n8n Partners with SAP to bring Visual AI Workflow Orchestration to Enterprise

Announcing SAP’s strategic investment in n8n

Today we are announcing that SAP has invested in n8n. The investment values n8n at $5.2bn, more than double our valuation from less than a year ago. Alongside the investment, we are also embedding n8n natively inside SAP's Joule Studio. It's a significant moment, and I want to share what it means. I started n8n almost seven years ago. Since then, the community that's grown around it (now 1.7 million monthly active builders) is what's shaped the platform. What's moved fastest this past year is enterprise adoption: more than 1,400 enterprise customers, including Fortune 500 teams running mission-critical processes. This partnership is one of the first moves toward bringing n8n closer to the systems our enterprise customers run on, starting with SAP. SAP is one of the most trusted names in enterprise software, 99 of the 100 largest companies in…

How n8n is powering the next wave of AI automation at Mercedes-Benz

AI automation at enterprise scale is still more about promise than reality for most organisations. Proof-of-concept projects stay in proof-of-concept. Isolated tools never connect to the systems that matter. Mercedes-Benz is doing it differently. The German OEM has rolled out n8n as its global low-code automation platform, deploying it across business units worldwide to bring AI-powered workflows into its core operations. For other companies, this offers a clear picture of what scaling AI automation actually requires. Built for enterprises that need control For an organisation operating across multiple regions and regulatory environments, sovereignty over data and architecture isn't optional. Mercedes-Benz chose n8n in part because of its self-hosted, cloud-agnostic deployment model – meaning workflows run on their own infrastructure, sensitive data stays where it belongs, and the company retains full control over its automation layer. That model supports organisations operating…

Advanced RAG: Data Cleaning and Retrieval Techniques

Retrieval-augmented generation (RAG) makes queries smarter, arming them with proprietary data and contextualized knowledge. But even the best RAG methods produce inaccurate answers, and context windows polluted by noisy data. Advanced RAG emerged to fix that. RAG isn’t a single method — there are several ways to boost the accuracy and reliability of LLM outputs with this framework. This guide covers the advanced LLM RAG techniques teams use in production. Why does basic RAG fall short? Basic RAG is sometimes called Naive RAG because of its simple nature. It indexes a set of documents via a single dense vector, and then embeds them, retrieves the top-K matches, and passes them to an LLM. Simple RAG in LLM systems works well in some scenarios, but they often struggle in real-world use. Here are a few common limitations: - Poor recall: It…

Building Better Agents: LLM Memory Types and Trade-Offs

Engineers often treat large language model (LLM) memory as a simple feature toggle. But in a production environment, memory acts as an agent’s central nervous system, determining whether a system feels like a coherent assistant or a fragmented script. In practice, LLM memory is a high-stakes design challenge. To build resilient agents, you must move beyond basic chat history and navigate a complex decision surface where every choice impacts scalability and reliability and implement best prompt engineering techniques. In this guide, we’ll analyze the trade-offs of architecting persistent memory into your AI systems, examining how to choose the right memory types, implementation layers, and consistency for production-grade performance. What’s LLM memory? An LLM with memory is a stateful system that integrates static training with real-time execution. To understand how LLM memory works, you have to distinguish between parametric knowledge —…

AI Agent Architecture Patterns: From Prototype To Production

The gap between prototypes and production-ready systems usually comes down to how you structure the underlying logic. While it’s natural to focus on the specific code used to trigger a model, the real engineering challenge is selecting the right AI agent architecture patterns to maintain stability under unpredictable, real-world inputs. A strong framework prioritizes how control flows between components, how tasks execute, and how failures are contained. Instead of reacting to individual model responses, you manage how data flows and where decisions happen. Each design choice acts as a safeguard, ensuring a single hallucination or API timeout doesn't compromise the automation. Misapplying these patterns often introduces failure modes that no amount of prompt engineering can fix. Choosing an autonomous loop where a step-by-step (pre-defined) sequence is required can stall a workflow. Centralizing control in a high-latency environment can slow every…

