“AI agent” is one of the most used - and least defined - terms in automation right now. This guide gives a concrete, jargon-free answer: what an agent actually is, how it differs from a normal AI call, and where it earns its place in a workflow.
The simplest definition
An AI agent is a language model that can take actions, not just produce text. A normal LLM call is a single round trip: you send a prompt, it returns an answer, done. An agent runs a loop - it thinks about what to do, calls a tool, reads the result, and decides whether it's finished or needs another step.
That loop is the whole difference. A chatbot answers your question. An agent can read a support ticket, search your documentation, check the customer's account status, draft a reply, and post it - making decisions at each step based on what it finds.
Agent vs. a plain LLM call
Think of a plain LLM call as a calculator: input goes in, output comes out, no memory of what it's doing or ability to reach outside itself. It can't look anything up, can't act, and can't recover if its first guess is wrong.
An agent is closer to an assistant with a to-do list and a set of tools. It can:
- Use tools - call an API, query a database, search the web, send a message
- Observe results - read what a tool returned and factor it into the next decision
- Branch - take different paths depending on what it discovers, not a path fixed in advance
- Persist - keep working across multiple steps until a goal is reached
The reasoning loop
Under the hood, almost every agent follows the same cycle: think → act → observe → repeat. The model plans a next step, calls a tool to execute it, reads the tool's output, and loops back to plan again. It exits the loop when it decides the task is complete - or when a guardrail (a step limit, a cost cap, an approval gate) stops it.
This is why agents handle messy, real-world tasks that rigid automation can't. They don't need every branch pre-wired. When something unexpected shows up, the agent reasons about it in the moment instead of failing on an unhandled case.
What agents need to be useful
A capable agent rests on three things:
- Tools - the actions it can take. In FlowTrux these come from MCP servers and native integrations: Slack, Gmail, Sheets, GitHub, your own API, and more.
- Context - the information it can draw on. A knowledge base (RAG) lets an agent ground its decisions in your actual documents instead of guessing.
- Guardrails - the limits that keep it safe. Step caps, approval steps for high-stakes actions, and clear stop conditions keep an agent reliable in production.
When you need an agent - and when you don't
Agents are powerful, but they're not always the right tool. Use an agent when the task involves judgment, multiple steps, or tool use that can't be fully scripted in advance: triaging tickets, researching a lead, reviewing a document, handling a request that could go several ways.
You don't need an agent for deterministic work - moving a form submission into a spreadsheet, or sending a fixed notification when an event fires. A single LLM step (or no LLM at all) is cheaper and more predictable for those.
Agents in a FlowTrux workflow
In FlowTrux, an agent is a node on the canvas. You give it an instruction, connect the tools it's allowed to use, and optionally attach a knowledge base. When the workflow runs, the agent node executes its think-act-observe loop, then passes its result to the next node - a Slack message, a branch, an approval gate, whatever the workflow needs.
The best way to understand agents is to build one. Describe a task that needs a decision - “read incoming support emails, answer the simple ones from our docs, and escalate the rest to #support” - and watch the agent handle the variation that a fixed if/else never could.