AI agents have become major drivers of enterprise automation at scale, with successful use cases having a notable impact. You must have noticed that everyone in the field of AI wants to find out how the AI agent works and understand its architecture. The growing interest in AI agents comes from the fact that they are different from basic automation and AI chatbots. AI agents bring an element of autonomy and are able to perceive the environment, reason and take relevant actions without human intervention.
- Data from Salesforce reveals that about 44% of consumers in the United States have no problem using AI agents as personal assistants (Source).
- A new study from CISCO indicates that agentic AI will handle 68% of customer service and support interactions by 2028 (Source).
- Nearly 93% of IT leaders in the United States are actively seeking opportunities to implement agentic AI in their business (Source).
You can see that businesses and individual users are recognizing the potential of AI agents, driving the adoption of agentic AI. However, reality paints a different picture, as many businesses are unprepared for the autonomous intelligence provided by AI agents. This is one of the main reasons why you need a deep understanding of AI agent architecture and the fundamentals that drive them. Familiarity with agentic AI architecture and key components of AI agent systems will give you the confidence to adopt AI agents.
Understand how an AI agent works
The first thing on your mind right now should be how AI agents work to deliver the benefits of autonomous automation. You can choose any of the AI agent examples and learn about their usefulness as autonomous software systems tailored to achieve specific goals. AI agents are not designed just to respond to your prompts and they have the ability to make decisions about the next course of action.
Unlike traditional AI tools and systems, AI agents can:
- Work to achieve a specific goal.
- Leverage different tools, including databases and APIs.
- Maintain context from previous interactions.
- Adjust their actions based on the results.
How can AI agents do all these things? A high-level overview of the working mechanism of AI agents reveals that they work in a continuously running loop. In the loop, AI agents observe the information, use reasoning to determine their next step, and act on their own. Additionally, AI agents also learn from the results before repeating the loop.
You can consider an AI-powered human assistant as the simplest example to understand how AI agents work. When you ask the assistant for help, it will observe your request and use reasoning to prepare plans for the next task. The assistant will use tools to follow up on your request, such as sending emails. Based on your feedback, the wizard will make adjustments to better execute the request in the next iteration.
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Unraveling the fundamental principles that govern AI agents
Agentic AI relies on a set of specific principles that define the behavior of AI agents and how they operate and interact with each other. You can find the answers to the question “What does the AI agent work for?” » by identifying the fundamental principles that serve as the building blocks of agentic AI architectures. Learning the fundamentals of AI agent systems can help you easily understand the layers of agentic AI architecture.
AI agents can work autonomously without relying on constant human intervention.
The work of each AI agent revolves around the goals for which it was designed. AI agents pursue their goals and evaluate how their actions will contribute to achieving the specified goals.
The ability of AI agents to perceive the environment around them allows them to interact with their surroundings. AI agents can collect data about their environment from sensors or other digital inputs and external systems.
You should know that AI agents have reasoning abilities, which make them rational entities. AI agents can combine data from the environment with retained context from past conversations and domain knowledge to make decisions.
AI agents do not react to inputs and have the ability to take initiatives based on predictions and models for future states. Rather than reacting to events, AI agents can anticipate changes and respond accordingly.
The highlight of AI agent architecture draws attention to the ability of AI agents to learn from past interactions and continually improve. AI agents identify different patterns, results, and feedback to optimize their decision-making and behavior, something you won’t find in static tools.
The fundamental principle of adaptability of AI agents makes them capable of adjusting their strategies in response to new events. The flexibility of AI agents is an essential requirement to handle uncertainty, incomplete information or completely new situations.
AI agents can also work with human agents and other AI agents to achieve the same goals. In multi-agent systems, AI agents can communicate with each other and coordinate to perform different tasks in unison.
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What are the components of agentic AI architecture?
The best way to learn about AI agent architecture will require an understanding of the different components. You can choose the three-tier intelligence model to understand how businesses can build and scale agentic systems.
1. Foundation level
The first layer of AI agent components is the base level, which defines the primary intelligence base of the system. You will find two crucial components in the base tier: the state and memory component and the knowledge layer.
The state component tracks the goals an agent pursues, the actions it takes, dependencies, and results. As a result, the agent always has a context with which to act rather than starting from scratch.
The memory component ensures the continuity of agents relying on two types of memory, short and long. Short-term memory is essential for maintaining flow during a specific task or conversation. On the other hand, long-term memory provides lasting knowledge, which you can find in business rule examples or customer history.
AI agents leverage the base level knowledge layer to access domain context and business data. Notable tools used in this layer are RAG, vector databases, and enterprise search. The knowledge layer combines structured and unstructured information to create a shared context for AI agent reasoning.
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2. Workflow level
The workflow level transforms the understanding developed at the basic level into action. You should be aware that the components of the workflow level determine how different agents will work together, manage sequencing, and ensure that agents are working on the right tasks. The two notable components of the workflow tier are the scheduler and the orchestrator.
The Agentic AI Architecture Workflow Tier Planner divides complex business goals into smaller tasks. It mainly focuses on designing dependencies, sequencing tasks, and determining what needs to happen with a clear explanation of all agent actions.
The orchestrator plays a major role in the operation of an AI agent by deciding which agents should perform a specific task. Additionally, the orchestrator also determines how the results can be combined to deliver a clear outcome. Other responsibilities of the orchestrator revolve around routing tasks based on their complexity, tracking progress, ensuring smoother handoffs, and resolving conflicts.
3. Autonomous level
The final layer of components in the agentic architecture is the autonomic level, which mainly deals with actions. You’ll find two main components in this layer: AI agents, and the tools and APIs used by the agents.
AI agents function as central components of the agent framework with their autonomous reasoning and abilities to use the right tools and APIs. Although they work independently, the orchestrator and planner guide the actions of the AI agents.
The usefulness of AI agents largely depends on their ability to interact with business systems. This is where APIs help agents trigger transactions, update workflows, retrieve data, and connect to different company systems. AI agents also use other tools to perform tangible actions and demonstrate business readiness.
Final Thoughts
Overview of the key principles and essential components of AI agent architecture reveals that agents do not work alone. If the hype around the autonomous reasoning and decision-making capabilities of AI agents grows, then this is possible thanks to the components that underpin agentic architectures. You will clearly notice that the fundamentals of agentic AI provide the ideal foundation for the long-term adoption of AI agents. With a comprehensive understanding of agentic AI architecture and related components, you can find the ideal roadmap for adopting AI agents for your business. Learn more about agentic AI and how it works today.