Production AI Playbook: Evaluation and Monitoring

This post is part of a series that explores proven strategies and practical examples for building reliable AI systems. New to n8n? Start with the introduction. Find out when new topics are added to the Production AI Playbook via RSS, LinkedIn or X. The Silent Drift Problem Your AI workflow passed every test. Classifications were accurate. Responses were on-point. You shipped it, and for two weeks, everything looked great. Then support tickets started trickling in. Customers were getting responses that missed the point. Classifications were landing in the wrong buckets. Nothing broke. No errors in the logs. The AI just quietly got worse. This is silent drift, and it's one of the most common failure modes in production AI systems. Unlike traditional software, where a bug either crashes or doesn't, AI outputs degrade gradually. A model update changes behavior slightly.…

Deploy n8n agents that show up as members of the team inside Microsoft apps

With the general availability of Microsoft Agent 365, n8n users can now build AI agents that show up inside the Microsoft 365 applications where people get work done. Now you can @mention agents built in n8n from Microsoft Teams, add them to emails in Outlook, or tag them in Word to bring them into your work. Microsoft Agent 365 gives each agent its own Entra ID, so employees can grant it access to Microsoft 365 services like SharePoint and Teams channels the same way they would a teammate. Microsoft handles identity, access policies, and compliance through Purview and Defender. Agent 365 also handles agent lifecycle management actions, rules and policies, analytics in the Microsoft 365 Admin Center; providing an effective and scalable way to control agents at scale through familiar tools. The n8n canvas is where you design what the…

ReAct Agent: Architecture, Implementation, and Tradeoffs

Some tasks can't be solved in a single LLM call. When a question requires looking up data, processing it, and making a decision based on the result, a one-shot response will either hallucinate the answer or give a shallow one. ReAct agents solve this with an iterative reasoning loop. Instead of trying to answer everything at once, the agent breaks the problem down step by step: think about what's needed, call a tool, observe the result, and decide what to do next. Each cycle grounds the model's reasoning in real data before moving forward. This Reasoning + Acting pattern turns opaque agent behavior into something you can follow, debug, and audit - every thought and action is visible in the execution trace. Here's how the ReAct pattern works, when to use it over other agent approaches, and how to build…

LLM Tool Calling: How It Works and How To Implement It

Large language models (LLMs) are brilliant reasoners. But without a way to interact with the world, they’re essentially locked behind a glass wall— they have enough knowledge to explain a refund policy in perfect detail but lack the hands to actually trigger one. For developers, this disconnect between reasoning and action is what separates sophisticated chatbots from production-grade agents. LLM tool calling offers an escape from the training-data silo, allowing models to move from passive text generation to active system participation. But the real engineering challenge isn’t just getting the model to output a valid JSON or a tool call — it’s building the orchestration, security, and observability required to ensure those calls don’t fail in a production environment. Here’s a rundown of what LLM tool calling is and how it works at scale. What LLM tool calling means? LLM…

Human-in-the-Loop vs. Human-on-the-Loop: When To Use Each System

There are three main ways people control the quality of AI systems: human-in-the-loop (HITL), human-on-the-loop (HOTL), and hybrid systems using both. These frameworks determine how systems make decisions and where humans intervene. Each approach affects scalability, risk tolerance, and operational expenses. This oversight spectrum gives you a wide range of potential workflows depending on the task, whether your team needs tight human-driven control or occasional check-ins. In this guide, learn the difference between human-in-the-loop versus human-on-the-loop. Plus, discover when to use each approach and how to implement it in your work. What’s human-in-the-Loop (HITL)? HITL is a process where AI performs tasks but humans control final decisions, preventing the system from executing certain actions without approval. This is a synchronous control pattern. The workflow stops at a decision gate until a human provides a required signal. For example, AI processes…

