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AI Agents

Difference between AI Agents vs Agentic AI

AI agents and agentic AI are often mixed up. One handles focused tasks with tools. The other aims for longer term planning and more independence. Here’s a clear explanation of both and how to decide which one you need.

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AI agentsAgentic AIWorkflows versus agentsChoosing between them

The terms AI agent and agentic AI are often used interchangeably, but they describe different levels of independence. Understanding the distinction helps teams choose an architecture that matches the risk and complexity of the task.

01

AI agents

An AI agent is a system that uses a language model to decide which action to take, calls a tool to take it, observes the result and repeats until a task is complete. The pattern is often described as a reason–act loop, popularised by the ReAct research paper.

Agents are typically scoped to a focused job: triaging a ticket, researching a question, or updating a record. Their tools, permissions and stopping conditions are defined up front.

02

Agentic AI

Agentic AI refers to systems with a higher degree of autonomy. They set intermediate goals, plan over longer horizons, coordinate multiple agents or tools, and adapt their plan as circumstances change.

In practice, agentic systems are usually compositions of several agents and deterministic workflow steps, with an orchestrator deciding which component handles each part of the job.

03

Workflows versus agents

Anthropic’s guidance on building effective agents draws a useful line: workflows follow predefined code paths that call models at fixed points, while agents let the model direct its own process. Many production problems are solved best by workflows, with agentic behaviour reserved for steps that genuinely need it.

04

Choosing between them

Use a single agent when the task is bounded, the tools are few and mistakes are cheap to correct. Move toward agentic designs when tasks span multiple systems, require planning, or benefit from specialised sub-agents.

Whichever you choose, add limits: maximum steps, restricted permissions, approval gates for high-impact actions and full logging of every decision.

Key takeaways
An AI agent runs a decide–act–observe loop for a focused task.
Agentic AI adds planning, longer horizons and coordination across multiple agents.
Predefined workflows are often the more reliable choice; use autonomy where it adds value.
Guardrails, approvals and logging matter more as autonomy increases.
Next article · RAG
What Actually Changes From Simple RAG to Agentic RAG →