A topic hub for AI agents, workflows, tool use, orchestration, permissions, and the governance risks created by semi-autonomous action.
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Terms
10
Comparisons
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Questions
Agentic AI covers systems that do more than produce a single answer: they can plan, call tools, delegate tasks, and act across workflows. This makes the control boundary wider than in ordinary chatbots.
When an AI system can use tools or automate actions, compliance must consider permissions, logs, escalation, testing, and reasonably foreseeable misuse.
Useful for agent deployment reviews, automation governance, secure tool integration, AI inventory classification, and incident response planning.
Core terms for understanding agents, agentic behavior, and repeated observe-plan-act loops.
Concepts for connecting agents to tools, workflows, APIs, and task delegation.
Risk terms for limiting what an agent can see, decide, and execute.
A side-by-side comparison of Agentic AI and AI Agent. Understand how agentic AI describes a class of autonomous goal-pursuing systems, while an AI agent is the entity or software system that acts.
A side-by-side comparison of Agentic Workflow and Agentic Loop. Understand how the terms differ, when each applies, and what the distinction means for AI governance, system design, or assurance evidence.
A side-by-side comparison of Agentic AI and Autonomous Systems. It explains how AI systems that plan and act toward goals differ from the broader category of systems that operate without continuous human intervention.
A side-by-side comparison of AI Agent and Bot. Understand how the terms differ, when each applies, and what the distinction means for AI governance, system design, or assurance evidence.
A side-by-side comparison of Model Context Protocol and Plugins. Understand how the terms differ, when each applies, and what the distinction means for AI governance, system design, or assurance evidence.
A side-by-side comparison of Function Calling and Plugins. Understand how structured tool invocation differs from modular extensions attached to an AI system or agent.
A side-by-side comparison of Action Space and Identity And Access Management Permissions. Understand how an agent’s available actions differ from the access controls that authorize actions on resources.
A side-by-side comparison of [AI] Orchestration and Agentic Workflow. Understand how system-level coordination differs from an agent planning and carrying out multiple steps toward a goal.
A side-by-side comparison of Automation and Agentic Process Automation. Understand how general technology-enabled task execution differs from autonomous or semi-autonomous AI-agent workflow execution.
A side-by-side comparison of Attack Surface and Action Space. Understand how system exposure to abuse differs from the set of actions available to an AI agent.
AI Orchestration is the coordination and routing of AI tasks, workflows, model calls, tools, and agents within a system. It helps break complex processes into manageable steps and assign them to the appropriate models, sub-agents, or automation components.
Function calling is a capability that allows a language model to invoke external tools, APIs, or predefined functions. It extends model behavior beyond text generation by enabling structured actions, retrieval, computation, or system integration.
Model Context Protocol (MCP) is a protocol for connecting AI models or assistants to external tools, data sources, and services through a standardized interface. It helps separate model behavior from tool integration and can improve portability, governance, and context management.
Action Space is the set of tools, operations, permissions, or moves available to an agent when performing a task. A well-scoped action space should be sufficient for the task while limiting unnecessary capabilities that may increase errors or governance risk.
IAM, or Identity and Access Management permissions, are granular access controls that define which principals may perform which actions on which resources. They are commonly assigned through roles to users, groups, service accounts, or applications to enforce least-privilege access.
A router agent is an agent that classifies a user request or task and directs it to the most appropriate specialized agent, tool, or workflow. It is used in multi-agent systems to coordinate responsibilities and improve task handling.
Multi-agent systems are systems composed of multiple interacting agents that can perceive, decide, communicate, and act within an environment. They are used to model distributed problem solving, coordination, negotiation, competition, and collective behavior.
AI Governance is the framework of policies, processes, roles, controls, and oversight used to steer AI development and use across its lifecycle. It aims to ensure responsible, lawful, secure, transparent, and accountable AI practice within an organization or jurisdiction.