Terminology of Agentic AI: A Beginner’s Glossary.

Agentic AI is transforming how businesses operate, but its terminology can be overwhelming for newcomers. This glossary provides clear definitions of essential concepts to help you navigate conversations about AI agents, their capabilities, and their business applications.

What is Agentic AI?

Agentic AI refers to artificial intelligence systems designed to act independently on behalf of users to accomplish specific goals. Unlike traditional AI that responds only to direct commands, agentic AI can take initiative, make decisions within defined parameters, and execute complex tasks with minimal human supervision.

These systems represent the evolution from passive, reactive AI tools to proactive digital assistants that can understand context, plan sequences of actions, and adapt to changing circumstances. The key distinction is their ability to maintain persistent “agency” – the capacity to act autonomously toward defined objectives over time.

Core Terminology

Agent

An agent is an AI system designed to perceive its environment through sensors, process this information, and take actions to achieve specific goals. In agentic AI, agents are typically software entities that can interact with digital systems, data sources, and sometimes physical devices through appropriate interfaces.

Unlike simple automation tools, agents can make decisions based on their programming, learned patterns, and environmental feedback. They maintain an ongoing “understanding” of their tasks and progress, allowing them to adjust strategies as needed.

Autonomy

Autonomy describes the degree to which an AI agent can operate independently without human intervention. Autonomous agents can make decisions and take actions based on their programming, available data, and environmental conditions without requiring step-by-step human guidance.

The level of autonomy can vary significantly between systems. Limited autonomy might involve an agent that requires human approval for important decisions, while high autonomy could mean an agent that handles complex workflows entirely independently, only alerting humans to exceptions or completed tasks.

Agent Architecture

Agent architecture refers to the internal design and organization of an AI agent’s components. This includes its perception mechanisms (how it gathers information), reasoning systems (how it processes information and makes decisions), and action modules (how it implements decisions).

Common architectural approaches include:

• Reactive architectures: Directly map perceptions to actions without complex internal reasoning
• Deliberative architectures: Maintain internal models and plan sequences of actions
• Hybrid architectures: Combine reactive and deliberative elements for both responsiveness and planning

Reinforcement Learning (RL)

Reinforcement learning is a machine learning paradigm where agents learn optimal behaviors through trial and error, receiving rewards or penalties based on their actions. In RL, agents develop policies (action strategies) that maximize cumulative rewards over time.

This approach is particularly valuable for agentic AI because it enables systems to improve through experience without explicit programming for every possible scenario. RL-trained agents can discover novel solutions and adapt to changing conditions in ways their creators might not have anticipated.

Multi-Agent Systems

Multi-agent systems involve multiple AI agents operating in a shared environment, often collaborating or competing to achieve goals. These systems can distribute complex tasks across specialized agents, each handling different aspects of a larger process.

The interactions between agents create emergent behaviors and capabilities beyond what any single agent could accomplish. Multi-agent architectures are particularly valuable for complex business processes that span multiple systems, departments, or areas of expertise.

Learning and Adaptation

Transfer Learning

Transfer learning is a technique where knowledge gained while solving one problem is applied to a different but related problem. For agentic AI, this means agents can leverage experience from previous tasks to perform better on new ones, even with limited training data.

This capability significantly reduces the resources required to deploy agents for new use cases, as they don’t need to learn everything from scratch. For businesses, transfer learning translates to faster deployment and greater versatility from their AI investments.

Few-Shot Learning

Few-shot learning enables AI systems to learn new tasks or concepts from just a few examples, rather than requiring extensive training data. This capability is particularly valuable for agentic AI in specialized business contexts where large datasets may not be available.

Agents with few-shot learning capabilities can quickly adapt to new scenarios, customer needs, or business requirements with minimal additional training, making them more responsive to changing business conditions.

Continuous Learning

Continuous learning refers to an agent’s ability to update its knowledge and skills over time based on new experiences and feedback. Rather than remaining static after initial training, continuously learning agents improve through ongoing interactions.

This capability is crucial for maintaining agent effectiveness in dynamic business environments where conditions, requirements, and best practices evolve. It also reduces maintenance costs by allowing agents to adapt to changes without requiring complete retraining.

Agent Capabilities

Planning

Planning refers to an agent’s ability to develop sequences of actions that will achieve desired goals. This involves reasoning about cause and effect, predicting outcomes, and optimizing paths to objectives.

Advanced planning capabilities allow agents to handle complex multi-step tasks, manage contingencies, and efficiently allocate resources. In business contexts, planning enables agents to orchestrate workflows, coordinate activities, and optimize processes without constant human guidance.

Reasoning

Reasoning encompasses an agent’s ability to draw logical conclusions, make inferences, and solve problems based on available information. This includes deductive reasoning (applying general rules to specific situations), inductive reasoning (identifying patterns to form general principles), and abductive reasoning (generating likely explanations for observations).

Strong reasoning capabilities allow agents to handle ambiguity, work with incomplete information, and make sound decisions in complex scenarios – all crucial for business applications where perfect information is rarely available.

Memory

In agentic AI, memory refers to systems that allow agents to retain and utilize information from past interactions and experiences. This includes both short-term memory for immediate context and long-term memory for persistent knowledge.

Effective memory systems enable agents to maintain contextual awareness across extended interactions, learn from experience, and provide personalized service based on historical data. For businesses, this translates to more coherent customer experiences and more effective task execution over time.

Tool Use

Tool use describes an agent’s ability to leverage external software, APIs, databases, and other resources to extend its capabilities. Rather than being limited to built-in functions, tool-using agents can interact with specialized systems to access information or perform actions beyond their core programming.

This capability dramatically expands what agents can accomplish, allowing them to interface with existing business systems, access specialized knowledge bases, or utilize computational resources as needed for specific tasks.

