# AI Agent vs Agentic AI: What's the Difference? An AI agent is a thing you deploy; agentic AI is a property that describes how much a system plans and acts on its own. This guide walks through the spectrum of agency, shows one refund request handled at four levels, and lists the questions that expose agent washing. > Source: https://www.robylon.ai/blog/ai-agent-vs-agentic-ai > Author: Mayank Shekhar (https://www.robylon.ai/author/mayank-shekhar) > Last updated: 2026-09-25 ## Key takeaways - An AI agent is a noun: a deployed system that takes a goal, decides the steps and acts through tools. - Agentic AI is an adjective: it describes how much autonomy a system has, so two products can both be agents and still differ a lot in how agentic they are. - Gartner said in June 2025 that only about 130 of the thousands of agentic AI vendors are real, so buyers should test every "agentic" claim. - The most useful buying questions are which tools the agent can call, what it can do without a human, what happens when it fails and how it is evaluated. - More autonomy means more cost and more chances for errors to compound, so risky actions like refunds should have limits and human approval. An AI agent is a system: software you deploy that takes a goal, decides what steps to take and carries them out through tools. Agentic AI is a property: it describes how much a system plans and acts without a person scripting each move. One is a noun you can buy. The other is a degree you have to measure. That distinction sounds pedantic until you're comparing three support vendors who all use the word "agentic" on their homepage. They can mean very different things. This guide covers both terms, a spectrum of agency with one refund request handled at each level, and the questions that separate a real agent from a relabeled chatbot. If you want the broader comparisons, we've covered [agentic AI vs generative AI](https://www.robylon.ai/blog/agentic-ai-vs-generative-ai) and [chatbots vs AI agents](https://www.robylon.ai/blog/chatbots-vs-ai-agents) separately. ## What is the difference between an AI agent and agentic AI? An AI agent is a concrete piece of software, while agentic AI is a description of how autonomously any AI system behaves. You deploy an agent. You assess how agentic it is. An AI agent has three parts: - **A goal**, such as "resolve this refund request". - **Tools**, say an order lookup and a refund API. - **A loop** in which a language model decides the next step, takes it, looks at the result and decides again. Our primer on [what AI agents are](https://www.robylon.ai/blog/what-are-ai-agents) goes deeper on each part. Agentic AI is the umbrella word for that behavior, and two widely read definitions shaped how it's used: - **Andrew Ng**, in a [June 12, 2024 letter in The Batch](https://www.deeplearning.ai/the-batch/welcoming-diverse-approaches-keeps-machine-learning-strong), suggested treating systems as agent-like to different degrees instead of arguing about whether something counts as an agent. He pointed to the adjective "agentic" as the word that lets you do that. - **Anthropic**, in its December 19, 2024 guide to building effective agents, grouped everything under "agentic systems" but separated workflows (models and tools run through predefined code paths) from agents (models that direct their own process and tool use). So "agentic" is a sliding scale. "Agent" is the thing sitting somewhere on it. ## How do AI agents and agentic AI compare side by side? The short version: an agent is an answer to "what did we deploy?" and agentic is an answer to "how much does it do on its own?" | | AI agent | Agentic AI | |---|---|---| | Part of speech | Noun: a system | Adjective: a property or approach | | Question it answers | What is running? | How autonomous is it? | | Unit you evaluate | One deployment with a goal, tools and permissions | The degree of planning, tool use and self-correction across a system | | Can be counted | Yes (you have three agents live) | No, it's a spectrum | | Can include several agents | Usually one per task or conversation | Often, when agents coordinate across tasks | | Typical support example | A refund agent connected to your store | A setup where a router hands work to specialist agents that plan multi-step fixes | | What to ask a vendor | What tools and permissions does it have? | What does it decide and do without a human? | ## What does the spectrum of agency look like? Agency in support software runs from zero, a scripted bot, up to several agents planning across tasks. Most real products sit somewhere in the middle, and plenty of them move along the scale depending on the ticket type. A useful four-step version: 1. **Scripted bot.** A person designed every path. Buttons and keyword matches trigger fixed replies. No model decides anything. 2. **Model that answers.** A language model reads your help content and writes a reply in natural language. It can explain, but it can't do. 3. **Agent that acts on one task.** The model calls tools to read and change data, such as an order record or a refund, inside limits you set. 4. **Multi-agent system that plans across tasks.