Every AI agent - regardless of how simple or advanced - is built from a small set of core components. This coordination enables the agent to observe information, make decisions, take action, and improve over time.
Understanding these building blocks is essential if you want to design, evaluate, or safely use AI agents in real-world workflows.
While implementations can vary, most modern AI agents share the same foundational AI agent architecture. This page breaks down each component, what it does, and why it matters.
At a high level, an AI agent is not a single model or script. It is a system made up of coordinated parts, each responsible for a specific function. Together, these components allow agents to operate autonomously rather than reactively.
At a high level, an AI agent is not a single model or script. It is a system made up of coordinated parts, each responsible for a specific function.
The foundation model - often a large language model (LLM) - acts as the reasoning core of an AI agent. This is the component responsible for understanding instructions, interpreting information, and generating decisions. Think of it as the "brain" of the agent.
The foundation model enables the agent to:
On its own, a foundation model is powerful but limited. It cannot access live data, remember past interactions reliably, or take real-world actions without support from other components. That's why the foundation model needs to coordinate with planning, memory, and tools to create a fully functional AI agent.
A common misconception is that an AI agent is the language model. In reality, the model is just one part of the system. Without planning, memory, tools, and feedback, the model behaves more like a chatbot than an agent.
Different foundation models have different strengths. Some excel at reasoning, others at following instructions, and others at creative tasks. The choice of foundation model depends on the specific requirements of the agent's task.
The planning module allows an AI agent to break a goal into actionable steps. This is what enables multi-step workflows rather than single responses. Planning transforms high-level goals into executable sequences of actions.
When an agent receives a goal, the planning module helps determine:
Some agents plan everything upfront, creating a complete roadmap before starting. Others plan dynamically, adjusting their approach after each action based on results. Dynamic planning is more flexible and can handle unexpected situations better.
Planning is what separates agents from prompt-based tools. Instead of producing a single answer, the agent reasons about the process needed to reach an outcome. This enables complex, multi-step tasks that would be impossible with simple prompt-response systems.
For example, if an agent needs to generate a comprehensive report, the planning module might break this down into: gather data, analyze trends, create visualizations, write summary, format document, and validate quality. Each step is planned and executed in sequence.
Memory gives an AI agent continuity. Without memory, every interaction would start from scratch, making it impossible to learn, adapt, or maintain context across tasks.
Most agents use multiple forms of memory:
Memory allows agents to:
How memory is implemented varies. Some agents use vector databases to store and retrieve semantically meaningful information. Others use knowledge graphs to represent relationships. The choice depends on the type of information the agent needs to remember and how it needs to access it.
For task-specific agents like AI Shorts on marketplaces, memory is often focused on the current execution context rather than long-term learning. This keeps agents simple and predictable while still allowing them to maintain context during a single task execution.
Tools are what allow AI agents to interact with the outside world. Rather than relying only on what they already know, agents can fetch information, manipulate data, or trigger systems through external tools and APIs.
Tool integration enables agents to:
Tool usage is often dynamic. The agent decides which tool to use based on the task at hand, evaluating available options and selecting the most appropriate one. This flexibility allows agents to adapt to different situations and use the best tool for each specific need.
Common types of tools include:
Static tools are always used the same way, while dynamic tools are selected at runtime, allowing agents to adapt to new situations. The ability to dynamically choose and use tools is what makes agents truly autonomous and flexible.
On marketplaces like SellerShorts, AI Shorts use tool integration to connect with external systems through webhooks and APIs. This allows specialized agents to access the data they need and deliver outputs in formats that integrate seamlessly with business workflows.
Learning allows AI agents to improve over time, while reflection helps them evaluate what worked and what didn't. These mechanisms enable continuous improvement and adaptation.
Learning and reflection can occur through:
Feedback can come from:
This feedback informs future decisions and planning, allowing the agent to refine its approach and avoid repeating mistakes. Over time, learning helps agents become more effective and reliable.
Reflection enables agents to adjust strategies, refine prompts, and optimize tool usage. Agents can reflect on whether they solved a problem correctly, whether the approach was efficient, and how they might improve next time. This self-awareness is what enables true improvement over time.
