Table of Content
What is the difference between AI agents and agentic AI?
An AI agent is one piece of software that uses a language model and tools to complete a task toward a goal. Agentic AI is the wider approach, and sometimes the whole system around the agents, in which software plans, decides and acts across several steps with limited human direction. Vendors use the two terms differently, so ask what a product actually does before you buy.
- There is no industry-standard definition. IBM describes agents as one component of agentic AI, while many vendor blogs describe agentic AI as the system that coordinates several agents. Both views agree on one thing: the more the software decides on its own, the more control and monitoring you need.
- Generative AI creates content from a prompt. An agent perceives information, decides and takes actions toward a goal. Agentic AI pursues a goal through planning and multi-step work, using tools and other systems.
- Anthropic draws a practical line: workflows follow predefined code paths, while agents let the model direct its own process and tool use. Most business jobs should start as a workflow.
- Gartner forecast in June 2025 that more than 40% of agentic AI projects will be canceled by the end of 2027, citing cost, unclear business value and weak risk controls. It also warned about “agent washing”, where vendors relabel chatbots and RPA as agentic.
- Before buying anything labeled agentic, ask seven questions: what it decides alone, what it can change, what needs approval, what it logs, how it fails, what each task costs, and whether it can start read-only.
- CommercePundit designs and builds agents and agentic workflows on your own systems.
What is an AI agent?
An AI agent is software that takes in information, decides what to do and acts toward a goal. IBM describes an AI agent as one designed to “perceive information, make decisions and take actions toward a goal.” In practice, a business agent is a language model connected to tools: it can read your inbox, query a database, call an API or fill in a form, then report back. A support agent that looks up an order, checks the return policy and drafts a reply is a typical example.
Anthropic, in its widely cited engineering guide (Building effective agents, December 2024), defines agents as “systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.” The important part is dynamic: the model chooses the next step, rather than following a fixed script.
What is agentic AI?
Agentic AI describes AI that works toward a goal with a degree of autonomy. IBM says it is focused on “achieving goals by planning, making decisions and carrying out multi-step workflows,” and that it is designed to use tools, data sources and other systems to finish tasks. An agentic system may involve one agent or several, plus the memory, rules, approvals and monitoring around them.
The word “agentic” is better understood as a spectrum than a label. A tool that drafts an email and waits for you is barely agentic. A system that watches your inventory, reorders stock within a budget and tells you afterward is much more so. Autonomy rises as you give software more steps, more tools and fewer approvals.
AI agents vs agentic AI: how do they differ?
Whether you search for AI agents vs agentic AI, agentic AI vs AI agents or agentic AI vs AI agent, the comparison is the same. The table below uses the most common business meaning of each term. Vendors vary, so treat it as a working guide rather than a standard.
| Question | AI agent | Agentic AI |
|---|---|---|
| What is it? | A single software worker with a model, tools and a goal | The approach, and often the full system, of goal-driven AI that plans and acts |
| Scope | Usually one task or a closely related set of tasks | Often several steps, tools or agents working toward a larger outcome |
| Who decides the next step? | The agent, within the tools and limits you give it | The system, which may split the goal, delegate and review results |
| Human involvement | Approvals for sensitive actions | Approvals plus monitoring, escalation rules and audit trails |
| Typical example | An assistant that triages a support inbox and drafts replies | A service operation that retrieves accounts, processes returns and escalates hard cases |
| Main risk | Wrong action on one task | Compounding errors across steps, and unclear accountability |
| Best starting point | One narrow job with read-only access | A mapped process where each step already works as a workflow |
Two ways vendors use the words
| View | AI agent means | Agentic AI means | Where you see it |
|---|---|---|---|
| Component view | One building block that perceives, decides and acts | The capability or approach that uses agents to pursue goals | IBM |
| System view | A specialized worker for a single task | The coordinating layer that plans and manages several agents | Many vendor blogs and explainers |
When a vendor says “agentic,” ask them which view they mean. The answer tells you whether you are buying a single worker or a managed system, and which one you are paying for.
Agentic AI vs generative AI: what changes?
Generative AI vs agentic AI is the second comparison buyers ask about. IBM separates them on four points. Purpose: generative AI produces content, while agentic AI pursues a goal that may involve content, decisions or actions in other systems. User role: with generative AI you guide the output through prompts, while with agentic AI you can state a broader objective and step back. Autonomy: generative AI mostly responds and waits, while agentic AI can plan and run steps on its own. System interaction: generative AI depends on the tools built around it, while agentic AI is designed to use tools, data sources and other systems. The two combine often: a generative model writes the draft, and an agentic layer decides when to send it and to whom.
