25 Must-Know AI Agents Terms for Beginners

If you've been paying attention to artificial intelligence (AI), you've likely heard a lot of buzz around AI agents and Agentc AI taking on increasingly complex tasks. AI agents and agentic AI are smart programs that can perceive, reason, and act to achieve goals. They promise to automate complex tasks and assist human decision-making. But what do all these terms, like AI agents and Agentic AI, mean?  Understanding these technologies requires learning a new vocabulary; terms like "Agentic RAG," "Chain of Thought," and "Function Calling" describe key agent capabilities. Knowing these terms has become increasingly important for anyone engaging with modern AI development.

Artificial intelligence (AI) could seem challenging despite being so simple due to the influx of new terms and concepts. To help you stay informed and up to date, we've gathered a small glossary of 25 must-know terms in AI agents and agentic AI for business professionals and beginners. This article could end your struggle and explain key concepts clearly in simple words, regardless of your age and occupation.

Here are 25 must-know AI agent terms for business professionals and beginners:

1. AI Agent

What is an AI agent? An AI agent is a program designed to interact with its surrounding digital or physical environment through inputs like data feeds or sensors. The agent processes this information to understand the current situation and takes action based on its understanding of how to achieve specific goals.

Think of it as a digital worker performing tasks like answering and solving customer queries. A more complex one is software managing inventory based on sales data.

• Senses its environment (digital or physical).

• Processes information to understand the context.

• Acts to achieve predefined objectives.

• Operates with some degree of independence.

2. Agentic AI

An agentic AI takes autonomous independence a step further and a degree higher than a basic AI agent. It operates largely without direct human command or intervention, as it can make decisions and execute tasks based on its programming and perceived environment.

It can understand its goal, navigate through obstacles, and achieve its goal without manual control. This self-governance defines its autonomous nature.

• Operates without constant human supervision.

• Makes independent decisions based on programming.

• Adapts actions to environmental changes.

• Often used for tasks requiring self-management.

3. Agentic RAG

Agentic Retrieval-Augmented Generation (RAG) improves how AI finds and uses information. It's more than just fetching data like standard RAG systems, as this approach involves the agent actively thinking about the information needed.

The agent might ask itself clarifying questions internally or refine its search strategy, and this iterative process helps it self-correct and improve the quality of its final output. It leads to more accurate reasoning and better answers, especially for complex questions.

• Combines information retrieval with active reasoning.

• Iteratively questions and refines its understanding.

• Enables self-correction for improved accuracy.

• Boosts performance in research and complex problem-solving.

4. Context Window

The context window is like an AI agent's short-term memory during a conversation. It defines how much previous interaction the agent can remember and consider. A larger context window allows the agent to recall earlier parts of the dialogue, which helps maintain coherence over longer exchanges.

Agents can refer back to the details mentioned minutes ago. A small context window might cause the agent to forget earlier points quickly. The size impacts the flow and consistency of interactions.

• Determines how much past information the agent retains.

• Larger windows enable longer, more consistent conversations.

• It affects the agent's ability to track dialogue history.

• It is crucial for maintaining coherence in interactions.

5. Hallucinations

Hallucinations occur when an AI agent confidently states incorrect information, essentially making things up and presenting falsehoods as facts. This is a significant issue, especially in applications requiring accuracy.

For example, an agent might invent historical dates or misquote sources. Developers work hard to reduce hallucinations through better training data and techniques. Addressing this problem is important for building trustworthy AI systems, as users need assurance that agent outputs are reliable.

• AI generates incorrect or fabricated information.

• Presents false statements as factual.

• A major challenge for AI reliability.

• Mitigation is key for trustworthy applications.

6. Chain-of-Thought (CoT) Prompting

Chain-of-thought (CoT) prompting guides an AI agent to think step-by-step. Instead of just giving a definitive answer, the agent explains its reasoning process. This technique helps improve logical thinking and problem-solving abilities.

It's particularly useful for complex tasks requiring multiple steps. By outlining its "thought" process, the agent often arrives at more accurate conclusions. This method makes the AI's decision-making more transparent. Users can follow the logic used to reach the result.

• Encourages step-by-step reasoning from the AI.

• Improves logical problem-solving abilities.

• It makes the AI's thinking process transparent.

• Useful for complex, multi-stage tasks.

7. Function Calling

Function calling allows an AI agent to interact with external tools or systems, moving beyond just generating text. The agent can query databases, use software APIs, or trigger automation scripts.

For example, an AI agent could check real-time flight prices using an airline's API or update a customer record in a CRM system. This ability lets agents perform practical actions in the real world. It connects language understanding to tangible task execution.

• Enables agents to use external tools and APIs.

• Allows interaction with databases and software systems.

• Connects language processing to real-world actions.

• Moves agent capabilities beyond text generation.

8. Agent Framework

An agent framework provides the underlying structure for building AI agents, offering pre-built components, tools, and libraries for developers. These frameworks simplify the creation of agents with reasoning and action capabilities.