Not all agents have sophisticated learning capabilities. Some follow fixed patterns while others adapt based on feedback. The level of learning depends on the agent's design and its specific use case.
When combined, the core components enable several defining features of AI agents. These features are what make agents useful for complex, real-world tasks.
Reasoning is the ability to process information logically, draw conclusions, and make informed decisions. The foundation model provides the reasoning capability, allowing agents to think through problems, evaluate options, and choose the best course of action based on available information.
Acting is the ability to take actions based on decisions. Through tools and actuators, agents can execute tasks, interact with systems, and produce outputs. This distinguishes agents from passive AI tools that only provide information.
Observing involves gathering information about the environment through sensors and input processing. Agents continuously observe inputs, outputs, and feedback to understand the current state and make informed decisions about what to do next.
Planning enables agents to break down complex goals into manageable steps. The planning module helps agents create strategies, sequence actions, and adapt plans based on results. This allows agents to handle multi-step workflows effectively.
Collaboration allows agents to work with other agents or human users to achieve shared goals. Through communication, coordination, and shared memory, agents can divide tasks, share information, and synchronize actions. This enables complex systems where multiple agents work together.
Self-refining is the capacity for continuous improvement. Through learning and reflection mechanisms, agents can evaluate their performance, identify areas for improvement, and adjust their behavior. This makes agents more effective over time.
Beyond the five essential components, modern AI agents often include specialized components that enhance their capabilities for specific use cases. These components extend the core architecture for specific needs.
RAG allows agents to retrieve external information and combine it with reasoning from the foundation model. This improves accuracy and allows agents to work with up-to-date data that wasn't part of the model's training.
How RAG works:
RAG is particularly valuable for agents that need to access current information, domain-specific knowledge, or data that changes frequently. It bridges the gap between what the foundation model knows from training and what information is needed for specific tasks.
For example, a customer service agent might use RAG to retrieve product information from a database while using the foundation model's reasoning to craft personalized responses. This combination produces accurate, context-aware answers that wouldn't be possible with either component alone.
Different types of tools serve different purposes in agent workflows:
Data Retrieval Tools: These tools help agents access information from external sources. They're the foundation of an agent's ability to work with up-to-date data. RAG systems are a common implementation of data retrieval tools, where agents can dynamically decide whether to pull external data based on the context of their task.
Static Tools: These tools perform predetermined actions. While the tools themselves are static, the agent can dynamically choose when and how to use them. This approach combines the reliability of predetermined actions with the flexibility of dynamic decision-making.
Dynamic Tools: These tools adapt based on context. The agent can configure or modify tool behavior based on the specific situation, providing maximum flexibility for complex scenarios.
Decision-Making Tools: These specialized tools help agents rank options, score outcomes, or evaluate trade-offs. They range from simple binary choices to complex multi-step planning systems. Decision-making tools form the core of the agent's ability to operate autonomously, allowing it to make choices without human intervention.
The real power emerges when tools are used in combination. An agent might use data retrieval to gather information, apply decision-making tools to analyze it, and then use static tools to take action based on its conclusions.
An AI agent only works effectively when its components are well coordinated. Understanding how components interact helps you design better agents and troubleshoot issues.
An AI agent only works effectively when its components are well coordinated.
A typical flow looks like this:
This flow is iterative. The agent loops through these steps until the goal is achieved or determines it cannot be reached. Each iteration allows the agent to refine its approach and adapt to new information.
Data flows between components continuously:
For task-specific agents like AI Shorts on marketplaces such as SellerShorts, this component interaction is streamlined and focused. Each AI Short is optimized for a specific task, which means the components are configured to work together efficiently for that particular use case. This specialization reduces complexity while maintaining the core benefits of agent-based architecture.
Knowing how AI agents are built helps teams:
For businesses using specialized AI agents from marketplaces, understanding components helps you evaluate which agents are well-designed and suitable for your needs. You can assess whether an agent has the right capabilities, whether it integrates with your systems properly, and whether it will deliver reliable results.
Continue learning about AI agents:
Author: SellerShorts Content Team | Last updated: December 2025