What Is Agentic Architecture?

One thing about technology is it’s growing incredibly fast, to the point we don’t know what will be extinct or suddenly growing. However, we can say that intelligent systems are there in the future, and one of the promising ones is AI that can think, reason, and act by itself without much interference from humans. 

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The aim is to create AI agents with powerful capabilities such as decision-making, goal-setting, and adaptability. You can’t only work faster and easier, but it also triggers AI applications to be more independent and scalable. 

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This page will explore everything about agentic architecture. You’ll find its main parts and go over the various kinds you might want to know. We'll help you understand how agentic systems are impacting the future of intelligent technology, whether you're new to the idea or looking to enhance your understanding.

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Understanding agentic architecture

To get a grasp on agentic architecture, it will be helpful to break down the term “agentic”. Basically, something is agentic if it behaves like an agent. They decide by themself, take actions, and go after their goals. It means the process involves creating systems that go beyond just following instructions; they can actually think through problems, plan their next moves, and respond intelligently to new situations. 

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Agentic architecture is the brain behind those systems. Agentic architecture helps an AI perform tasks, as well as understand the reasons behind the actions and tweak its methods if things shift around. Agentic systems are built to be more flexible and goal-driven. Rather than just following strict rules or instructions, they focus on learning and adapting. 

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In real life, you can imagine something like this: you have an employee who can think for themselves instead of just a machine that sticks to a script. This architecture is really important for advanced AI applications, like smart assistants that help you with your schedule and autonomous robots that can move around in the real world. It's really about equipping AI with the ability to think, take action, and get better by itself. 

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Components of agentic architecture

Five essential components are at the core of any agentic AI system, which collaborate to facilitate the system's ability to think, act, and adapt in a manner that is more akin to that of a human decision-maker. Here are those five components:

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• Perception. This is the method by which the AI requires information from its surroundings. In the same way that humans utilize their senses, agentic systems use tools such as sensors, data inputs, or APIs to comprehend what’s happening around them.
• Cognition. Cognition is what’s next after the system gathers information. The AI analyzes, solves problems, and plans the next steps. You can say it’s the “brain” of the process, as they help the system to decide what to do.
• Action. As the name suggests, it’s where the decision changes into action. The AI takes action by sending a command, completing a task, or interacting with the environment or users. That’s all based on the objectives and the knowledge.
• Memory. Agentic systems don’t simply execute actions and then promptly forget about them. To make better decisions in the future, they retain information regarding their past experiences, outcomes, and lessons learned. In short, AI can improve with the help of memory, so they’ll work more efficiently in the future.‍
• Communication. No AI operates in complete isolation. The system is able to engage with other AI agents or external systems. The better the communication, the better the outcome, as sharing goals, receiving feedback, and enhancing collaboration are easier to do.

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Put them together, and they’ll be the backbone of agentic architecture. They’ll enable AI systems to be beyond passive tools. Instead, AI will be much more proactive, adaptive, and capable of completing complicated tasks on their own. 

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Types of agentic architectures

1. Single-agent architecture

This is exactly what the term implies. It’s a system where there’s only one intelligent agent behind the AI. This agent is responsible for performing all tasks on its own. Starting from understanding the environment to take action, that’s all they are for. They are independent, meaning they don’t need other agents to operate. 

If you already have a well-defined scenario in your system and don’t have complex interactions, then a single-agent architecture might be the best choice for you. 

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Pros

• Simplicity. Single-agent systems are easier to build and maintain. It’s not a complicated system since there’s only one decision-maker. It’s perfect for small companies that don’t need complex AI agents.
• Efficiency. Considering their simplicity and function, single-agent systems are effective as long as you already have well-scoped problems. They are capable of operating faster, as they have no shared resources with others.
• Lower resource required. In comparison to multi-agent setups, single-agent systems typically need less computational power. It also means lower cost needed. 

