| Practical effect | What the evidence supports | Planning use |
|---|---|---|
| Software acts and decides | Agents go beyond generative AI, which creates content, by acting and making decisions the way a human might. | List which decisions you are willing to hand over before choosing a tool. |
| Multi-step work with tools | Agents can execute multi-step plans and use external tools in digital environments. | Inventory the systems the agent must reach, because each connection is part of the design. |
| Control flow moves to the model | An agent uses a large language model to decide the control flow of an application. A workflow runs LLMs and tools through predefined code paths. | If you can write the path down in advance, build a workflow instead. |
| Autonomy is graded | Autonomy is a spectrum, not an on/off property. | Decide how much latitude the model gets per task rather than approving an agent as a whole. |
Figures and definitions above follow MIT Sloan’s explanation of agentic AI and LangChain’s definition of an agent.
A workable ai agents definition is this: an AI agent is autonomous software that perceives, reasons and acts toward goals, through external tools. The condition is autonomy; when the code path is fixed in advance, the system is a workflow, not an agent.
For wider context, see our guide to AI and automation tools for software teams, which places agents alongside the other automation options a team can choose from.
What changes when software becomes an agent
The practical shift is from software that produces output to software that takes steps. MIT Sloan frames the difference as generative AI creating content while agents go further, acting and making decisions. That has consequences for how a team scopes, buys and supervises the work.
- Tasks, not prompts, become the unit of work. Because an agent can execute multi-step plans, you describe an outcome and let the system sequence the steps. This suits jobs with several dependent actions. It suits one-off text generation less, where a plain model call is enough.
- Integrations become a design decision. AWS describes agents extending their capabilities by connecting to external software, APIs or devices. Every connection widens what the agent can reach, so decide which systems it may touch before you evaluate models.
- Predictability depends on who controls the flow. In a workflow your code decides the order of steps. In an agent, the model decides. If auditors or customers need the same path every time, that points toward a workflow.
- Oversight is a dial. Since autonomy is a spectrum, you can give an agent narrow latitude on a sensitive task and more on a low-risk one. Treat this as a per-task setting.
Those consequences only make sense once the definition is clear, so the next section compares how the major sources phrase it.
How major sources define an AI agent
Definitions differ in emphasis but agree on autonomy and action. MIT Sloan describes AI agents as autonomous software systems that perceive, reason and act toward goals. That wording is the basis for the summary at the top of this page.
IBM defines an AI agent as a system that autonomously performs tasks by designing workflows with the tools available to it. AWS uses a similar framing: a software program that can interact with its environment and perform self-directed tasks. Both place the weight on self-direction and on tools.
LangChain’s definition is the most concrete for builders. It says an agent uses a large language model to decide the control flow of an application. That makes the question “who decides the next step?” the test of whether something is an agent. If a language model does, you have an agent. If predefined code does, you have a workflow that happens to call a model.
Agents, chatbots and workflows
An AI agent differs from a chatbot mainly in what it can do beyond conversation. IBM describes nonagentic AI chatbots as ones without available tools, memory or reasoning. They respond inside a conversation and stop there. An agent, by contrast, can use tools, which lets it act on other systems and not only reply.
Tool use also addresses a limit of language models. A model on its own is bounded by its training data. IBM explains that agentic technology uses tool calling on the backend to obtain up-to-date information, so the agent can fetch current facts instead of guessing from what the model learned earlier.
The other neighbour is the workflow. LangChain separates the two cleanly: a workflow orchestrates LLMs and tools through predefined code paths, while an agent lets the model choose. Workflow automation is therefore the right label for fixed pipelines that include a model step. Teams exploring that fixed-path style of automation can see a concrete case in How Insurance Teams Are Saving Time With RPA: 7 Practical Use Cases And Where To Start In 2026 – Interestingdevelopment.
The decision loop behind an agent
An agent works by repeating a short cycle until the task is done. Understanding the cycle explains most of what agents do well and where they go wrong.