Examples like LangChain or Google's ADK provide memory, planning, and tool use modules. Using a framework speeds up development and encourages standard practices. It gives builders a starting point rather than making everything from scratch.

• Provides tools and libraries for building agents.

• Simplifies the development of reasoning and action capabilities.

• Includes components for memory, planning, and tools.

• Accelerates agent creation and promotes standards.

9. Agentic Workflow

An agentic workflow defines a structured process for how an AI agent tackles tasks. The agent autonomously plans the necessary steps to reach a goal and executes these steps, potentially using different tools or sub-agents.

The workflow might include loops for refining results or handling errors. This approach allows agents to manage complex projects with minimal human guidance, enabling responsive, goal-focused automation across multiple stages.

• Structured process for autonomous task completion.

• The agent plans, executes, and refines steps independently.

• It often involves multiple stages and tool usage.

• Enables complex, goal-directed automation.

10. Simple Reflex Agents

Simple reflex AI agents are the most basic type of AI agents. They react solely based on the present situation they perceive, and their behavior is governed by predefined rules (condition-action rules).

If a certain condition is met, they perform a specific action. These agents have no memory of past events. They cannot consider history when making decisions. Think of a thermostat turning on heat when the temperature drops below a set point.

• Act only based on current perception.

• Use predefined condition-action rules.

• Have no memory of past events.

• Suitable for simple, reactive tasks.

11. Model-Based Reflex Agents

Model-based reflex agents are smarter than simple reflex agents, as they maintain an internal "model" or understanding of how the world works. This model helps them track changes even when they can't directly see everything.

They can consider past events stored in their internal state, allowing for more informed decisions in partially observable environments. For example, self-driving cars need a model to track unseen nearby vehicles.

• Maintain an internal model of the environment.

• Can handle partially observable situations.

• Consider past states when making decisions.

• More sophisticated than simple reflex agents.

12. Goal-Based Agents

Goal-based AI agents are designed with specific objectives in mind. They focus on achieving these desired states or outcomes. These agents plan sequences of actions to reach their goals and might need to consider different paths and choose the most effective one.

Many business process automation agents fall into this category. For example, an agent aiming to schedule a meeting needs to find available times for all attendees.

• Focused on achieving specific, predefined goals.

• Plan sequences of actions to reach objectives.

• Consider different paths to achieve the goal.

• Common in business automation applications.

13. Utility-Based Agents

Utility-based agents go beyond just achieving goals; they aim for the best possible outcome. They consider different potential states based on a "utility" function. This function measures how desirable or successful a state is.

The agent chooses actions expected to yield the highest utility score, which is useful when goals alone aren't enough, like selecting the fastest and cheapest delivery route. They seek optimal results based on defined preferences.

• Aim to maximize a "utility" or success measure.

• Evaluate outcomes based on desirability.

• Choose actions leading to the best-expected result.

• Useful for optimization problems with trade-offs.

14. Learning Agents

Learning AI agents can improve their performance through experience as they adapt their behavior based on feedback from their actions and outcomes. These agents can operate in changing environments and learn new strategies.

Machine learning techniques are core to their function. A spam filter that gets better at identifying junk mail as it sees more examples is a learning agent. Continuous improvement is their defining characteristic.

• Improve performance through experience and feedback.

• Adapt behavior based on past actions and outcomes.

• Utilize machine learning techniques.

• Suitable for dynamic or evolving environments.

15. Hierarchical Agents

Hierarchical agents tackle complex problems by breaking them down and organizing tasks into a hierarchy of sub-tasks. A main agent might delegate simpler tasks to specialized sub-agents. This structure allows for efficient management of intricate decision-making processes.

Think of a project manager agent coordinating researcher, writer, and editor agents. This layered approach mirrors how complex organizations often work.

• Break down complex tasks into simpler sub-tasks.

• Organize tasks in a nested hierarchy.

• Often involving delegation to specialized sub-agents.

• Efficiently manage layered decision-making.

16. Multi-Agent Systems (MAS)

A multi-agent system (MAS) consists of multiple AI agents interacting within a shared environment. These agents might cooperate to achieve a common goal. They could also compete for resources or coordinate their actions.

Each agent typically has its own objectives and capabilities. Learning MAS helps understand complex emergent behaviors from agent interactions. Examples include simulated economies or coordinated robot teams.

• Multiple agents interact in a shared environment.

• Agents may cooperate, compete, or coordinate.

• Each agent often has individual goals/capabilities.

• Used to model complex, distributed systems.

17. Temperature

Temperature is a setting used during AI text generation, as it controls the randomness or creativity of the agent's responses. A low-temperature value makes the output more focused and predictable. The agent tends to choose the most likely words.

A high temperature increases randomness, leading to more diverse and sometimes creative outputs. Adjusting the temperature helps balance predictability with novelty in the generated text.

• Controls randomness in AI text generation.

• Low temperature yields predictable, focused output.