How do agents compare with generative AI, chatbots, RPA and workflows?
Confusion between these categories is the main reason projects start in the wrong place. The table separates them by the two questions that matter to a buyer: who chooses the next step, and can it change things in your systems.
| Technology | What it does | Chooses its own next step? | Acts in other systems? | Example |
|---|---|---|---|---|
| Generative AI | Creates text, images, code or summaries from a prompt | No, it responds to input | Only through tools built around it | Drafting marketing copy or summarizing a report |
| Chatbot | Holds a conversation, often from scripts or a knowledge base | Limited | Rarely | Answering common questions on a website |
| RPA | Repeats fixed steps across screens and apps | No, it follows rules | Yes, by following the script | Copying invoice data between two systems |
| Workflow automation | Runs a predefined path, sometimes with a model at one step | No, the path is fixed | Yes | Routing a lead and sending a templated reply |
| AI agent | Uses a model and tools to complete a task toward a goal | Yes, within limits | Yes, through approved tools | Researching an account and drafting outreach |
| Agentic system | Plans and coordinates multi-step work, often across several agents | Yes, across steps | Yes | Monitoring orders, resolving exceptions and escalating the rest |
Two comparisons come up in every buyer call. In an agentic AI vs RPA comparison, RPA repeats fixed steps and breaks when a screen or process changes, while an agentic system interprets the situation and chooses steps, at the price of being harder to predict. In an AI agent vs chatbot comparison, a chatbot talks, while an agent can also act through connected tools.
Our guides to AI workflow automation tools and AI agent orchestration cover the two ends of this table in more detail.
Workflows vs agents: the line that decides your design
Anthropic recommends finding the simplest solution that works and adding complexity only when it clearly improves results, which “may mean not building an agentic system at all.” Its guide separates workflows, where LLMs and tools are orchestrated through predefined code paths, from agents, where the model directs its own process. It lists five workflow patterns that cover most business needs before you reach for a full agent:
- Prompt chaining. Split a task into sequential model calls with checks between steps.
- Routing. Classify an input and send it to a specialized path.
- Parallelization. Run subtasks at the same time, or run the same task several times and compare.
- Orchestrator-workers. A central model breaks a task down and delegates to worker models.
- Evaluator-optimizer. One model produces a result and another critiques it in a loop.
What is an agentic workflow?
An agentic workflow is a multi-step process in which a model plans, uses tools and checks its own results inside guardrails that you define. It sits between a fixed workflow and a fully autonomous agent: you set the structure, and the model handles judgment within each step. Most successful business deployments look like this.
The guide also warns that agents trade cost and latency for flexibility, and that their autonomy brings higher costs and the risk of compounding errors. That is the reason to try a fixed workflow first and promote a step to an agent only when the path really cannot be predicted.
What are real agentic AI examples in business work?
IBM gives two clear agentic examples: a customer service system that retrieves account details, processes returns and escalates complex cases to a human, and a system that monitors production data and coordinates corrective responses across connected systems. It also shows how generative and agentic AI combine. For a customer event, generative AI drafts the agenda and invitations, while an agentic system checks calendars, reserves a room and tracks responses.
Closer to the work our clients ask about, the pattern repeats across functions:
| Function | Single agent | Agentic system |
|---|---|---|
| Sales | Researches one account and drafts outreach | Scores leads, enriches records, books meetings and updates the CRM, with approvals |
| Marketing | Drafts and schedules a campaign asset | Plans a campaign, produces assets, publishes, measures and adjusts |
| Finance | Matches one batch of invoices | Matches payments, flags exceptions, chases late invoices and posts to the ledger |
| Operations | Summarizes daily orders | Watches stock, reorders within limits and escalates supplier issues |
See concrete cases in our AI agent examples and best AI agents for business. For the newest consumer-grade products, our comparison of Claude Duet, OpenAI Dots, Meta Muse and Grok Bot shows where always-on agents work and where they have already failed in public.
Should you use a workflow, an agent or an agentic system?
| If this is true | Start with | Why |
|---|---|---|
| The steps are known and rarely change | A workflow | Predictable, cheaper and easy to test |
| One task has unpredictable steps, such as researching an account | A single agent with narrow tools | The model can choose the path within safe limits |
| A process spans several tools and teams | A workflow with agent steps | Keeps control while adding judgment where needed |
| The goal is broad and the path varies each time | An agentic system with approvals | Planning and delegation are the point, so add monitoring |
| The action moves money, deletes data or signs documents | A person approves every time | Autonomy should not extend to irreversible actions |
What are the risks and costs of agentic AI?
