AI Agent
AI Agents
An AI agent is software that uses an AI model to pursue a goal by choosing steps, using tools like apps and search, and taking actions for you.
AI Agents
What Is Agentic AI?
Agentic AI is a term for AI systems designed to pursue a goal with some degree of autonomy: they break the goal into steps, take actions with connected tools, check the results, and adjust their approach. It describes a property on a spectrum, not a single product. The more a system plans and adjusts its own steps, the more agentic it is. Usage varies, and there is no single standard definition.
Also called agentic systems
Example
A mid-size accounting firm closes the books for its clients each month. A staff accountant gives an agentic system one goal: prepare the bank reconciliations for a client’s three operating accounts and flag anything that needs attention.
The system pulls the bank statements and the general ledger cash accounts through approved connections and matches each bank transaction to a ledger entry. One deposit has no matching entry. Instead of stopping, it searches the client’s open receivables, finds an unpaid invoice for the same amount whose number appears in the deposit’s payment reference, and drafts an entry to record the payment against that invoice. It does not post the entry. It adds the item to a review list with its reasoning, and the accountant approves, edits, or rejects each proposed change before anything touches the books.
What makes a system agentic
“Agentic” describes behavior, so it helps to break it into traits you can look for in any system:
- A goal instead of a script: you describe the outcome, and the system works out the steps.
- Several steps, not one reply: it keeps working until the goal is met or it reaches a limit.
- Acting in other systems: it reads from and acts in other systems through tool use, also called function calling.
- Course correction: it checks results and changes approach when something fails.
- Independence: how far it can go before a person reviews or approves its work.
A product can be strong on some traits and weak on others. One assistant might use tools well but pause after every step to ask what to do next. Another might run for a long time on its own, but only inside a single app. Checking each trait gives you a clearer picture than the label does. The mechanics of the underlying loop are covered on the AI agent page.
Agentic AI, AI agents, and autonomy
Vendors apply “agentic” to everything from a chatbot that can call one tool to systems that work through long tasks with little supervision, so the label alone says little. Two neighboring terms help pin it down.
An AI agent is a specific system built to act this way. Agentic AI describes the approach and how much a system plans and adjusts its own steps.
AI autonomy is a separate setting: how much the system may do without a person’s sign-off. It is usually set task by task, so a system that plans its own steps can still be required to get approval before it sends, pays, or deletes anything. When you assess a product, ask both questions: how much does it work out for itself, and how much is it allowed to do without approval?
How it relates to generative AI
Generative AI produces content on request: a draft, a summary, an image, a block of code. Agentic AI is about acting toward a goal. The two are not rivals. Agentic systems typically use a generative model, usually a large language model, as their reasoning engine: it reads the situation, decides the next step, and chooses which tool to use.
The practical difference is what happens after the output. With generative AI alone, the model’s job ends at the output, and a person or a fixed process decides what happens next. With agentic AI, the system can take the next step itself, such as filing a document, updating a spreadsheet, or emailing a vendor. That shift from producing content to taking actions in real systems is why agentic setups need more attention to access, oversight, and record keeping.
How to evaluate an “agentic” product
Because “agentic” can mean very different things, ask for specifics before a system touches live data. These questions work for a vendor demo or an internal project:
- Approval: which actions can it take without a person’s sign-off, and can you change that?
- Access: which systems can it write to, not just read from, and with what permissions?
- Limits: what stops it, such as a cap on steps, run time, or spending, and what happens if it fails partway through a task?
- Records: what does it log about each action and its reasoning, and can those actions be reversed?
- Testing: how was it tested on tasks like yours, with your kind of data and exceptions?
- Checkpoints: where do human-in-the-loop reviews sit, and who is responsible for them?
If the answers are vague, resolve them before launch rather than after. If the steps never change, a fixed AI workflow may be simpler and more predictable.
Agentic AI vs. generative AI
| Agentic AI | Generative AI | |
|---|---|---|
| Core job | Pursues a goal by planning and taking steps | Creates content in response to a request |
| How it is triggered | A goal, schedule, or event, then it works through several steps | A prompt, usually from a person or an app, handled one request at a time |
| Output | Actions taken in other systems or prepared for approval, such as updated records, drafted entries, or sent messages | Text, images, audio, video, or code for a person or app to use |
| Typical example | Reconciling accounts and drafting entries for review | Drafting a client email or summarizing a report |
| Main risk | A wrong or manipulated action in a real system, or an early error carried into later steps | Inaccurate or made-up content that someone uses without checking |
A simple test: if a person takes every action, generative AI is helping with the content; once the system takes the next step itself, it is acting agentically.
Further reading
- AI EmployeesSee how SN3 deploys role-trained AI systems inside the tools a team already uses, with guardrails and support after launch.
- AI Fluency AssessmentA free assessment of where your business stands on AI adoption, useful context before you try AI that takes actions on its own.
From definition to deployment
See an AI agent doing real work.
SN3 AI Employees are role-trained AI systems deployed inside the tools your team already uses, with guardrails and support after launch.