledge by being fed a dozen pieces of data. Now, if you give this very smart brain the ability to act on its own to attain outcomes, it becomes a foundation AI agent.
Cherry on top: It does everything on its own instead of relying on guidance and assistance from you.
Foundational AI agents work through pre-defined structures. They lack deep adaptability, yet they form the ground on which more complex agents rise. Within agentic architecture, they are often grouped into distinct types of AI agents.
Simple Reflex Agents
Model-Based Reflex Agents
Goal-Based Agents
Utility-Based Agents
Note that each of these has their own crucial role in the making of AI systems.
Types of foundation AI agents
1. Simple Reflex Agents
Simple reflex agents act only on the current percept—the information they sense in the moment. They follow a straightforward condition-action rule: when a condition is met, a set action follows.
Example: Thermostats are an example of simple reflex agents too.
2. Model-Based Reflex Agents
Model-based reflex agents are built on simple reflex agents by using an internal model of their surroundings. This model allows them to keep track of parts of the world which they are unable to see directly.
For example: Automatic vacuum cleaners.
3. Goal-Based Agents
Goal-based agents move beyond simple rules and internal models. They make choices with specific goals in mind. Rather than merely reacting, they plan steps that lead them closer to a set objective.
Example: A route-planning AI is one of the examples of goal-based agents.
Take this as an example, a route-planning AI in a navigation app would choose either the quickest, or the closest path when guiding the driver with directions.
4. Utility-Based Agents
Moving on to utility-based agents, they make sure to measure the worth of every outcome while also making sure that every choice that they make is a balance between gain and loss, a quiet pursuit of not just success, but the right success.
Example: In the world of trading, such an agent scans the shifting landscape of investments and chooses the path where reward shines brightest.
The Role of Foundation Agents in Agentic Architecture
An overview of foundation AI agents
Agents | Strengths | Limitations | Use Cases |
| Simple Reflex Agents |
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| Model-Based Reflex Agents | Can operate in environments which are only being observed partially. Gives better adaptability (as compared to simple reflex agents). |
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| Goal-Based Agents |
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| Utility-Based Agents |
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The Role of Foundation Agents in Agentic Architecture
Foundational AI agents hold the following capabilities:
They understand the desired outcome of users.
They make sense of the situation.
They draw a plan to achieve the desired outcome.
Lastly, they take action to achieve the desired outcome.
Due to the above capabilities pals, you can already realize that foundation agents are infact quite important as they are the core reasoning engines which makes the system move beyond mere automation.
The anatomy of foundational agents in architecture
Let's see glance at the way the agentic system works. It has multiple layers in it, with each layer assigned with a different duty:
Sensor: As the name speaks for itself, here the agent sees and hears everything.
Retrieval / Tool layer: This one hunts information in databases, knowledge bases, or calls external services.
Reasoning / Model layer: The foundation model lives here—it understands the context, makes decisions, and creates a plan.
Memory: With this, agents can track what’s already been happened, and what is yet to be done.
Action / Orchestration layer: You're going to see all the plans being carried out from here by running commands, using tools, or coordinating multiple agents to finish tasks.
Practical Applications across Industries
Healthcare
Simple reflex agents watch over patient vitals and raise alerts when conditions shift.
Utility-based agents shape treatment paths, choosing the option that offers the greatest benefit.
Healthcare apps that track patient vitals and maps that shift with the road ahead show us how mobile application development with AI agents create experiences that feel seamless, intelligent, and alive.
Finance
Goal-based agents focus on uncovering fraud, responding when suspicious patterns emerge.
Utility-based agents refine portfolio management, selecting investments that balance profit with protection.
Retail & E-commerce
Model-based agents track and adjust inventory, keeping supply aligned with demand.
Goal-based agents shape customer personalization, tailoring experiences to meet
In retail and e-commerce, tailoring the shopping journey is only part of the work. To gain true visibility, SEO services ensure the right customers find what they seek at the right time.
Transportation
Goal-based agents guide navigation systems, charting routes toward set destinations.
Utility-based agents streamline fleet management, balancing cost, time, and efficiency.
Conclusion
There you go! Foundation AI agents have become a crucial part of agentic architecture, and their union is ultimately opening the doors to convenience for us, isn’t that great? So, whether weaving AI agents into enterprise systems, developing personal assistants with AI agent tools or building WordPress development, join smart design with automation to move ahead with ease. To glimpse the future of AI and see how businesses across the world are embracing it, visit the World Summit AI which is happening just around the corner. Get in touch with us for guidance regarding AI or AI solutions.
Want to look at advanced AI agents and learn about their power? Stay tuned for our next blog: Advanced and specialized AI agents in Agentic systems.
Frequently Asked Questions
What are Agents in Agentic AI?
Agents are AI systems that can do tasks independently, by sensing the environment, processing information and making decisions accordingly.
What is the difference between Agentic AI and agents?
Agents are the individual actors—self-contained units that perform tasks. Agentic AI is the wider design that brings these actors together. Think of agents as players and Agentic AI as the stage that allows them to move, interact, and complete the play.
What is the AI Agent framework?
The AI Agent framework is the structure that holds the work of an agent. It works by following these steps: sensing their surroundings, reasoning with the information, and implanting the action. This cycle keeps repeating in order for the agent to adapt and grow.
What are foundation models in AI?
Foundation models are large, pre-trained systems that serve as the roots of modern AI. They learn from vast amounts of data and can then be adapted for many different tasks. Like a river fed by countless streams, they provide the flow from which many AI applications spring.
What is Google's agentic AI framework?
Google’s agentic AI framework is their method for building AI systems that work as agents. It has some layers, such as sensing inputs, taking in the information, reasoning through models, and taking action. Each layer connects to the next in order to guide the agent from perception to decision to action.
