AI Agents vs Agentic AI: What’s the Difference?

By Loïc Guyon (Tileo), founder of AI Jungle · Updated 2026-10-17

An AI agent is a bounded software actor performing tasks for a user or another system (IBM). Agentic AI adds wider planning and coordination, often involving agents (IBM; arXiv). The terms are not standardized. For a firm, ask how much authority the software needs. Choose a bounded agent for a clear task and stop line. Choose a wider agentic system for planning across approved steps. Always define what it may read and draft, what a person must approve, and where it must stop.

Scope signal: If you can name the approved input, returned artifact, and stop line, request a bounded agent before asking for broader autonomy.

What is the difference between AI agents and agentic AI?

An AI agent is a software actor; agentic AI describes a wider pattern or degree of autonomous planning and action. That is the useful working distinction, not a claim that every vendor uses the words in the same way.

IBM defines an AI agent as a system or program that can perform tasks autonomously for a user or another system (IBM). Its agentic AI page describes a system that can accomplish a goal with limited supervision. It says such a system consists of AI agents whose work can be coordinated in a multiagent system (IBM). Moveworks makes a similar, attributed distinction: an agent handles a defined task, while agentic AI coordinates agents, data, and tools across a wider workflow (Moveworks).

The research literature also shows why rigid labels are risky. One review defines AI agents as autonomous software entities built for goal-directed work in bounded digital environments. It then places agentic AI further along a spectrum that adds multi-step planning and coordination (arXiv). Treat these as useful taxonomies from named sources, not settled law.

For a firm, the practical unit is authority. The label matters less than the boundary around the actor:

• Allowed reads: Which workspace material can the agent use?
• Draft artifact: What must the agent prepare for the team?
• Human approval: Who reviews the exact text before it leaves?
• Stop line: Where does the agent’s authority end?

An agent may still do meaningful work within a narrow boundary. An agentic system may plan across more steps, but wider planning does not answer the authority question. A buyer still needs to decide what the software can see, prepare, and place before a person.

How should an ops lead choose between them?

Choose a bounded agent when the team can name the workspace material, draft, approver, and stop line before work begins. Consider a wider agentic system when the desired outcome requires planning or coordination across several approved steps. This is an editorial decision rule based on the distinctions above, not a performance comparison.

Start with the work your firm already recognizes. Candidate outreach, client updates, proposals, and meeting follow-up all end in a reviewable artifact. The following rows are examples, not reported deployments.

The table does not dictate which technology to buy. It forces the team to make the authority boundary visible. If a single workflow is enough, a bounded agent is easier to specify. If the system must choose among several approved steps or coordinate several agents, the request is more agentic. In both cases, the external message waits for the person who owns it.

For an executive-search firm, the candidate-outreach row is a useful test because the work mixes sensitive relationship context with a clear communication moment. The same pattern applies to recruiting, M&A advisory, and consulting: let the agent prepare the artifact inside the workspace, then keep the human decision at the point where words leave the firm. See the executive-search workspace and the broader Pitstop workspace.

What permissions and approvals should the request include?

Write the request around reads, drafts, approval, and the stop line. “Use our systems” leaves too much open. A professional-services firm should be able to read the boundary without interpreting a product label.

1. Name the workspace area. State whether the agent may use projects, clients and CRM, network, meetings and prep briefs, content, knowledge, or finance.
2. Name the approved context. Identify the project, client, meeting, or other material relevant to the task.
3. Name the draft. Ask for a candidate message, client update, proposal, follow-up, or another reviewable artifact.
4. Name the approver. Assign the person who owns the exact external text.
5. Name the stop line. State that the external message waits for exact-text approval.

These are Pitstop’s operating recommendations, not NIST requirements. NIST states that its AI Risk Management Framework is voluntary and is intended to help organizations manage AI risks and add trustworthiness considerations to the design, development, use, and evaluation of AI systems (NIST). Use the framework itself when your team needs its risk language.

Inside Pitstop’s shared workspace, the team and its agents can work together. Agents may come from ChatGPT, Claude, or Codex through MCP, or be built by AI Jungle. The workspace can cover projects, clients and CRM, network, meetings and prep briefs, content, knowledge, and finance. The safe mechanism is simple: agents ask before anything leaves; a person approves the exact text; the journal shows any gap. A yes is bound to the exact content, autonomy is set per agent, and the journal is hash-chained.

