What Is an AI Agent? Anthropic, OpenAI, and LangChain, Compared
What Anthropic says
Anthropic groups everything under the banner of agentic systems, then draws one architectural line that does most of the work. In Building Effective Agents, they separate workflows, where language models and tools are orchestrated through predefined code paths, from agents, which they describe as systems where LLMs “dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.”
The tell in Anthropic’s framing is control. If you wrote the path and the model just fills in the blanks, that is a workflow. If the model decides the path as it goes, that is an agent. Anthropic is also refreshingly blunt that the more autonomous option is not automatically the better one: workflows give you predictability for well-defined tasks, and you should reach for an agent only when the flexibility is worth the cost.
What OpenAI says
OpenAI keeps its definition focused on the outcome. In its practical guide to building agents, it states plainly that “agents are systems that independently accomplish tasks on your behalf.”
The emphasis here is independence. Conventional software lets you automate your own workflows; an agent runs those workflows for you, with enough autonomy that you are not approving every step. OpenAI frames an agent around three working parts: a model doing the reasoning, tools for gathering context and taking action, and instructions plus guardrails that govern behavior. Same core idea as Anthropic, pointed at a different question. Anthropic asks who controls the process. OpenAI asks who the work gets done for.
What LangChain says
LangChain gives the most technical definition, and the most honest caveat. Its co-founder Harrison Chase, writing on the LangChain blog, notes that everyone has a slightly different definition of an agent, then offers his own: an AI agent is a system that uses an LLM to decide the control flow of an application.
What makes LangChain’s version useful is that it refuses to treat “agent” as a binary. Chase points out that even a simple setup where an LLM routes a request between two paths has a small amount of agentic behavior. Agency is a dial, not a switch. That reframing matters, because most of the arguments about whether something “is really an agent” are actually arguments about where on the dial it sits.
Where they agree, and where they diverge
Line the three up and the shared core is obvious. In every definition, a language model is making decisions about what to do next, using tools, instead of executing a script you hard-coded. The differences are about what each lab chooses to emphasize.
| Source | How they define an AI agent | The emphasis |
|---|---|---|
| Anthropic | An LLM that dynamically directs its own processes and tool usage | Who controls the path |
| OpenAI | A system that independently accomplishes tasks on your behalf | Independence, on the user’s behalf |
| LangChain | A system that uses an LLM to decide an application’s control flow | The LLM deciding what’s next, on a spectrum |
Read together, they answer three different questions about the same thing: control (Anthropic), autonomy and purpose (OpenAI), and decision-making (LangChain). None of them contradicts the others. Stack them and you get a sturdier definition than any one provides alone: an agent is a system where the model, not your code, decides the next step, acting through tools, with enough independence to pursue a goal rather than complete a single instruction.
The agency spectrum
The most useful thing to take from all three is that agency is a gradient. A plain LLM call sits at one end. A fully autonomous, multi-step agent sits at the other. Most real systems live somewhere in the middle, and the labels blur exactly where the marketing noise is loudest.
Anthropic draws its workflow-versus-agent line based on whether the model controls the path. LangChain would tell you the router is already a little agentic. OpenAI cares most about the autonomous end, where the system genuinely runs tasks for you. They are all describing the same gradient, just pointing at different stretches of it. Once you see the spectrum, “is it really an agent?” becomes the more useful “how much of the control flow is the model actually deciding?”
What marketers actually mean by “agent”
Here is where the labs and the market part ways. When a vendor’s landing page says “AI agent,” it rarely maps to any of the three definitions above. In practice, marketing uses “agent” to mean almost anything with an LLM inside it: a smarter chatbot, a content generator, a sequence of automated steps, an assistant that drafts replies. Some of those are genuinely agentic. Many are workflows wearing a costume, which is the agent-washing problem I covered in depth in my agentic AI in marketing breakdown. It is real enough that Gartner estimates only around 130 of the thousands of vendors claiming agentic capability are delivering the real thing.
So when someone sells you an “agent,” run it against the one question all three definitions share: does the language model decide the next step, or did someone script it in advance? If the model is choosing the path, picking the tools, and adapting to what comes back, you have an agent in the sense Anthropic, OpenAI, and LangChain all mean. If the steps are fixed and the model is just generating text inside them, you have a workflow, and you should price it and trust it like one. That is not a knock on workflows. It is how you stop paying agent prices for automation, a shift I unpack further in why marketing is being rewritten by agents.
Frequently asked questions
What is an AI agent in simple terms? A system that uses an LLM to decide its own steps toward a goal, taking actions through tools instead of following a script you wrote. It decides; a chatbot only answers, and a workflow only follows rules.
What is the difference between an AI agent and agentic AI? “AI agent” is the thing: the system that acts. “Agentic AI” is the broader adjective for the whole category of systems that show that autonomous, goal-directed behavior. Anthropic treats both workflows and agents as agentic systems, with agents being the more autonomous end.
What is the difference between an AI agent and a chatbot? A chatbot responds to messages. An AI agent takes actions across tools to complete a task, decides its own next steps, and adapts based on results. The agent can do things; the chatbot mostly says things.
How do Anthropic, OpenAI, and LangChain define an AI agent? Anthropic: an LLM that dynamically directs its own processes and tool usage. OpenAI: a system that independently accomplishes tasks on your behalf. LangChain: a system that uses an LLM to decide an application’s control flow. Same core, different emphasis.
What are examples of AI agents? Autonomous research-and-outreach systems, coding agents that fix issues across files, analytics agents that investigate anomalies and recommend fixes, and orchestration agents that coordinate work across your tools. The common thread is the model deciding what to do next, not a person scripting each step.
Sources
- Anthropic, Building Effective Agents
- OpenAI, A Practical Guide to Building Agents
- LangChain, What Is an Agent? (Harrison Chase)
- Gartner, Over 40% of Agentic AI Projects Will Be Canceled by End of 2027 (June 2025)
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