Implementation Concepts

Large Language Models (LLMs)

Large Language Models are neural networks trained on vast text corpora to understand and generate human language. While not exclusively for agentic AI, LLMs like GPT-4, Claude, and PaLM provide the linguistic understanding and generation capabilities that power many current agent implementations.

LLMs enable agents to interpret natural language instructions, generate appropriate responses, and communicate effectively with humans and other systems. They serve as both the interface through which humans direct agents and often as core components of agent reasoning systems.

Prompt Engineering

Prompt engineering is the practice of designing effective instructions and context for AI systems, particularly LLMs. In agentic AI, well-crafted prompts define agent behaviors, constraints, objectives, and personality.

Effective prompt engineering is crucial for agent performance, ensuring systems understand their purpose, operate within appropriate boundaries, and produce consistent, high-quality outputs. For businesses implementing agentic AI, prompt engineering represents a key skill for tailoring agents to specific use cases.

Fine-Tuning

Fine-tuning refers to the process of adapting pre-trained AI models (like LLMs) to specific domains or tasks through additional training on specialized datasets. This process allows general-purpose models to develop expertise in particular business contexts or functions.

Fine-tuned models can better understand industry-specific terminology, follow domain-specific conventions, and solve specialized problems more effectively than general models. For businesses, fine-tuning represents an important pathway to developing agents with relevant expertise.

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation combines information retrieval with text generation to enhance AI outputs with relevant, up-to-date information. RAG systems search for pertinent information from specified knowledge bases and incorporate this information into their responses.

This approach is particularly valuable for agentic AI in business contexts, allowing agents to access company documentation, product information, policy guidelines, and other proprietary knowledge that wasn’t included in their training data.

Ethical and Safety Concepts

Alignment

Alignment refers to ensuring AI systems act in accordance with human values, intentions, and expectations. For agentic AI, alignment involves designing agents that reliably pursue the goals their creators intended, without developing harmful or unexpected behaviors.

As agents become more autonomous, alignment becomes increasingly critical to ensure they remain beneficial tools rather than sources of disruption or harm. Businesses implementing agentic AI must consider alignment as a fundamental design requirement, not an optional feature.

Containment

Containment encompasses strategies for limiting an agent’s capabilities, access, or impact to prevent potential harm. This includes restricting what systems an agent can interact with, what actions it can take, and what resources it can access.

Effective containment balances agent utility with safety considerations, allowing sufficient freedom for agents to provide value while preventing them from causing damage through errors, misunderstandings, or exploitation. For businesses, containment represents a key risk management strategy.

Explainability

Explainability refers to an AI system’s ability to communicate its reasoning, decisions, and actions in terms humans can understand. Explainable agents can articulate why they took specific actions, what factors influenced their decisions, and how they arrived at particular conclusions.

This capability is essential for building trust, enabling effective oversight, and meeting regulatory requirements. In business contexts, explainability allows stakeholders to validate agent behaviors and intervene appropriately when necessary.

Value Alignment

Value alignment focuses specifically on ensuring AI systems act in accordance with human ethical principles and social norms. This goes beyond functional alignment (doing what was explicitly instructed) to include avoiding harmful, deceptive, or manipulative behaviors even when not explicitly forbidden.

For businesses implementing agentic AI, value alignment is crucial for maintaining brand reputation, customer trust, and regulatory compliance. It requires careful consideration of potential impacts on stakeholders and society at large.

Business Implementation

Agent Orchestration

Agent orchestration refers to the coordination and management of multiple AI agents working together on complex tasks or workflows. This includes defining how agents communicate, share information, allocate responsibilities, and resolve conflicts.

Effective orchestration enables businesses to decompose complex processes into manageable components, each handled by specialized agents with appropriate capabilities. This approach improves scalability, maintainability, and performance for enterprise AI implementations.

Human-in-the-Loop

Human-in-the-loop systems combine AI automation with human oversight and intervention. These hybrid approaches maintain human control over critical decisions while leveraging AI for efficiency, consistency, and scalability.

This model is particularly valuable for high-stakes business processes where errors could have significant consequences. It allows organizations to capture the benefits of agentic AI while maintaining appropriate human judgment and accountability where needed.

Supervised Autonomy

Supervised autonomy describes systems where agents operate independently within defined parameters but remain subject to human oversight and intervention. This approach balances efficiency with control by allowing agents to handle routine aspects of tasks while escalating exceptions or critical decisions to human supervisors.

For businesses, supervised autonomy provides a practical middle ground between fully manual processes and complete automation, offering significant efficiency gains while maintaining appropriate human guidance for complex or sensitive matters.

Agentic Workflows

Agentic workflows are business processes where multiple steps are coordinated and executed by AI agents, either independently or in collaboration with human workers. These workflows can span multiple systems, departments, and timeframes, maintaining continuity and context throughout.

By implementing agentic workflows, businesses can achieve greater consistency, 24/7 operation, and detailed process tracking while freeing human workers to focus on high-value activities requiring creativity, judgment, or emotional intelligence.

Conclusion

This glossary provides a foundation for understanding the key concepts in agentic AI, but the field continues to evolve rapidly. As these technologies mature, new terminology and approaches will emerge, expanding the possibilities for business applications.

For organizations exploring agentic AI implementation, understanding these concepts is just the beginning. The next steps involve identifying appropriate use cases, developing implementation strategies, and establishing governance frameworks to ensure these powerful tools deliver value while managing associated risks.

By building literacy in agentic AI terminology across your organization, you’ll enable more productive discussions about potential applications, implementation approaches, and expected outcomes – ultimately positioning your business to capture the full value of this transformative technology.