An AI agent is software that works toward a goal through a series of decisions and actions. In an LLM-based agent, a language model helps interpret the goal and choose the next step; tools let the system read information or change something outside the conversation. The agent then checks the result and decides whether to continue, retry or ask for help. Lilian Weng's overview of autonomous agents describes planning, memory and tool use as common building blocks. The AWS Agentic AI Lens definitions likewise describe agents as systems that perceive context, take multi-step actions and adapt toward a defined goal.
A concrete example
Suppose you ask an assistant to find a 30-minute meeting slot next week. A chat interface might explain how to compare calendars. An agent with permission to use calendar tools could read availability, propose two slots, check for conflicts and prepare an invitation. Sending the invitation should still require the level of approval appropriate to your organization. Tool access, permissions and the ability to act matter more than whether the interface looks like a chat window.
How the agent loop works
- Understand the goal and context. The agent receives the request, constraints and any information it is allowed to read.
- Choose a next step. It may divide a large task into smaller steps, retrieve missing information or ask for clarification.
- Use a tool. Depending on its permissions, it may search, read a file, call an API or draft a change.
- Check the result. It compares the tool output with the goal, corrects mistakes or stops when it needs a person to decide.
This loop is a useful model, not a guarantee that every product has reliable planning or self-correction. Some systems follow a fixed workflow with an LLM at one step; others let the model choose among tools repeatedly. Calling either one an “agent” tells you little about its actual authority or reliability.
Agent, chatbot or automation?
A chatbot describes an interface: you exchange messages. A chatbot can also call tools, so “chatbot” and “agent” are not mutually exclusive. A fixed automation follows rules chosen in advance, such as sending a report every Monday. An agent is most useful when the route to a clear goal can change as new information arrives. For a repeatable task with stable rules, a conventional workflow may be simpler to test and maintain.
Before adopting an agent, ask what it can read, what it can change, how it knows a task is complete and where a person can interrupt it. A calendar assistant that only proposes times has a different risk profile from one that sends invitations or changes bookings.
Where agents help and where they fail
Agents are suited to bounded, multi-step work: gathering information from several approved sources, preparing a draft from those sources, checking a set of records or coordinating a routine workflow. Give the system an observable finish condition, such as “produce a draft with source links” or “identify scheduling conflicts without sending invitations.”
They can still misunderstand instructions, trust bad source material, choose the wrong tool or repeat an unproductive step. A model's fluent explanation is not evidence that an action succeeded. For consequential work, require a visible result, tool logs where appropriate and a human checkpoint before irreversible actions. Start with read-only access, test on representative tasks and expand permissions only after the failure cases are understood.
A practical selection checklist
- Is there a clear goal and a way to verify the result?
- Does the task need decisions across several steps, or would a fixed workflow be enough?
- Which tools and data does the agent need, and which actions should require approval?
- What happens when a tool fails, data conflicts or the agent is uncertain?
- Can you inspect the sources and final output before relying on them?
The useful distinction is the job the system can reliably complete with the access you give it. Evaluate that behavior on your own bounded task before treating an “agent” label as a product capability.