AI agents are transforming from mere tools into collaborative partners. But as this shift accelerates, so does the complexity of terminology surrounding these digital assistants. Let’s unpack the 15 essential terms you need to understand the rapidly evolving landscape of AI agents.
1. Large Language Models (LLMs)
At the foundation of modern AI agents lie Large Language Models — sophisticated AI systems trained on vast amounts of text data to understand and generate human-like content. These digital powerhouses typically exceed 72 billion parameters, giving them the ability to handle complex tasks, understand nuanced requests, and generate remarkably coherent responses.
LLMs serve as the “brains” behind many AI agents, providing the reasoning capabilities and linguistic understanding that make human-AI collaboration possible. Their extensive training across diverse datasets enables them to handle everything from creative writing to technical problem-solving with impressive versatility.
2. Small Language Models (SLMs)
While LLMs get much of the attention, Small Language Models play an equally important role in the AI ecosystem. With fewer than 72 billion parameters, SLMs offer a more efficient alternative for many real-world applications. Their more compact design makes them:
- Faster to deploy
- Less resource-intensive
- More cost-effective for specific tasks
- Easier to fine-tune for specialized domains
SLMs demonstrate that bigger isn’t always better — sometimes a more focused, efficient model can deliver superior results, especially when operating under real-world constraints like limited computing resources or response time requirements.
3. Tokens: The Currency of AI Communication
To understand how AI agents process information, you need to understand tokens — the fundamental units of text that language models work with. A token could be:
- A complete word
- Part of a word
- A single character or punctuation mark
- A special symbol
When you interact with an AI agent, your text gets broken down into these tokens, processed through the model, and then reassembled into a coherent response. The concept becomes particularly important when working with API-based systems, where pricing often depends on token usage. Efficient prompting that achieves your goals with fewer tokens can significantly impact both performance and cost.
4. Prompts: The Art of Instructing AI
The prompt serves as the primary interface between humans and AI agents — it’s how we communicate our needs, questions, and instructions. But crafting effective prompts has evolved into a sophisticated skill of its own, often called “prompt engineering.”
A well-crafted prompt:
- Provides clear context and constraints
- Specifies the desired format or approach
- Includes relevant examples when helpful
- Anticipates potential misinterpretations
The difference between an average prompt and an excellent one can be dramatic, potentially transforming a vague, unhelpful response into a precise, actionable insight. As AI agents become more integrated into workflows, the ability to “speak their language” through effective prompting becomes an increasingly valuable skill.
5. Context Window: The AI’s Working Memory
The context window represents how much information an AI agent can consider at once — essentially, its working memory. This parameter determines:
- How much of the previous conversation the AI can reference
- The length of documents it can analyze in a single pass
- Its ability to maintain coherence across a complex interaction
As context windows expand (from a few thousand tokens to hundreds of thousands or more), AI agents become capable of more sophisticated analysis, remembering longer conversations, and handling more complex tasks that require integrating information across large bodies of text.
6. Hallucination: When AI Confabulates
Despite their impressive capabilities, AI agents sometimes generate information that is simply incorrect — a phenomenon known as hallucination. These errors can range from minor inaccuracies to completely fabricated facts, citations, or explanations.
Hallucinations occur because language models predict text patterns rather than accessing a definitive source of truth. While they can appear convincingly authoritative, these fabrications highlight the importance of verification and the need for approaches that ground AI responses in reliable information sources.
Understanding when and why hallucinations occur helps users maintain appropriate skepticism while still benefiting from an AI agent’s capabilities. It also drives the development of techniques like RAG (discussed below) that aim to reduce these errors.
7. Temperature: Controlling AI Creativity
The temperature setting serves as a creativity dial for AI outputs, controlling the randomness in their responses:
- High temperature (0.7–1.0): Produces more diverse, creative, and sometimes unexpected outputs — ideal for brainstorming, creative writing, or generating multiple alternatives
- Low temperature (0.1–0.3): Generates more deterministic, focused, and predictable responses — better for factual questions, consistent outputs, or task-specific instructions
This parameter allows users to tune an AI agent’s behavior to match the task at hand — whether that requires factual precision or creative exploration. Finding the right temperature for different use cases becomes an important aspect of effectively working with AI agents.
8. Retrieval-Augmented Generation (RAG)
RAG represents a significant advancement in making AI agents more reliable and factually grounded. This approach combines the generative capabilities of language models with information retrieval systems that can access verified data sources.
When using RAG, an AI agent:
- Analyzes the query to understand what information is needed
- Retrieves relevant information from trusted sources
- Uses this information to generate a response that’s both helpful and factually accurate
This hybrid approach helps mitigate hallucinations by anchoring responses in verified information, making RAG particularly valuable for applications where accuracy is crucial — from customer support to research assistance.
