AI Glossary: AI Agents

_INTRODUCTION

Autonomous AI systems are reshaping how organizations handle complex tasks. Understanding the core concepts behind AI agents is essential for leaders evaluating AI investments and deployment strategies.

Agent

An agent is an AI system capable of perceiving its environment, making decisions, and taking actions to achieve specific objectives without constant human intervention. In business contexts, agents automate decision-making processes by analyzing data, evaluating options, and executing tasks autonomously. This capability enables organizations to scale operations, reduce human error, and focus human expertise on higher-value work.

Multi-agent System

A multi-agent system consists of multiple autonomous AI agents working together, each with specialized capabilities, to solve complex problems collaboratively. These systems distribute tasks across agents that communicate and coordinate with one another, improving resilience and problem-solving capacity. For enterprises, multi-agent architectures enable parallel processing of diverse business challenges while maintaining consistent governance across the organization.

Planner

A planner is an AI component that decomposes high-level objectives into sequential steps or strategies, determining the optimal path to achieve goals. Planners reason about dependencies, resource constraints, and potential outcomes to create executable action sequences. In business applications, planners ensure AI systems approach complex tasks methodically, improving decision quality and enabling transparency in AI reasoning.

Memory

Memory refers to an agent's capacity to retain, recall, and learn from past interactions, decisions, and outcomes to inform future behavior. Effective memory systems allow agents to improve performance over time and maintain context across extended interactions. For organizations, agent memory enables personalized customer experiences, institutional knowledge retention, and continuous improvement of business processes.

State

State represents the current condition or configuration of an AI agent at any given moment, including its goals, available resources, and environmental context. Maintaining accurate state is critical for agents to make coherent decisions and coordinate actions in dynamic environments. Proper state management ensures reproducibility, traceability, and auditability of AI system behavior for regulatory compliance and performance monitoring.

Observation

Observation is the process by which an agent perceives and collects information about its environment and the outcomes of previous actions. Accurate observations enable agents to understand results and adjust behavior accordingly. In business systems, reliable observation mechanisms ensure agents respond appropriately to changing conditions and provide clear feedback loops for human oversight.

Execution Loop

An execution loop is the continuous cycle in which an agent observes its environment, processes information, decides on actions, and executes those actions repeatedly. This iterative process allows agents to refine strategies based on feedback and adapt to new information. For enterprises, robust execution loops enable reliable automation of mission-critical processes while maintaining safety through monitoring and intervention points.

Task Decomposition

Task decomposition is the process of breaking down complex objectives into smaller, manageable subtasks that agents can execute sequentially or in parallel. This approach improves problem-solving efficiency and clarity by creating explicit step-by-step execution paths. Strategic leaders value decomposition because it makes AI systems more explainable, verifiable, and easier to debug when issues arise.

Tool Calling

Tool calling enables AI agents to invoke external software, APIs, databases, or services to execute tasks beyond their native capabilities. Rather than attempting all tasks internally, agents dynamically select and use appropriate tools based on their needs. For organizations, tool calling creates ecosystem integration capabilities, allowing AI agents to seamlessly access business systems, data sources, and specialized services.

Autonomous Workflow

An autonomous workflow is a complete end-to-end process where AI agents handle all decision-making and execution with minimal human intervention. These workflows combine agents, memory, planning, and tool calling to deliver business outcomes automatically. Organizations implementing autonomous workflows achieve significant efficiency gains, cost reduction, and improved consistency compared to manual processes.