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Cons

• Limited adaptability. As single agents, they tend to struggle with dynamic or large-scale environments. In those situations, multiple perspectives would be much more helpful and make sense. 
• No collaboration. The system might be overwhelmed when performing complex tasks or unexpected scenarios since there are no other agents to share the load.
• Scalability struggles. There’s no upgrading the scalability; you can only rely on it on its own, as a single-agent system is not something you can use to handle a bigger load. Larger scalability means a complete redesign.
  
  

Use Cases

• Virtual assistants like chatbots or scheduling tools
• Autonomous robots, such as vacuum robots
• Smart thermostats or simple home automation systems
• Customer service bots handling routine queries
  
  

In the end, single-agent architecture is the right choice for you as long as you have a small scalability system, clear problems, and a predictable environment. 

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2. Multi-agent architecture

Multi-agent architecture involves some intelligent agents working together within the same system. Each of them works independently, but they can communicate, share information, and coordinate their actions so they can achieve their goals. It’s literally a working team, but with AI agents. 

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This system suits dynamic, large-scale environments and too-complicated tasks best. If your system is impossible to be handled by single-agent architecture, it means you need to use this system.

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Pros

• Collaboration and coordination. Multiple agents are created to work together. They can divide their tasks and even combine their power to handle complex and challenging tasks more efficiently. 
• Scalability. You will not struggle to expand the scale. You just need to add more agents, and it’s done! All without needing to redesign the entire architecture. 
• Resilience. AI is prone to making mistakes; that’s why it’s important to have backups. If an agent does the work poorly, other agents can take over to improve the job.

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Cons

• Increased complexity. Of course the more agents you have, the more challenging it is to communicate and coordinate properly.
  
  
• Communication overhead. Agents need to constantly exchange information. It slows things down and needs more resources as well, especially if you have real-time systems.
• Prone to conflict. It needs extra coordination. Agents might work against each other or duplicate tasks. There’s a high potential risk of reducing overall efficiency. 

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Use Cases

• Autonomous vehicles conversation in traffic systems
• Supply chain managements with agents to handle logistics, inventory, and demand
• Multi-agents in warehouse automation
  
  

Last but not least, multi-agent architecture is ideal if you have complicated tasks and need collaboration. for better, reliable, and faster results. 

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3. Vertical architecture

The term is a bit different compared to the first two. Vertical architecture is the term used to describe the design of an AI agent in layers or levels, with each layer responsible for a distinct function. It ranges from low-level sensing to high-level reasoning and planning. Imagine it as a hierarchy within the agent. Each level builds upon the information or actions of the one below it.

Vertical architecture thinks and does like humans. We first process what's happening, then interpret it, make decisions, and finally take action. In this system, the same logic applies. It’s done in the software, though, not our body. 

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Pros

• Clarity and organization. The system is more straightforward to design, test, and troubleshoot when it’s divided into separate layers that handle different functions. This way, the agents will be less likely to work against each other, as each of them has a clear role.
• Adaptability. It’s easy to replace one layer without the hassle of redesigning the entire system. Since it’s highly adaptable, you can worry less about struggling with changing the system back and forth.
• Capable of complex works. Hierarchy helps break down the agent’s behavior into manageable levels of reasoning and action. It makes tasks less complicated and hectic. 

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Cons

• Slower response time. The system may be less responsive during time-sensitive situations. The data and decisions flow through multiple layers; therefore, it’s most likely to happen. 
• Rigid structure. Although the layered setup provides clarity, it can also reduce the system flexibility. Sometimes, real-world situations don’t follow the “ideal” logic, so flexibility is something you should take into account. 
• Harder to scale horizontally. Vertical systems are great for depth, but they don’t naturally support collaboration across multiple agents unless integrated with other architectures.

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Use cases

Vertical architecture is known to be used in: 

• Robotics: where sensing, processing, and acting must happen in sequence
• Game AI: to separate real-time player tracking, decision-making, and character movement
• Workflow automations: where you need multistep approvals. 

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4. Horizontal architecture

Horizontal architecture differs from vertical architecture in its methodology. Horizontal architecture takes a different approach from vertical architecture. Horizontal architecture stacks their layers into separate, specialized components instead of using top-down hierarchy. It creates more independent components. 

