n8n Introduces Dedicated Agents to Complement Workflows

n8n Introduces Dedicated Agents to Complement Workflows n8n has introduced Agents, a pre-built system designed for open-ended or conversational tasks that operates alongside traditional workflows without replacing existing AI Agent nodes. Agents manage multi-turn, unstructured jobs by working out execution steps independently using assigned models, tools, and workflows. Workflows can serve as scoped tools for agents, while workflows can also trigger agents via the new Message an Agent node. Built-in functionality includes sessions, memory, versioning, approvals, and execution visibility, shielding credentials from direct agent access. The legacy AI Agent node remains unchanged and fully supported alongside the new feature. Autonomous Execution Alongside Workflows While fixed automations continue to run effectively via standard workflows, n8n Agents are engineered for tasks where inputs and step-by-step requirements vary, such as conversational Slack requests or dynamic support tickets. Instead of requiring users to manually assemble chat triggers, memory nodes, and AI Agent nodes on a canvas, n8n Agents arrive pre-configured with memory, sessions, channels, versions, and approval mechanisms. Bidirectional Integration and Tool Scoping Agents and workflows can operate in both directions. Agents can employ pre-existing workflows as tools, allowing teams to limit an agent's permissions—such as letting it trigger a designated CRM update workflow rather than granting direct write credentials. Conversely, workflows can embed an agent as an individual step using the new Message an Agent node, passing input and receiving the agent's contextual response. Observability, Setup, and Compatibility Running agents offer full session observability, displaying inputs, outputs, and the specific tools called at each step. Users can create agents manually in the Agents tab or through n8n Assistant, testing models immediately using Gateway credits without external API keys. Existing configurations using the AI Agent node remain unaffected and continue to work as before.

n8n 推出 Agents 功能：支持自主规划与工作流协同的开箱即用型智能体 n8n 正式推出独立于工作流的新功能 Agents。用户只需以自然语言设定目标并配置模型与工具，智能体即可自主推导解决步骤，适用于多轮交互与非固定流程任务，并可与已有工作流双向互通。 Agents 提供开箱即用的预配置体验，内置记忆、会话、发布渠道、版本与审批等基础能力，无需从底层工作流搭起。 智能体与工作流深度结合：工作流可作为工具受智能体调用以限制系统权限，工作流也可通过新增的 Message an Agent 节点调用智能体。 原有的 AI Agent 节点保持不变，已构建的内容不受影响。 可通过 Agents 标签页或 n8n Assistant 快速创建，借助 Gateway 额度无需自备模型 API 密钥即可直接测试。 针对开放式多轮任务的全新构建方式 传统 n8n 自动化擅长处理固定序列的流程，但在面对每次输入各异、需要根据前一步结果动态调整的场景（如 Slack 故障排查、支持邮件处理）时，搭建固定工作流相对繁琐。Agents 类似于开箱即用的成套电脑，集成了记忆、会话管理和版本控制等功能，用户只需提供提示词并接入工具，智能体便会自行推导执行步骤，会话中也会完整记录每步调用的工具与输入输出。 与工作流的双向协作及权限隔离 Agents 能够将用户已有的 n8n 工作流作为工具使用，从而实现严格的权限管控。例如智能体在记录工单时仅能触发特定写入工作流，而无需直接获取底层 CRM 的凭据。反向操作中，工作流可通过新加入的 Message an Agent 节点随时将智能体作为其中一个环节调用，且更新已发布的智能体会同步作用于所有引用它的工作流。 上手与快速启动 用户可在 Agents 选项卡中手动创建，或向 n8n Assistant 描述需求由其自动搭建。依托 Gateway 提供的信用额度，用户初期无需配置第三方 AI 服务的 API 密钥即可挑选模型进行对话测试，后续亦支持换用自定义密钥。

