Agentic AI
AI Agents
Agentic AI describes AI systems that pursue a goal with some autonomy by planning steps, using tools, checking results, and adjusting as they go.
AI Agents
What Is an AI Agent?
An AI agent is a software system that uses an AI model, usually a large language model, to work toward a goal by deciding which steps to take and then taking them. It can use tools such as search, email, calendars, and business apps, check the results, and adjust its next move. Well-designed agents operate within set permissions and guardrails, often with a person approving important actions.
Also called LLM agent
Example
Imagine a recruiting firm with a steady stream of job applications. It gives an AI agent a goal: check each application against the role’s must-have requirements and help book interviews.
The agent reads each résumé, checks the applicant tracking system for duplicates, and notes which requirements it could and could not confirm. If a required license is missing, it decides whether to send the applicant a pre-approved request or flag the gap. After a recruiter approves a shortlist, the agent offers candidates open times from interviewer calendars. If someone asks for a different week, it finds new slots instead of giving up. Anything unusual goes to a person.
Recruiters make every screening and hiring decision. Because some places, such as New York City and the EU, regulate AI used in hiring, the firm checks local rules first.
How an AI agent works
Most AI agents run in a loop: look at the goal, decide on the next step, take it, check the result, and repeat until the job is done or a stop rule applies. That loop depends on a few parts working together.
- Model: usually a large language model (LLM) that reads the situation and decides what to do next.
- Instructions and goal: what the agent is for, what “done” looks like, and what it must never do.
- Tools: the apps, search, databases, and APIs (connections that let one piece of software use another) it is allowed to use. The model requests a tool with a structured message, a capability called tool use (or function calling), and the surrounding software or platform runs it and returns the result.
- Knowledge and memory: reference material such as policies or product details, plus a record of what it has already done during the task and, in some setups, memory carried over from earlier tasks.
- Guardrails and checkpoints: guardrails such as permission limits and caps on steps or spending, plus points where a person must approve before the agent continues.
The more clearly each part is defined, the more predictable the agent tends to be.
AI agents vs. chatbots and automations
A chatbot answers, and you decide what to do next. A rule-based automation follows fixed steps someone designed in advance, and it can stall or go wrong on anything its rules did not anticipate.
An AI agent combines some of both. You give it a goal rather than a script, and it chooses the steps, picks the tools, and changes course when something unexpected comes up. That flexibility is the point, and also the risk: the same agent may take a different path each time, even on similar inputs.
In practice the lines blur. Many AI assistants can also search the web or use connected apps, and some can carry out multi-step tasks for you, so the same product may act like a chatbot one moment and an agent the next.
Two neighboring terms are worth separating. An AI workflow usually follows a predefined path and calls a model at certain steps, while an agent decides the path itself. Agentic AI is a broader, loosely defined label for systems that show this goal-directed, multi-step behavior to varying degrees.
When an AI agent is a good fit
Agents suit tasks that take several steps, involve messy or varied inputs, and still have a clear definition of done. Common examples include:
- Triaging support requests: reading the message, looking up the order, and drafting a reply for a person to review.
- Researching a prospect or competitor across several sources and compiling a short brief.
- Updating customer records in a CRM (customer relationship management system) from emails and meeting notes.
- Investigating why a campaign’s results changed by checking several ad and analytics sources and drafting an explanation for a person to verify.
If the steps never change, a fixed automation is usually cheaper, faster, and easier to audit than an agent. If the task needs judgment you cannot write down, or a mistake would be costly or hard to undo, keep a person making the final call. A good test: could you explain the job, the tools, and what “done” means to a new hire in a few minutes? If not, the agent will likely struggle too.
Risks and how to start
Because agents act rather than just answer, their mistakes have consequences. Watch for these risks:
- Wrong actions: an agent can misread a goal, rely on made-up or outdated information, or take a step that looks reasonable but is still wrong.
- Too much access: an agent that can send email, edit files, or make payments can do real damage if it goes wrong. Content it reads, such as a web page or an incoming email, may also contain instructions, sometimes hidden, meant to redirect it. This is a form of prompt injection, and current safeguards cannot fully prevent it, which is another reason to limit access.
- Cost and runaway loops: each step can mean another request to the AI model, and those requests usually cost money, so a stuck agent can waste time and budget.
- Weak oversight: without logs, it is hard to see what the agent did or why.
Start narrow. Pick one well-understood task, give the agent only the access it needs, and begin in draft-only or read-only mode. Add human-in-the-loop approval for anything that sends, deletes, pays, or changes records, either for each action or by approving a batch or message template up front, as in the recruiting example. Review its activity regularly, and widen its scope only after it has proven reliable on the small job.
AI agent vs. chatbot vs. rule-based automation
| AI agent | Chatbot | Rule-based automation | |
|---|---|---|---|
| What it does | Works toward a goal by taking actions across tools | Answers questions and drafts content in a conversation | Runs a fixed sequence of steps when triggered |
| Who decides the steps | The agent, within limits you set | You, after reading each reply | Whoever built the rules, in advance |
| Handles unexpected input | Often adapts its plan, though not always correctly | Responds flexibly but usually leaves any action to you | Poorly; it may stop, skip the case, or process it incorrectly without noticing |
| Typical example | Reviewing applications and booking interviews | Answering a staff question about HR policy | Copying new form entries into a spreadsheet |
| Oversight needed | High: limited permissions, logs, and approval for key actions | Moderate: check answers before relying on them | Lower once tested, plus monitoring for breakages |
Use the simplest option that fits: fixed automation for fixed steps, a chatbot for answers, and an agent when the path varies and actions are needed.
Further reading
- AI EmployeesUseful if you want to see how AI systems set up for specific roles can work inside a team’s existing tools, with guardrails and support after launch.
- AI ImplementationHelpful if you are working out which processes to redesign and which systems to connect before putting an agent to work.
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.