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How so? Each component or module is responsible for a specific function and communicates directly with one another as needed. This approach is more akin to teammates working together rather than adhering to a sequence of command.

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This architecture is built for the system where modularity, responsiveness, and flexibility are essential. It enables the system to scale across various functions and adapt more easily without being locked into a rigid structure. 

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In this system, perception, memory, decision-making, and action are regarded as distinct units. These modules are able to respond dynamically based on context, as there’s no strict order in which tasks must be done.

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For instance, a perception module may detect an abrupt change in the environment and promptly notify the decision-making module, without the need to await instructions from a higher level. 

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Pros

• Flexibility. This system tends to engage in more dynamic interactions, as there’s no rigid hierarchy compared to other architecture. This way, horizontal architecture is able to handle unpredictable environments that constantly change. 
• Parallel processing. Different components can run at the same time. It means the decision-making process and response times can be faster than expected.
• Easy integration. Enhance the system's adaptability and scalability without needing to redesign the entire system. New modules can be inserted easily. 

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Cons

• Coordination challenge. It’s a bit of a hassle to coordinate accordingly unless you have well-designed and precise communication protocols. If not, components may transmit outdated information instead.
• Lack of central control. The system may encounter difficulty in prioritizing tasks or resolving conflicts between modules since there’s no clear top-down structure.
• Debugging difficulties. In comparison to layered systems, the identification of the source of an error may be more challenging due to the fact that everything occurs in parallel. 

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Use cases

Here are what commonly use horizontal architecture for their system:

• Email summarization tool
• Multi-tasks AI assistants, such as voice recognition and emotion analysis
• Language interpreters that summarize, paraphrase, or translate content

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5. Hybrid architecture

As you might have guessed, Hybrid Architecture is a combination of both horizontal and vertical architecture. That’s why they have a more balanced and capable AI system. This architecture integrates both a layered, side-by-side model and utilizes vertical layers for structure and horizontal modules for flexibility. It’s a smart mix rather than a choice.

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What they aim to offer is simple yet the best: the structured decision-making of vertical systems and the horizontal systems adaptability. It’s particularly beneficial in complicated environments where AI agents must simultaneously adapt to change, think critically, and act quickly. All at once. 

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In a conventional hybrid system, the core logic or decision-making may adhere to a vertical structure, which involves digesting data step-by-step from perception to action. However, individual layers may be built as horizontal modules that perform in parallel and communicate freely. For instance, a robot may implement a vertical flow to determine the appropriate task (e.g., navigation, object retrieval, or recharge) and horizontal modules to manage real-time sensor data, voice commands, and movement control. 

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Pros

• Well-balanced design. Hybrid architecture is the best choice for intricate real-world tasks, as it provides structure where it is required and flexibility where it’s beneficial. 
• Improved adaptability. The system is capable of effectively responding to both predictable routines and unexpected events. They integrate various processing methods to do so. 
• Modular and scalable. You can modify components of the system, including horizontal modules and vertical layers, without much worry. 

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Cons

• Higher design complexity. A combination of two architectural styles within one system means higher design complexity. You also need careful planning and strong integration to ensure the system is going well. 
• Possibility of coordination issue. If not executed correctly, the combination of vertical control and horizontal communication can lead to inefficiencies or confusion.
• Resource-intensive. Everything that's super great tends to require powerful resources. Hybrid architecture needs higher computing power and memory since it runs both structured and parallel processes.

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Use cases

• Virtual assistants like Siri or Google Assistant that use vertical structure to interpret your voice command and horizontal structure to manage different modules such as music and reminders.
• Smart home systems like thermostats that adjust temperature when you change the setting and learn the habit over time. 

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Agentic frameworks

When creating agentic systems, several frameworks enable an AI agent to figure out how to think, make choices, and interact with its surroundings. Let’s discuss three of the most popular types: reactive, deliberative, and cognitive. 

