Agentic AI Glossary
Concise, industry-standard definitions for enterprise artificial intelligence, agent orchestration, and business automation workflows.
Agentic AI
An advanced class of artificial intelligence systems designed with goal-oriented autonomy. Instead of waiting for turn-by-turn prompts, Agentic AI plans steps, invokes tools, handles execution errors, and works toward complex objectives autonomously.
AI Agent
An autonomous software entity powered by a foundation model that acts on behalf of a user or system. It perceives inputs, plans actions, utilizes tools, and generates structured outputs to achieve localized goals.
AI Agent Orchestration
The software infrastructure and coordination layer that directs multiple AI agents. It handles state management, persistent memory stores, routing paths, error-recovery loops, and human-in-the-loop validation checkpoints.
Multi-Agent System
A network of specialized AI agents working collaboratively. Each agent is assigned distinct prompts, contexts, and tools (e.g., writer, editor, auditor) and passes intermediate results to others to optimize complex outputs.
Tool Calling
The mechanism by which an AI model generates structured arguments to invoke external code, query databases, or execute APIs. The model describes its intent programmatically, allowing the orchestration wrapper to perform the action.
Retrieval-Augmented Generation
Commonly known as RAG. A pattern that enhances LLM generation by querying external databases, vector indices, or corporate file stores for relevant context prior to prompting the model, eliminating outdated info errors.
Human-in-the-loop
A system design pattern that inserts human checkpoints into autonomous loops. Critical or high-risk tasks (like ledger edits or system wire transfers) are paused until an authorized human user reviews and approves the step.
Agent Memory
The persistence layer for AI agents. Short-term memory retains context within a single task session, while long-term memory leverages vector databases to reference historical sessions and past outcomes.
Agent Evaluation
The process of auditing and benchmarking AI agent behavior, reasoning steps, tool selections, and output accuracy against test datasets to prevent drift or regressions.
Workflow Automation
The practice of using software triggers and scripts to automate multi-step business processes. In an agentic context, this integrates language models to handle unstructured data variables dynamically.
AI Copilot
An interactive, in-app assistant that aids users in completing tasks (such as drafting text, summarizing files, or writing code) by providing suggestions and inline actions within a human-driven session.
AI-Native Business Process
A business process designed from the ground up to be executed primarily by autonomous AI agents, eliminating legacy paper/bottleneck steps in favor of continuous API-level orchestration and human auditing.
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