9. Chain-of-Thought Prompting
For complex reasoning tasks, Chain-of-Thought Prompting encourages AI agents to break down their thinking into explicit steps. Rather than jumping straight to a conclusion, the agent walks through its reasoning process, making each step transparent and verifiable.
This technique:
- Improves accuracy on complex problems
- Makes the AI’s reasoning process inspectable
- Helps identify where errors might occur
- Creates more trustworthy and explainable outputs
By making thinking explicit, Chain-of-Thought approaches help bridge the gap between the black-box nature of large models and the need for transparency in critical applications.
10. Function Calling
Function Calling extends an AI agent’s capabilities beyond text generation, allowing it to interact with external tools, APIs, and systems. This capability transforms AI from a conversational partner into an actor that can take concrete actions in the digital world.
With Function Calling, AI agents can:
- Search databases for specific information
- Update records in external systems
- Trigger workflows or automations
- Interact with other software tools and services
This bridging capability is what enables many of the most powerful applications of AI agents in business contexts, where integration with existing systems and processes is essential.
11. AI Agents: Purpose-Driven AI Systems
While the term “AI agent” encompasses various implementations, at its core, an agent is a software program that uses artificial intelligence to interact with its environment, gather information, and perform specific tasks autonomously.
Unlike passive models that simply respond to queries, agents actively work toward defined goals. They can:
- Take initiative based on their programming
- Learn from their interactions
- Adapt to changing circumstances
- Persist in achieving objectives
This goal-directed behavior is what distinguishes true agents from more basic AI applications and marks a significant step toward more autonomous and capable AI systems.
12. Agent Framework: The Foundation for Building AI Agents
An Agent Framework provides the essential architecture and components needed to create functional AI agents. These frameworks handle the complex orchestration required for autonomous operation, typically including:
- Planning and reasoning capabilities
- Memory management systems
- Tool integration interfaces
- Error handling and recovery mechanisms
- Communication protocols
Popular frameworks like LangChain, AutoGPT, and others provide developers with the building blocks to create specialized agents without reinventing the wheel, accelerating the development and deployment of practical AI solutions.
13. Agentic Workflow: Intelligent Process Automation
Agentic Workflows represent the next evolution in business process automation. By combining traditional deterministic workflows with AI agents, these systems can handle complex processes that previously required human judgment and intervention.
In an agentic workflow:
- Routine steps may follow predefined paths
- Decision points leverage AI capabilities
- Exceptions can be handled intelligently
- The overall process adapts to changing conditions
This hybrid approach maintains the reliability of traditional automation while incorporating the flexibility and intelligence of AI agents — particularly valuable for knowledge work that requires both consistency and adaptability.
14. Agentic RAG: Proactive Information Retrieval
Building on the RAG concept, Agentic RAG puts the AI in control of the information-gathering process. Rather than following fixed retrieval patterns, the agent actively decides:
- What specific information to look for
- Which sources to prioritize
- How to refine searches based on initial results
- How to synthesize and apply the information gathered
This proactive approach makes the retrieval process more intelligent and targeted, producing more relevant and useful responses, especially for complex queries that might require multiple information-gathering steps.
15. Multi-Agent Systems: AI Teamwork in Action
The most sophisticated AI applications often involve Multiple AI Agents working together to solve complex problems. In these systems, different agents may have specialized roles, knowledge bases, or capabilities — creating a collaborative environment that can tackle challenges beyond the reach of any single agent.
Multi-agent systems might include:
- Research agents that gather and verify information
- Reasoning agents that analyze and draw conclusions
- Creative agents that generate novel solutions
- Critic agents that evaluate and refine outputs
- Coordinator agents that manage the overall process
This division of labor allows for more sophisticated problem-solving approaches that mimic human team dynamics, where different specialists contribute their unique strengths to a common goal.
Why Understanding These Terms Matters
As AI agents become more integrated into our work and lives, familiarity with these concepts isn’t just technical knowledge — it’s becoming an essential literacy for the modern professional. Understanding this terminology helps you:
- Communicate more effectively about AI capabilities and limitations
- Make informed decisions about AI implementation
- Identify opportunities where AI agents can add value
- Maintain appropriate trust and skepticism
- Participate in shaping how these technologies evolve
The organizations and individuals who grasp these concepts will be better positioned to harness the potential of AI agents while navigating their complexities and challenges.
This glossary represents our current understanding, but the field continues to evolve rapidly. New techniques, approaches, and paradigms emerge regularly, building on these foundational concepts.
The most successful adopters of AI agent technology will be those who maintain a learning mindset — staying curious about new developments while building on a solid understanding of these core principles.
As we move forward, the partnership between humans and AI agents will continue to redefine what’s possible in knowledge work, creativity, and problem-solving. By understanding the language of this revolution, you’re taking an important step toward participating in and shaping its future.