** A coordinating layer splits a problem into parts and hands them to specialist agents, each with its own tools. Researchers are trying to make this scale more precise. A 2025 paper from University of Washington researchers, [Levels of Autonomy for AI Agents](https://arxiv.org/abs/2506.12469), defines five levels by the role a person plays next to the agent: operator, collaborator, consultant, approver and observer. It argues that autonomy should be a deliberate design decision, separate from what the agent is capable of. That framing is handy for buyers: a capable agent doesn't have to run at full autonomy on every action. ## How would each level handle the same refund request? The clearest way to see the difference is to run one ticket through all four levels. Take a hypothetical case: Maya emails a home goods store to say her order of four glasses arrived with two broken, and she wants her money back. ### Level 1: the scripted bot The bot spots the word "refund" and replies with a link to the returns policy and a form. Maya fills in the form. A human agent picks it up the next morning, checks the order, asks for a photo and issues the refund. The bot saved about 30 seconds of typing and nothing else. ### Level 2: the model that answers A language model reads the returns policy and writes a friendly, accurate reply: damaged items qualify for a refund within 30 days, please send a photo. It sounds helpful. But it can't see Maya's order, can't confirm the delivery date and can't issue anything, so the ticket still lands with a person. ### Level 3: the agent that acts on one task Now the system has tools. The agent works through the case on its own: 1. Looks up Maya's order and confirms it was delivered four days ago. 2. Asks for a photo of the damage. 3. Checks the policy and sees that partial refunds under a set amount are allowed without approval. 4. Refunds the two broken glasses, emails a confirmation and closes the ticket. If the amount had been over the limit, it would have routed the case to a person with everything already gathered. This is the level where support teams start to see tickets actually close, and our guide to [AI agents that take action](https://www.robylon.ai/blog/ai-agents-that-take-action-guide) covers how to scope those tools. ### Level 4: the multi-agent system A first agent reads the email and works out that it's a damaged-delivery refund. Then the work splits: - A refunds agent does everything the level 3 agent did. - A second specialist files a damage claim with the carrier. - A third notices this is the sixth broken-glass complaint on the same product this month and flags it for the operations team. Nobody asked for those last two steps. The system planned them because the goal was "fix the problem," not "answer the email." Level 4 is impressive when it works. It's also the level with the most moving parts, and every extra part is another place to fail. ## Why does the difference matter when you're buying support software? It matters because "AI agent" tells you what a vendor sells, while "agentic" is a claim about how much work that product will actually take off your team. You're paying for the second one. Two products can both ship "AI agents" and still differ a lot. Give each a damaged-order refund request: one looks up the order, issues the refund and closes the case, while the other writes a nicer version of your returns policy. The difference shows up directly in your resolution rate and staffing plan. Gartner's forecasts show why vendors are keen to claim the label. In a [March 5, 2025 press release](https://www.gartner.com/en/newsroom/press-releases/2025-03-05-gartner-predicts-agentic-ai-will-autonomously-resolve-80-percent-of-common-customer-service-issues-without-human-intervention-by-20290), Gartner predicted that by 2029 agentic AI will autonomously resolve **80%** of common customer service issues without human intervention, leading to a **30%** reduction in operational costs. A forecast like that gives every support vendor a reason to put "agentic" on the box, whether or not the product does much on its own. > **Rule of thumb:** buy against the level of agency your top ticket types need, not against the label on the box. ## What is agent washing, and how do you test a vendor's "agentic" claim? Agent washing is rebranding an existing product as agentic AI without adding real agentic capability. Gartner used the term in a [June 25, 2025 press release](https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027), describing it as the relabeling of AI assistants, robotic process automation and chatbots. Gartner estimated that only about **130** of the thousands of agentic AI vendors are real. There's an irony here. In a June 2024 letter in The Batch, Andrew Ng said he was more likely to read articles about "agentic" workflows because they were less likely to be marketing fluff. A year later the word was marketing's favorite. Honestly, that's what happens to any useful term once budgets attach to it. The fix is to stop debating the label and ask what the product does. Four questions cover most of it: - **What tools can it call?** Ask for the list of systems it can read from and write to, and which of those writes are live in the demo. - **What can it do without a human?** Get specific: can it issue a refund, change an address, cancel an order? Up to what value? - **What happens when it fails?** A tool times out, the model misreads the policy, the customer changes their mind halfway. You want to see the handover, not hear about it. - **How is it evaluated?** Ask how the vendor scores conversations, whether you can use your own criteria, and whether they test the same cases repeatedly. Then ask for a live run on one of your own tickets. A product that only answers questions will explain your refund policy beautifully and change nothing in your store. ## Where does agentic AI go wrong? Agentic AI goes wrong in three predictable ways: it costs more, errors stack up over long chains of steps, and it can take actions you'd never approve. None of these are reasons to avoid it. They're reasons to limit it. ### It costs more Every planning step, tool call and self-check is another model call. Anthropic's 2024 guide says it plainly: agentic systems often trade latency and cost for better task performance, and the simplest solution is often enough. Gartner's June 2025 release predicted that over **40%** of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value