The costs show up in three places: model usage that grows with every extra step, the engineering to connect and secure tools, and the people who review results. Gartner forecast in June 2025 that over 40% of agentic AI projects would be canceled by the end of 2027 because of escalating costs, unclear business value or inadequate risk controls, and said many products labeled agentic are rebranded assistants, RPA or chatbots. It estimated only about 130 of the thousands of vendors claiming agentic capabilities were genuine (reported by South China Morning Post and others). It is a forecast, not an outcome, but the reasons it gives are the ones we see in real projects.
- Compounding errors. A small mistake in step two becomes a large one by step six. Test on sandboxes and add checkpoints.
- Over-broad access. An agent that can read everything can leak everything. Grant the narrowest permissions that work, and read MCP vs API for how tool access is structured.
- Unclear ownership. Name one person accountable for each agent, the way you would for an employee.
- Weak logging. If you cannot replay what the agent did and why, you cannot fix it or defend it.
How do you tell real agentic AI from agent washing?
Ask the vendor these seven questions. Specific, testable answers are a good sign. Vague answers about “autonomy” are not.
- What does it decide on its own? Ask for the exact decisions, not a general claim.
- Which systems can it change? Reading a record and editing it are different risks.
- What needs human approval? Payments, deletions and external messages should be on the list.
- What does it log? You need a replayable record of each action and the reason.
- How does it fail? Ask what happens when a tool errors or the model is unsure.
- What does one completed task cost? Ask for usage per task, not only a monthly fee.
- Can we start read-only? A trial that can only read and draft is the safest first step.
How should a business start with agents?
- Map one process. Pick a job with clear inputs, outputs and an owner.
- Build it as a workflow first. Measure time, errors and cost.
- Promote one step to an agent only where the path really varies.
- Keep approvals on for anything that sends, pays, signs or deletes.
- Review weekly for the first month, then widen the scope.
Our guide to building an AI agent for your business walks through the build. If you want a team to design and run it, see our agentic AI development services, AI agent development, AI automation and AI development services.
Frequently Asked Questions
What is the difference between AI agents and agentic AI?
An AI agent is a single software worker that uses a model and tools to complete a task toward a goal. Agentic AI is the broader approach, and often the whole system, in which software plans and carries out multi-step work with limited human direction. Vendors define the terms differently, so ask which meaning they use.
Agentic AI vs generative AI: are they the same?
No. Generative AI creates content such as text, images or code from a prompt. Agentic AI pursues a goal by planning, deciding and acting across steps, often using generative AI as one part. A system can combine both, for example by drafting an email and then sending it after approval.
What are some agentic AI examples?
IBM gives a customer service system that retrieves account details, processes returns and escalates complex cases to a human, and a system that monitors production data and coordinates corrective responses. In business, it also covers sales, finance and operations systems that plan and act across several tools.
Is ChatGPT an agent or an LLM?
ChatGPT is a product built around a large language model. The model alone answers prompts. When the product is given tools and permission to take multi-step actions, such as browsing or working inside connected apps, it behaves like an agent. The difference is whether it acts, not which name it carries.
What are the different types of AI agents?
Classic AI textbooks describe five types: simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents and learning agents. In business, people more often group agents by job, such as sales, support, finance or research agents.
Is Siri an example of agentic AI?
Voice assistants such as Siri mostly respond to commands one at a time, so they are closer to assistants than to agentic systems. A system is agentic when it can plan several steps, use tools and work toward a goal without a prompt for each step.
AI agent vs chatbot: what is the difference?
A chatbot holds a conversation and answers questions, often from scripts or a knowledge base. An agent can also take actions, such as looking up an order, updating a record or booking a meeting, through connected tools. Many products combine both.
Agentic AI vs RPA: how do they differ?
RPA repeats fixed steps and breaks when the screen or process changes. Agentic AI uses a model to interpret the situation and choose steps, which makes it more flexible but harder to predict. Many teams keep RPA for stable tasks and add agents for tasks that vary.
Is agentic AI safe to use in a business?
It can be, if you limit what it can access and change. Start read-only, keep approvals on for payments, messages and deletions, log every action and assign an owner. Anthropic advises testing agents in sandboxed environments with guardrails.
How do I build an AI agent for my business?
Pick one narrow job, define the tools and data it needs, build it as a workflow first, and add agent behavior only where the path varies. Test on sample data, keep approvals on and review results weekly. Our guide to building an AI agent covers the steps.
Bottom Line
An AI agent is the worker, and agentic AI is the goal-driven approach and system around it, though vendors blur the two. Judge any product by what it decides, what it can change and what it logs. Start with a workflow, promote only the steps that need judgment, and keep a person in charge of anything irreversible. If you want help deciding what to automate and building it safely, talk to us about agentic AI development services.