n8n now has **Agents**. You describe what an agent should do, give it a model and the tools and workflows it can use, and it works out the steps itself. You can talk to it in Slack, run it on a schedule, or call it from any workflow, and it's the same agent in every one of those places. With agents, it's easier to handle open-ended or back-and-forth jobs that could get complicated to build as a fixed workflow. And you can build an agent without first learning how workflows work. Agents sit **next** to your workflows, and the two are built to work together. Your agent can use your workflows as tools, so you decide exactly what it's allowed to do in your systems. And when a workflow needs an agent for one of its steps, the new **Message an Agent** node calls it from inside the workflow. If you use the AI Agent node today, nothing about it has changed. Everything you've built keeps working. 👇 **Check out the following video which goes into detail with a real world example.** 👇 For as long as n8n has existed, automating something meant working out the steps and adding them to the canvas. That's still the right approach for plenty of work. A lead comes in, you enrich it, score it, route it. The more fixed the sequence, the better a workflow fits, even if one of the steps is a model making a decision. Two things have changed: That matters most for jobs where the input is different every time. Someone on the team asks in Slack why a customer's usage dropped last month. Answering takes a few rounds: pull the account, ask which product line they mean, check the support history, come back with a summary and a follow-up question. The next step depends on the answer to the last one, so there's no way to lay out the process in advance. The same goes for a support email, a new GitHub issue, or any request where you know the domain but not the task. For those jobs, you want to describe the goal in plain language, give the agent access to what it needs, and let it work out the steps as it goes. Until now, taking on a job like that in n8n meant fitting an open-ended conversation into a workflow. You could do it, and plenty of people did, but it took some work to build. Agents are built for those jobs. Sometimes you want the workflow in charge, with the agent as a step inside it. Sometimes you want the agent in charge, with workflows as tools. n8n now gives you both. You have, and so have we. A chat trigger, a memory node, an AI Agent node with a few tools attached, and a workflow around it to hold it all together. Lots of n8n users run agents built exactly this way, and they work. The difference is how much of it you have to assemble yourself. It's a bit like building your own PC versus buying a pre-built one. Building your own gets you a working machine, but you pick every part and connect every cable. A pre-built PC arrives with everything connected and ready to go, and you can still open it up and add what you need. n8n Agents are the pre-built version. Memory, sessions, channels, versions and approvals come with every agent, so you spend your time on what the agent should do. You can still extend it with any tool or workflow you like. You don't need to know how workflows work to build one. You describe what the agent should do in plain language, and when you open it later, its instructions read like a brief you'd write for a colleague. If your first agents were built in a tool where you write instructions and attach tools, this is how you build them in n8n. Each agent has: Once it's running, you can see what it did. Each session shows every step the agent took, which tools it called, and the input and output of each call. An agent can use three kinds of tools, and you choose per tool: Workflows are the part we're most excited about. Every workflow you've built in n8n is something an agent can use, and none of it needs to change. Take a support agent handling the inbound queue. It has three workflows as tools: The agent reads each ticket, decides whether it needs account context, drafts a reply, logs what happened, and escalates anything urgent. It decides when each workflow runs. What happens when a workflow runs is fixed, step by step, the way you built it. That second workflow is the one to notice. Without a workflow in between, logging a note would mean giving the agent write access to your CRM and trusting its instructions to keep it to the notes field. With the workflow, the agent never holds that credential. It holds a workflow that adds a note and does nothing else. Around that, the controls you'd expect: So you choose, per use case, how much is defined process and how much the agent decides. And you can move that line later: pull a task out of the agent into a workflow when you want it fixed, or hand a workflow to an agent when you want it used with discretion. For anything sensitive, start where the blast radius is small: scoped tools, a test channel, and approvals on any action that writes to a system of record. A support agent is only useful if the team can count on it. Three things help with that: Agents work in the other direction too. When a workflow needs an agent for one of its steps, add the **Message an Agent** node. It sends the agent a message built from your workflow data and passes the agent's answer to the next node. The agent brings its own instructions, tools and memory, so the node itself stays simple: you define what goes in, and you get the answer back. It's the same published agent your team uses everywhere else, so when you update the agent, every workflow that calls it gets the update. The AI Agent node is still there and works as it always has. Use whichever fits the job. Open the **Agents** tab and click **Create Agent**, or describe what you want to n8n Assistant. The Assistant picks the best fit for the job, workflow or agent, and builds it. If you already know you want an agent, say so. Either way you get the instructions, tools and channels drafted, ready to test. With Gateway credits, you don't need an API key from an AI provider to try it. Pick a model, have the first conversation, and bring your own keys later if you want to. Between the Assistant and Gateway credits, getting started takes little more than a description of what you want. What you end up with is an agent you can read and change yourself. If you're not sure what to build first, try an internal Slack bot connected to one or two systems your team asks about all the time. A few other ideas: The full guide is in the docs: [Build and manage agents](https://docs.n8n.io/build/build-and-manage-agents). We have more coming for agents soon. In the meantime, tell us what you built, what it did, and where it fell down. We're reading the [community forum](https://community.n8n.io/t/introducing-n8n-agents-a-new-way-to-build-agents-you-set-up-once-and-use-anywhere/306323) closely.