• High temperature produces diverse, creative output.

• Balances consistency versus novelty.

18. Role-Based Agents

Role-based agents are assigned specific functions within a larger system, similar to job roles in a human team, where each agent specializes. Examples include agents designated as Researchers, coders, or executors.

This division of labor allows for more focused and efficient task handling. A complex problem can be tackled by coordinating agents with distinct expertise. This approach is common in multi-agent systems designed for specific workflows.

• Assigned specific functions like Researcher or Coder.

• Specializes in a particular task or area.

• Enables division of labor within a system.

• Common in structured multi-agent workflows.

19. Memory Types

AI agents can possess different kinds of memory to store information.

1. Short-term memory holds recent interactions, similar to the context window.

2. Long-term memory stores persistent knowledge or learned facts.

3. Semantic memory relates to general world knowledge (e.g., "Paris is in France").

4. Procedural memory involves knowing how to perform tasks or sequences of actions.

Having varied memory types allows agents to retain and use diverse information effectively.

• Includes short-term (recent interactions) and long-term (persistent knowledge).

• Semantic memory stores general facts.

• Procedural memory stores how-to knowledge.

• Enables retention and use of different information types.

20. Episodic Memory

Episodic memory specifically stores past events and interactions the agent has experienced, similar to the agent's personal history log, allowing the agent to recall specific past dialogues or occurrences.

For example, it might remember a user's previous request or preference. This type of memory helps to personalize interactions. It provides context based on specific past experiences, not just general knowledge.

• Stores specific past events and interactions.

• Functions as the agent's personal experience log.

• Enables recall of particular past dialogues or actions.

• It helps personalize interactions based on history.

21. Planner-Executor Pattern

The Planner-Executor pattern is a common structure for agent action. One part of the agent (the Planner) creates a string of steps to achieve the particular goal. Another part (the Executor) then carries out those planned steps

.

This separation helps organize complex tasks where the planner focuses on high-level strategy and the executor handles the low-level details of performing actions. This pattern provides a clear division of responsibilities within an agent.

• Separates task planning from task execution.

• The planner component creates the sequence of steps.

• The executor component carries out the planned actions.

• Organizes complex task management within an agent.

22. Tool-centric vs. Model-centric Agents

Tool-centric and Model-centric agents describe a difference in agent design philosophy. Tool-centric agents primarily rely on using external tools (like APIs or databases) to perform actions. Their main job is selecting and orchestrating these tools.

Model-centric agents rely more heavily on the internal knowledge and capabilities of their underlying language model. They generate responses or solutions directly from the model's understanding. Many modern agents blend both approaches effectively.

• Tool-centric agents focus on using external tools/APIs.

• Model-centric agents rely more on the core AI model's knowledge.

• Describes a focus difference in agent design.

• Many practical agents combine both approaches.

23. Feedback Loop

A feedback loop allows an agent to learn and improve from its experiences. The agent performs an action and observes the outcome or receives user feedback. This information is then used to adjust its future behavior.

This cycle of action, outcome, and adjustment is necessary for learning agents as it allows them to adapt to new situations or refine their strategies. Positive feedback reinforces good actions, while negative feedback discourages poor ones.

• The agent learns from outcomes or user responses.

• Information from actions adjusts future behavior.

• Enables adaptation and performance improvement.

• The core mechanism for learning agents.

24. ReAct (Reason + Act)

ReAct is a specific technique that combines reasoning and acting within an agent's workflow. The agent alternates between thinking about the problem (reasoning) and taking action (acting).

For example, it might reason about what tool to use next, then act by calling that tool. The results of the action then feed back into the next reasoning step. This interplay helps agents tackle complex tasks requiring intermediate steps and tool use.

• Combines reasoning steps with action steps.

• The agent alternates between thinking and doing.

• Action results inform subsequent reasoning.

• Effective for tasks needing intermediate steps and tool use.

25. Agent Loop

The Agent Loop describes the fundamental cycle of operation for many AI agents. It typically involves several stages repeated continuously.

1. The agent perceives its environment to gather information.

2. It then plans its next action based on its goals and current understanding.

3. Next, it acts upon the environment.

4. Finally, it might Learn from the outcome, updating its internal state or model.

This Perceive-Plan-Act-Learn cycle drives the agent's ongoing behavior.

• Fundamental operational cycle: Perceive-Plan-Act-Learn.

• The agent continuously senses, decides, acts, and updates.

• Drives the agent's ongoing interaction with its environment.

• Forms the basic rhythm of agent behavior.

Conclusion:

The field of AI agents is rich with specialized concepts and terminology. Learning these 25 terms can give you a solid foundation to understand the fundamental points of AI agents and Agentic AI, from basic definitions like "AI Agent" to specific techniques like "Agentic RAG. This knowledge helps businesses and technologists understand, evaluate, and implement these powerful tools. These concepts will also remain important in the long term as we integrate AI agents and agentic AI into various processes.