This means an agent can read approved context and prepare work beside the team without owning the final external communication. When a candidate message, client update, proposal, or follow-up is ready, the workflow stops for exact-text approval. The person sees what would leave and decides whether that exact text is approved. For the approval model, see how human-in-the-loop pit stops work. For meeting context, see the AI notetaker guide.

Are AI agents just LLM wrappers?

Some agent designs use an LLM as a reasoning component, but “LLM wrapper” is too weak to describe the operating boundary. IBM says large language models sit at the core of many agents and contrasts a traditional model response with agentic technology that can use tool calling and create subtasks (IBM). The research review likewise separates reactive generation from agents that have structured initialization, bounded autonomy, and persistent task orientation (arXiv).

For a buyer, inspect what surrounds the model. What may it read? What artifact may it draft? Does it plan only within that task, or coordinate across approved steps? Who approves the exact external text? Where does its authority stop? Those questions reveal more about the operating setup than the wrapper label.

How do generative AI, AI agents, and agentic AI relate?

A practical ladder is content generation, bounded action, then wider orchestration, but it remains an attributed model rather than a universal taxonomy. The research review treats generative AI as a baseline that produces outputs from prompts. It describes AI agents as adding goal-directed action within a bounded scope, and agentic AI as adding broader planning and multi-agent coordination (arXiv).

That ladder helps a firm rewrite a vague request. These are examples, not reported deployments:

• Generative request: “Draft the summary from these approved notes.” The output is content.
• Bounded-agent request: “Read the approved meeting notes and project context, prepare the client update, and stop for exact-text approval.”
• Agentic-system request: “Plan the approved preparation steps across the project, client, meeting, and knowledge context, then present every external message for exact-text approval.”

The generative request asks for content. The bounded-agent request defines an actor with a stop line. The agentic-system request requires wider planning. None of the labels removes the need for a person to approve the exact words that leave the firm.

What are the five types of AI agents?

IBM presents five common agent types: simple reflex, model-based reflex, goal-based, utility-based, and learning agents. This is IBM’s taxonomy, not the only valid one (IBM).

• Simple reflex agents act from current perception and programmed condition-action rules, without memory (IBM).
• Model-based reflex agents use current perception and memory to maintain an internal model, while remaining limited by their rules (IBM).
• Goal-based agents search and plan action sequences aimed at a goal (IBM).
• Utility-based agents select actions using a utility or reward function (IBM).
• Learning agents add new experiences to their knowledge base and can combine learning with goal-based or utility-based reasoning (IBM).

This list classifies decision behavior. It does not choose an authority boundary for your firm. You still need to define allowed reads, the draft artifact, human approval, and the stop line. The autonomous AI agents guide explores the boundary in more detail.

Is ChatGPT an agent, an LLM, or agentic AI?

The name “ChatGPT” alone is not enough to classify the operating setup. This article does not assign one label to every mode or configuration of a changing product. The evidence supports a narrower test: a model produces responses, while an agent adds goal-directed behavior and bounded action; wider agentic systems add planning and coordination (arXiv).

Ask what the setup can do in the request at hand. If it only returns generated content after a prompt, evaluate it as generation. If software can pursue a goal within a boundary, inspect it as an agent. If the setup can plan and coordinate across agents or systems, treat it as a wider agentic setup. These are editorial classification rules derived from the cited review, not official product classifications.

The same answer applies to “Is ChatGPT generative or agentic AI?” Inspect the configured behavior, permissions, and action boundary instead of relying on the product name.

What should the final request contain?

The final brief should make the authority boundary visible before work starts. Pitstop recommends including:

• The approved workspace area and context.
• The named draft artifact.
• The information the agent may read.
• The steps the agent may plan.
• The person who approves exact external text.
• The point where the agent must stop.

If the brief describes a bounded task, ask for a bounded agent. If the outcome requires planning across approved steps, consider an agentic system and keep the approval boundary equally explicit. The agentic AI tools guide provides more context for that choice, and the AI proposal generator guide applies the same thinking to proposals.

My recommendation is simple: do not buy the most autonomous label. Choose the narrowest authority that can prepare the work your team needs. Expand that authority only when the wider planning scope, human approver, and stop line are clear.

Pitstop is the workspace where your team and your AI agents run the firm together. It is available at one price per firm, never per seat, with a free plugin.

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