Agentic System

An agentic system is an AI system built around one or more AI agents that autonomously pursues goals through reasoning, planning, and tool use. While an AI agent represents a single reasoning entity, an agentic system includes the supporting infrastructure such as tool interfaces, integration layers (including MCP client and MCP server), hosts, and the orchestration layer. These components enable agents to operate reliably, persistently, and at scale. Unlike a general AI system, which may simply generate outputs or provide predictions (such as a classifier, translator, or recommender), an agentic system exhibits proactive, goal-directed behavior that unfolds across multiple steps and adapts over time.

Key characteristics

• Goal orientation – Pursues explicit objectives across multiple steps
• Multi-step reasoning – Plans and executes sequences of actions iteratively
• Tool integration – Can invoke tools through custom connectors, plugins, or standardized protocols such as the Model Context Protocol (MCP)
• Adaptivity – Adjusts strategy when conditions change or tools fail
• Persistence – Maintains state or memory across actions and sessions
• System-level reliability – Depends on orchestration for retries, guardrails, and monitoring
• Scalability – Can coordinate multiple agents and tools within a single system

Understanding relationships

Agentic system vs. AI agent

An agent is a single reasoning entity that uses an LLM to decide and act. In contrast, an agentic system is the larger AI system that embeds one or more agents within the necessary infrastructure (tool interfaces, integration layers, hosts, and orchestration) that allows them to function dependably and at scale.

Agentic system vs. AI system

All agentic systems are AI systems, but not all AI systems are agentic. Many AI systems simply generate predictions or outputs (e.g., classifiers, translators, recommenders), whereas agentic systems exhibit autonomy, planning, and proactive goal pursuit.

Examples

• A travel-planning system that interprets preferences, searches flights, checks weather, books reservations, and adapts if disruptions occur
• A research companion that searches papers, extracts citations, builds knowledge graphs, and synthesizes findings
• A trading system that monitors markets, executes trades, and adjusts strategies dynamically

Non-examples

• A single AI agent running in isolation without supporting infrastructure
• An LLM with function calling that makes one-off tool calls without persistent goals or multi-step planning
• A classifier or translator that produces outputs but does not pursue goals or coordinate tools
• A basic chatbot that answers queries without planning or adaptation

An agentic system is not just an agent, and not just an AI system, it is the combination of one or more agents with the infrastructure needed for autonomous, goal-directed, multi-step behavior at scale.