Build and Update Workflows with n8n's MCP Server

Describe what you want from Claude, ChatGPT, or your IDE, and get a ready-to-run workflow in a few minutes, built directly in n8n. No more copy-paste, no more back-and-forth. n8n's MCP server can now build workflows from a prompt (and not just run them)! The MCP server has been around for a few months, but previously you could only execute existing workflows. Now you can build new ones from scratch and update existing ones, directly in your n8n instance. - Go from prompt to a ready-to-run workflow. Tell your AI client what you want. It builds the workflow, validates it, runs it, and fixes itself if something breaks. No messing with JSON files or copy-pasting errors. - Works in whatever AI client you already use. Claude, ChatGPT, Cursor, Windsurf - if it “speaks” MCP, you can point it at n8n.…

How to evaluate the performance of AI agents?

Traditional software testing is straightforward: you give input X and expect output Y. If the function returns the wrong value, the test fails. LLM-based agents don't work that way. They're non-deterministic which means the same prompt can produce different outputs across runs. They operate over multiple steps, making decisions about which tools to call, what parameters to pass, and how to interpret results. An agent can complete an execution without errors and still hallucinate facts, miss the user's intent, or take unnecessary steps. Classical testing may not catch problematic outputs produced by an AI Agent. When building AI Agents, you face three main evaluation challenges: - You're evaluating trajectories, instead of just outputs. An agent might give the correct final answer but call the wrong tools, use the wrong parameters, or take five steps when one would do. If you…

Workflow Automation vs. Orchestration: Architectural Differences That Matter at Scale

How much do workflow automation versus orchestration architectures differ at scale? Each solves different problems. Workflow automation handles individual tasks, while orchestration coordinates multiple tasks into end-to-end processes. Choosing the right approach or combining them shapes how your processes behave under real-world conditions. This article breaks down the architectural differences between workflow automation and orchestration. You’ll see how those differences affect reliability and behavior in production systems and learn how to choose the right approach for your needs. What is workflow automation? Workflow automation runs a sequence of tasks when triggered by a preset event or criteria. It’s designed for a bounded scope where logic flows from point A to point B. These systems prioritize efficiency in repetitive business processes (sending notifications, updating records, moving data between systems) by relying on stateless execution/focusing on task-level execution. Traditional (stateless) automation workflows…

Orchestration vs. Choreography: Which One to Choose – or Use Both?

Orchestration vs. choreography isn’t just an architectural choice – it’s a decision about how your system thinks. Orchestration relies on one central controller to coordinate every step of a workflow, providing full visibility and control. Choreography takes an opposite approach. Services communicate through events and act independently instead of sharing a single point of control. Both patterns solve the problem of how services collaborate, but they do so in fundamentally different ways. Choosing one over another directly impacts how you can scale, debug, and operate your system in production. In this article, we’ll compare orchestration and choreography and discover the tradeoffs between control and autonomy. Microservices orchestration vs. choreography explained In orchestration, a central controller acts like a conductor. It tells each microservice when to execute its logic and tracks the outcome. This provides a clear and predictable control flow.…

We need re-learn what AI agent development tools are in 2026

This article was written by Andrew Green, technical writer and industry analyst. We pay Andrew, but he refuses to write anything else but his own opinion. The big boys entered the market, OpenClaw appropriated the MCP security strategy, and everyone started vibe coding but only if they already knew how to code. It really feels like 2025 was the year of agents, mainly because the industry came to a consensus about how we expect an agent to behave. That and because we found we can bypass context window sizes by spawning sub-agents. When we first wrote the Enterprise AI agent development tools, we focused a lot on the building blocks of writing agents, such as RAG, memory, tools, and evaluations. One year later, all these capabilities appear to have been commoditized to some degree. We now expect most vendors to…

RAG System Architecture: Components, How To Implement, Challenges, and Best Practices