Think, act, observe
IBM calls the cycle Think-Act-Observe and says such loops are used to solve problems step by step. In plain terms:
- Think. The model looks at the goal and what it already knows, and decides what to do next.
- Act. The agent carries out that decision, typically by calling a tool such as a search, a database query or another application.
- Observe. The agent reads the result and feeds it back into the next round of thinking.
This is the same perceive, reason and act pattern in MIT Sloan’s definition, spelled out as an engineering loop. The practical point is that quality depends on each pass. A poor decision in the thinking step produces a poor action, and the observation then carries that mistake forward unless the loop checks it.
Loop patterns and tool calling
LangChain names several established ways to structure that loop: ReAct, Reflexion, plan-and-execute, and orchestrator-worker patterns. They are variations on how an agent plans, checks itself or divides work, and they appear in many agent frameworks. When you evaluate a framework, ask which of these patterns it supports and how much of the loop you can inspect and control.
Tool calling is the mechanism that makes the Act step possible. The language model is the reasoning engine, tool calling is its link to the outside world, and the loop is what ties the two together. Prompt quality still matters inside every pass; our guide to Mastering AI Prompt Engineering: Practical Strategies For Faster, More Reliable Results In 2026 – Interestingdevelopment covers that side.
Types of AI agents
IBM lists 5 main agent types, ordered from the simplest to the most advanced. The ordering is the useful part: as you move up, the agent handles more of its own decision-making, and you take on more of the work of testing and supervising it. Match the type to the decision the task actually requires, and avoid reaching for the most advanced type by default.
A second way to compare agents is by loop design. The patterns LangChain names (ReAct, Reflexion, plan-and-execute and orchestrator-worker) describe how reasoning, self-review and task division are arranged. A team choosing a framework is usually choosing among these arrangements as much as among agent types.
A selection checklist for teams
Choosing an agent for a project comes down to matching autonomy to need. Work through these decisions in order and stop as soon as a simpler option answers the question.
- Test whether the path can be written down. If the steps are known in advance, LangChain’s distinction says you want a workflow with predefined code paths. Reserve agents for tasks where the next step depends on what the last step returned.
- Check for a need for fresh information. If answers must reflect current data beyond a model’s training, you need tool calling, as IBM explains. Confirm the framework can connect to the APIs or software you rely on, as AWS notes agents often do.
- Start with a single agent. LangChain advises starting with a single agent and good prompt engineering, and adding tools before adding agents. Prove the simple version before you introduce coordination between agents.
- Match the type to the task. Use IBM’s ordering from simplest to most advanced as a ladder, and climb only when the simpler type fails on your actual task.
- Compare loop patterns. Ask whether a framework supports ReAct, Reflexion, plan-and-execute or orchestrator-worker designs, and which one fits how your task breaks down.
- Set autonomy per task. Because autonomy is a spectrum, grant the least latitude that still gets the job done and widen it only as results justify.
This checklist suits engineering and product teams making a first decision. Teams already running agents can use it as a review, particularly the first item, since many systems built as agents turn out to follow a fixed path.
Frequently asked questions
Is there research behind the agent definition?
Yes. MIT Sloan cites a 2025 research paper by Kellogg and co-researchers that explores the economic implications of agents that enhance large language models, and its own definition comes from that research context. See MIT Sloan’s agentic AI explainer.
Does an AI agent have to be built on a large language model?
LangChain’s definition does rely on a large language model deciding the control flow. The MIT Sloan, IBM and AWS definitions describe autonomy, perception and self-directed action without limiting the technique, so the language-model requirement depends on whose definition you adopt.
Can agents work outside a chat window?
Yes. AWS says agents often extend their capabilities by connecting to external software, APIs or devices, and MIT Sloan describes them using external tools in digital environments. A chat box is one possible interface, not the defining feature. Details are in AWS’s overview of AI agents.