or inadequate risk controls. ### Errors compound Reliability multiplies. If each step in a process is right 95% of the time, a ten-step process is right only about 60% of the time (0.95 to the power of 10). That's arithmetic, but the research backs it up. The 2024 [τ-bench study](https://arxiv.org/abs/2406.12045) tests agents on simulated retail and airline support tasks. GPT-4o using function calling: - Solved about **61%** of retail tasks on a single try. - Solved the same task correctly in all eight of eight attempts only about **25%** of the time. Think of it like a relay race where every handoff is a chance to drop the baton. More runners, more handoffs. ### It can take actions you'd never approve An agent that can refund can refund the wrong person. The controls that help are boring and effective: - Value limits on money-moving tools, with anything above them sent to a person - Human approval on irreversible steps like cancellations and account changes - Tools scoped to the minimum each agent needs - Logs of every tool call, so you can see what happened and why A 2025 University of Washington framework on agent autonomy calls this the approver role: for low-risk actions the agent runs alone, and for risky ones a person signs off. If you're building your own, our [step-by-step agent build guide](https://www.robylon.ai/blog/build-ai-agent-step-by-step-2026) covers where to put those checkpoints. ## Where does Robylon sit on this spectrum? Robylon is an AI customer support platform that resolves tickets across email, chat, voice and WhatsApp. It works standalone or alongside an existing helpdesk. In the terms of the spectrum above, each Robylon agent works at level 3: it acts on a task through tools rather than only answering. Routing sits on top of that. - **Triage agent:** the first AI agent to read a new chat or ticket. It works out what the customer needs and routes the conversation to one of your sub-agents, the built-in knowledge base agent or a human. - **Plain-language rules:** routing rules are written in plain language. The product docs give the example "If the customer asks for a person, hand over to a human." - **Single agents:** a single agent handles a whole conversation on its own with no transfers between agents. Every voice agent is a single agent. - **Tools:** agents act through tools built from your APIs, forms and connected apps, with 32 pre-built integrations including Shopify, HubSpot, Salesforce, Google Sheets, Slack and Microsoft Teams. - **Handover:** handing over to a human is itself a tool the agent calls, and the triage agent only tells a customer they're being transferred when it actually calls that tool. - **Reliability:** agents have version history with restore, teams choose the AI model per agent, and a failed model call retries on a backup model. - **Quality:** evaluations score conversations against your own criteria, alongside knowledge base gap reports and bulk knowledge base testing. Robylon's homepage states it will "Resolve 85% of support tickets without a human" and that teams are "Live in 7 days." In one published case, a [gaming company had over 95% of queries resolved automatically](https://www.robylon.ai/customer-stories/robylons-ai-agent-resolves-knowledge-base-queries-for-a-gaming-company). To see how triage, sub-agents and tools fit together, take a look at the [Robylon platform overview](https://www.robylon.ai/platform/overview). ## Frequently asked questions ### Is agentic AI the same thing as an AI agent? No. An AI agent is a specific piece of software that works toward a goal and takes actions through tools. Agentic AI is a description of behavior: how much a system plans, decides and acts without being told each step. Every AI agent has some agency, but the amount varies widely. A support agent that only looks up order status is less agentic than one that plans a refund, checks policy, issues credit and files a carrier claim. ### Can a chatbot be agentic? A scripted chatbot that follows a fixed decision tree is not agentic, because a person wrote every path in advance. Once a bot is connected to a language model and allowed to call tools, such as looking up an order or updating a record, it starts to show agentic behavior. At that point most people would call it an AI agent rather than a chatbot, though vendors are not always careful with the labels. ### What does agent washing mean? Agent washing is Gartner's term for vendors rebranding older products, such as chatbots, AI assistants or robotic process automation, as agentic AI without adding real agentic capabilities. The quickest test is to ask the vendor to show a live action: which system the agent wrote to, what it decided on its own and where a human approved the step. A product that can only answer questions is an assistant, whatever the pricing page calls it. ### Do I need a multi-agent system for customer support? Usually not at first. Many support teams get most of the value from one well-scoped agent per task, such as refunds or order tracking, with a routing layer in front. Multiple coordinating agents help when conversations span several departments or systems, but they add cost, latency and more places for things to break. Start with the smallest setup that resolves your top ticket types, then add agents when a real gap appears. ### How should I measure whether an agentic support tool works? Measure outcomes, not conversations. Track the share of tickets resolved with no human touch, how often the agent took a wrong action, how often it escalated correctly, and customer satisfaction on automated conversations. Also run the same test cases repeatedly, because a tool that gets a refund right once may not get it right every time. Score real transcripts against your own policy, not a vendor's generic benchmark.