A simple retrieval augmented generation architecture (RAG) setup usually works fine with a few documents and a basic retriever, but those setups fall apart quickly once you try to run them in production. Small issues that don’t matter much in controlled settings — slightly off chunks or slow lookups — turn into high latency, dangerous AI hallucinations, and spiraling API costs in real-world use. In this guide, we’ll break down the RAG system architecture components and the trade-offs to consider when implementing production-ready RAG architecture, challenges, and best practices. What is RAG architecture? RAG architecture refers to how you design your retrieval system: which embedding models and vector types to use, how to chunk and index documents, and whether to add reranking. This is different from the RAG pipeline (the step-by-step data ingestion) and RAG application (the complete end-user solution).…

Production AI Playbook: Introduction

The Production AI Playbook introduces the patterns and capabilities teams use to build production AI systems with n8n. It reflects lessons learned from teams integrating AI into real operational systems, where reliability, governance, and maintainability matter as much as model capability. New sections will be added on a rolling basis, covering how to combine deterministic automation with AI, design scalable agent architectures, maintain human oversight, monitor performance, and operate AI workflows reliably in production. Find out when new topics are added via RSS, LinkedIn or X. n8n’s workflow architecture n8n is a node-based workflow automation platform where composable nodes chain together into execution pipelines. Workflows orchestrate data movement, system integrations, business logic, and AI steps in one place. This architecture makes it easier to visualize, explain, and control how automation systems operate. n8n is source-available under a fair-code license, which…

Production AI Playbook: Deterministic Steps & AI Steps

This post is part of a series that explores proven strategies and practical examples for building reliable AI systems. New to n8n? Start with the introduction. Find out when new topics are added to the Production AI Playbook via RSS, LinkedIn or X. The Reliability Gap in AI Workflows Here's a pattern that plays out across teams building with AI. You connect an LLM to your workflow, feed it some data, and get impressive results. At a glance, the summaries are sharp. The classifications generated by the AI system feel right. The generated content sounds natural. So the team ships it. Then the edge cases start showing up everywhere. A customer name with special characters breaks the parsing. A support ticket written in sarcasm gets classified as positive feedback. An LLM generates a perfectly worded email but hallucinates a product…

Firecrawl + n8n: real-time web data for your AI workflows

Firecrawl is offering 100,000 credits when you connect through n8n Cloud We've partnered with Firecrawl to make it easier than ever to bring web data into your n8n workflows. Connect to Firecrawl in one step, create an account without leaving the canvas, and start building immediately on n8n Cloud. No API keys to track down, no separate sign-up flow. This builds on recent improvements to n8n-managed authentication in Cloud, where n8n handles credential setup and lets you connect to dozens of supported services in one step during node setup. Already on n8n Cloud? Just add the Firecrawl node to your workflow and click "Connect to Firecrawl" when setting up credentials. Not on n8n Cloud yet? Try it now so you can take advantage of this offer from Firecrawl and explore the power of real-time web data in your workflows risk-free.…

Production AI Playbook: Human Oversight

This post is part of a series that explores strategies, shares best practices, and provides practical examples for building reliable AI systems in n8n. New to n8n? Start with the introduction. Find out when new topics are added via RSS, LinkedIn or X. The Control Problem Nobody Talks About You built an AI agent that drafts emails, summarizes support tickets, and updates your CRM. It works flawlessly in testing. Then you deploy it to production, and suddenly you're explaining to your VP of Sales why a prospect received a reply promising a 90% discount that doesn't exist. The technology is capable, but capability without oversight is a liability. Every team deploying AI into workflows that touch customers, data, or decisions eventually hits the same realization: you need a way to keep humans in the loop without killing the speed that…

Build Multi-Domain RAG Systems with Specialized Knowledge Bases

This Verified Node Spotlight was written by Jenna Pederson, Staff Developer Advocate for Pinecone. Imagine you manage multiple vacation rental properties. A guest at one of your properties texts asking how to turn on the heat, but you accidentally send them instructions for your other property's completely different thermostat. You look unprofessional, your guest is confused, and now they are cold. This isn't just a customer service nightmare, but a knowledge management problem. When you shove all your property documentation into one knowledge base, you're asking your AI to search through everything every time to figure out what's relevant. It's like creating a spreadsheet with 10,000 rows and 30 columns and never separating your data into tabs. Our brains don't work that way, and neither does our business or AI. The same principle that pushes us to separate spreadsheet tabs…