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1. Reactive Architectures

The main goal of reactive architecture is to respond quickly. These systems don’t conduct much planning or thinking; instead, they react to what’s going on around them. The agent reacts right away if a sensor notices a change. It’s like a reflex: you react without thinking in advance. 

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That’s also how this architecture works. They are perfect for quick, simple jobs where speed is more important than strategy. 

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2. Deliberative Architectures

Deliberative architecture is smarter. Agents from this architecture take their time to think about options, arrange a plan, and even develop a model before they act. They consider what will happen several moves ahead, just like in chess. 

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Yes, it offers some benefits, but it can also slow things down. If you’re on a system where careful planning matters more than instant response, then deliberative architecture is the right choice. 

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3. Cognitive Architectures

Cognitive architectures try to copy how people think. In short, they are a combination of reactive and deliberative systems, including their memory, learning, and reasoning skills. They also think about things, learn from their mistakes, and change how they act over time.

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These frameworks are the most advanced and are commonly used in research or AI programs that need to think like a human. 

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4. Real-world applications of agentic AI

Finance

Speed and precision are indispensable in the financial sector. Agentic AI systems are currently being used to manage risk, make investment decisions, and monitor markets with minimal human intervention. 

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Use case in finance: an autonomous trading agents

Nowadays, you might have heard about an AI agent that constantly analyzes global financial markets, news headlines, and economic trends. Based on the goals you set in advance, the agent decides when to buy or sell stocks for you. It doesn’t need a human to tell it what to do every minute, which is the best part of having this agent. Instead, the agent adjusts their strategy depending on the situation.

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For a clearer image, this case might help you understand it better: hedge funds and popular trading platforms use agentic AI to perform high-frequency trading, which puts the users at ease to take big decisions. You don’t even need to monitor everything; agents will do it for you.

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Healthcare

In the healthcare sector, agentic AI might not be as popular as financial, but AI agents are widely used to analyze data, offer recommendations, and even automate routine care in support of the medical professionals. 

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Use care in healthcare: Personal Health Monitoring

You can imagine this easily: a wearable device powered by agentic AI. It tracks your heart rate, sleep hours (and your habits as well), and activity levels continuously. It doesn’t stop there. It notices if there are irregular patterns, such as a drop of heart rate or sudden rise of blood pressure. It analyzes what happens and sends alerts to your phone and even suggests actions like resting or visiting a doctor. 

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Another case you might need to know is how remote patient monitoring systems now use agentic AI to provide care for chronic disease patients at their homes. Of course, these systems still need medical professionals, unlike other sectors, but they are a big help to lessen the workload, especially in analyzing data. 

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Customer Service

Customer service industry requires speed, accuracy, and empathy. That’s why agentic AI is necessary in this sector to help businesses engage with their customers better. 

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Use case in customer service: Intelligent Virtual Assistants

Do you notice a lot of virtual assistants in e-Commerce or online shops start to feel like friends instead of robots? Those AI agents understand your issue deeply, ask relevant questions, and provide personalized solutions in a matter of seconds! It has the ability to escalate the issue to a human and provide a summary of the conversation if your problem is a bit complicated. 

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For instance, banks, telecom providers, and e-Commerce businesses are using agentic AI to manage various tasks, such as solving invoicing issues, monitoring orders, and giving product recommendations based on customer behavior. This approach ensures that customers receive 24/7 service with the human-like intelligence of AI agents.

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Get started with agentic AI

Want to integrate agentic AI into your system and automate some of your tasks? Put your trust in our platform, Boltic.

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Boltic is a no-code AI workflow builder designed to help you build, manage, and deploy complex agentic workflows. All without the need to write lines of code from scratch. No matter what your industry—retail, finance, healthcare, or B2B—Boltic gives you the tool to automate complex workflows, take smarter and wiser decisions, and scale with ease.

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With Boltic, you’ll be able to:

• Automate time-consuming processes.
• Make data-driven decisions.
• Improve team collaboration.
• Deliver more personalized experiences.
• Adapt workflows as your business grows.

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Ready to see what agentic AI can do for you? Contact Boltic and bring intelligent automation to your everyday performances.