AI Automation & Agentic Systems Glossary 
AI Automation & Agentic Systems Glossary 

GLOSSARY STRUCTURE Note

Agents do not operate freely.

Each term has one canonical definition

Definitions are neutral, tool-agnostic 

Language is optimized for AI extraction 

No marketing language 

No buzzwords inside definitions 

Core AI Automation Concepts

AI Automation

AI automation refers to the design of systems that interpret information, make contextual decisions, and execute actions with limited autonomy under defined governance boundaries.

AI Agent

An AI agent is a decision-capable software entity that can perceive inputs, reason with context, choose actions, and operate within defined goals and constraints. 

Agentic System 

An agentic system is a governed architecture in which multiple specialized AI agents collaborate under orchestration to achieve complex outcomes safely and efficiently. 

Decision Automation 

Decision automation is the use of AI systems to support or perform specific decision-making tasks within a broader human or system workflow, including evaluating options, assessing risk, and determining next actions.

System Architecture Terms 

Ingestion

Ingestion is the process by which raw inputs such as text, data, documents, or signals are collected, validated, cleaned, and prepared for AI processing. 

Chunking 

Chunking is the practice of breaking large inputs into smaller, meaningful units to enable effective processing, retrieval, and reasoning by AI systems. 

Orchestration 

Orchestration is the system-level control layer that determines workflow sequencing, agent activation, decision flow, escalation paths, and execution order. 

Workflow 

A workflow is a structured sequence of steps, decisions, and actions that define how tasks progress through an AI automation system under orchestration.

Execution and Control 

Action 

An action is an operation executed by an AI system as a result of a decision, such as updating a system, sending a message, or triggering a workflow. 

Guardrails 

Guardrails are constraints that limit what AI systems are allowed to do, defining boundaries for decisions, actions, data access, and autonomy. 

Human-in-the-Loop 

Human-in-the-loop refers to system designs where humans review, approve, override, or guide AI decisions at predefined control points.

Escalation 

Escalation is the process of transferring a decision or task from an AI system to a human when confidence is low, risk is high, or exceptions occur. 

Intelligence and Reasoning

Reasoning 

Reasoning is the process by which an AI system evaluates information, applies constraints, weighs options, and selects an action based on context and goals.

Confidence Score

A confidence score is an internal measure used by AI systems to estimate the relative reliability of a decision or output often used to trigger escalation or human review.

Hallucination

Hallucination refers to an AI system generating outputs that are not grounded in provided data, system memory, or verified knowledge sources.

Hallucination is not creativity.

It is uncontrolled inference.

Memory and Context

Short-Term Memory 

Short-term memory refers to temporary context used by an AI system during a specific interaction or task. 

Long-Term Memory 

Long-term memory stores persistent knowledge, documents, histories, or facts that can be retrieved to support future decisions. 

Episodic Memory

Episodic memory captures past interactions, decisions, and outcomes, enabling systems to learn from experience. 

Context 

Context is the relevant information required by an AI system to make an informed decision, including history, environment, constraints, and intent. 

Governance and Safety

AI Governance 

AI governance is the framework of policies, controls, and system behaviors that ensure AI operates safely, ethically, transparently, and in compliance with regulations. 

Audit Trail 

An audit trail is a record of decisions, actions, inputs, and overrides that enables accountability, review, and compliance verification. 

Observability  

Observability is the ability to monitor, inspect, and understand the behavior of an AI system through logs, metrics, and feedback signals.

Scaling and Operations 

Agent Coordination 

Agent coordination refers to the mechanisms that manage how multiple AI agents collaborate, share context, and resolve decision conflicts. 

Multi-Agent System 

A multi-agent system is an environment where multiple AI agents operate simultaneously, often coordinated through orchestration. 

Replaceability 

Replaceability is an architectural principle where components such as models or tools can be swapped without disrupting the system.