20 Best MCP Servers for Developers: Building Autonomous Agentic Workflows

The Model Context Protocol (MCP) feels like magic until you try to deploy it. You connect Claude to your local database, ask a question using natural language, and it executes complex SQL instantly. But the moment you close your laptop, that agent dies. It cannot react to customer emails, run on a schedule, or trigger alerts. Your powerful tools are trapped in your local IDE. In this guide, we will break down these barriers. We will categorize the best MCP servers for coding, data, and ops, and then show you how to orchestrate them using n8n. By the end, you will have a curated toolkit and a method to turn temporary chats into persistent, automated systems. This guide is optimized for developers who understand LLM basics but want to build production-grade AI workflows. Let's dive in! How we composed this…

n8n Tunnel Service Discontinued

We are discontinuing the n8n Tunnel Service and the related --tunnel option. This post explains why, what changes for you, and how to set up secure alternatives for local webhook development. TL;DR - The n8n Tunnel Service has been disabled and is being discontinued. - If you need a public URL for local webhook testing, use a third-party tunneling service such as Cloudflare Tunnel or ngrok. - Regardless of the tunnel provider, treat your local webhook endpoint like a production entry point: verify signatures, use secrets, and minimize exposure. What was the n8n Tunnel Service? The n8n Tunnel Service provided a simple way to expose a locally running n8n instance to the public internet for development and testing. This was commonly used to receive webhooks from third-party services (for example GitHub, Stripe, Slack, and many others) when developing workflows locally.…

15 Practical AI Agent Examples to Scale Your Business in 2026

AI agents bring exciting functionality to building with AI, adding usefulness that goes beyond automation. They are also already delivering value across industries, from fraud detection and customer support to logistics, HR, manufacturing, agriculture, and energy optimization — often by automating repetitive decision-heavy work. Let’s dig into what AI agents are, the various types that exist, and some AI agent examples across industries — so you can understand how to use them in your work and build them using a tool like n8n. What is an AI agent? An AI agent is a system that operates autonomously to perform tasks, using AI technologies (like LLMs), connected tools, and even coordinates with other agents as part of more complex workflows. The major factors that define an AI agent include the ability to: - Perceive an environment - Take actions that affect…

How n8n Handles Vulnerability Disclosure - and Why We Do It This Way

As n8n grows, so does the scrutiny our codebase receives from the security community. That is a good thing. In the past months we have published many security advisories, and with that comes natural questions from our users: How much notice will I get before a vulnerability is published? Why can't I get more time? And how does all of this work when the source code is publicly available? We want to answer these questions openly, because we believe that a well-understood disclosure process builds more trust than a secretive one. The tension at the heart of open-access security n8n's source code is publicly available. This is core to who we are — it enables our community to inspect, extend, and contribute to the platform. But it also creates a specific challenge for security patches that closed-source vendors do not…

Introducing Chat Hub

If you’ve been following the rise of AI in the workplace, you know the challenge: AI usage is becoming commonplace, but it’s often unmanaged. Chat Hub changes that by providing a single, unified interface for your organization to direct users for all AI-related tasks and processes, bringing the power of n8n’s AI agents to every team member securely and simply. The Problem: The Rise of "Shadow AI" As AI capability and understanding grows, users across organizations are eager to use AI-powered tools to speed up their work. However, this can often lead to "Shadow AI": unmanaged and unmetered usage that causes headaches for IT and operations teams. This can include: - Inconsistency: Staff might regularly use their own AI-powered tools and models without consideration for their organization’s standards. - Security Risks: Without a centralized system, data integrity and security can…