Stefan Papp

Building AI agents in n8n? These 4 components are the core: 1️⃣ Brain – the LLM powering your agent (OpenAI, Mistral, DeepSeek, Anthropic, etc.) 2️⃣ Prompts – define your agent’s role, tone, and behavior 3️⃣ Memory – track past conversations (use simple memory, MongoDB, Redis, etc.) 4️⃣ Tools – give your agent real-world access (like Gmail, HTTP requests, or other workflows) This setup is what separates AI agents from basic chatbots.

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🚀 Is Data Mesh 2.0 coming? Zhamak Dehghani introduced Nextdata OS on 22.04.2025. She named it “The development and operating platform for autonomous data products“. In their latest Q&A session with Joe Reis and others they discussed several aspects. I put the most important point together in my latest blog ▶️ https://lnkd.in/eBsHs383 ❇️ Data Products ❇️ Product Mindset ❇️ Purpose of Nextdata OS ❇️ Domain Ownership & Gateway Data Products ❇️ Companies where data is the product ❇️ Functional description of Nextdata OS ❇️ Open World assumption ❇️ How dbt fits in Furthermore i put together the most important concepts discussed.

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The Era of Specialized AI Agents - Collaboration and Orchestration There is no doubt that companies will rely on AI Agents to manage their operations. But here is the big question, Can a single provider offer all the AI Agents needed for every function in a company? The answer is clear – no, it is not possible. Let us look at the reality. Most organizations handle functions such as, Hiring Employee Policies Training & Development Finance Auditing Payroll Management Production Marketing Sales Customer Support Tax and more Each of these requires domain expertise. Expecting a single provider to specialize in all of these areas is unrealistic. This is not the era of just automating repetitive tasks and running them periodically. We are in a time where each of these functions must be handled by specialized teams empowered with AI Agents built specifically for those functions. It is not about just executing tasks; it is about taking complete responsibility for the process, end-to-end. This brings us to a critical challenge: How will AI Agents from different specialized providers collaborate? The answer lies in an orchestrator – a platform to manage and integrate these agents seamlessly. But this raises more important questions: 1. What is the hiring policy for AI Agents? Should they be treated like virtual employees and added to HRMS systems for tracking and management? 2. Cloud or On-Premises? Where should this orchestrator live? Cloud solutions offer scalability, but sensitive industries like healthcare may prefer on-premises solutions for security and compliance. 3. Does a cloud-based orchestrator mean moving the entire organization to the cloud? For many businesses, especially in regulated industries, this may not be acceptable. 4. Is this the right approach for healthcare? Healthcare requires a human touch, strict compliance, and patient-centric care. On-premises orchestration might be the right choice to maintain trust and privacy. Here is the takeaway, This is not about one-size-fits-all automation. Every function needs specialized AI Agents, built and managed by experts in their respective fields. An orchestrator will play a vital role in ensuring these agents work together seamlessly, delivering measurable outcomes. For industries like healthcare, on-premises or hybrid setups will lead the way, where AI supports human efforts rather than replacing them. What is your view? Are we ready for this shift to specialized AI ecosystems? ZautoAI Giridharan Palanisamy MONESHKUMAR NATARAJAN HARIPRAKASH CHINNASAMY #SpecializedAI #AIOrchestration #FutureOfWork #AIForHealthcare

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Building AI Agents from Scratch — The Complete Guide 🔍 If you want full control and flexibility over your AI Agents, this tutorial walks you through every step of building one from the ground up. Here’s what you’ll learn: 🔹 LLM Integration with Instructions Learn how to integrate Anthropic’s Claude 4 Sonnet API as your agent’s core reasoning engine — complete with system prompts that define behavior and responses. 🔹 Conversation Memory System Understand how to give your agent short-term memory, enabling contextual, multi-turn conversations that feel more natural and human-like. 🔹 Tool Use Capabilities Explore how to extend your agent’s abilities by connecting external tools — from simple calculations to more advanced function calls. 🔹 Agentic Loop Architecture Build a decision loop that lets your agent reason, act, and refine its output iteratively for accurate, multi-step task completion. 🔹 Full Working Implementation Get a complete example with working code to test different agent behaviors — from direct responses to multi-tool orchestration. 📘 Author: Leonie Monigatti

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→ 𝐔𝐧𝐥𝐨𝐜𝐤 𝐭𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐋𝐋𝐌 𝐓𝐨𝐨𝐥𝐬 𝐢𝐧 2025: 𝐀𝐫𝐞 𝐘𝐨𝐮 𝐑𝐞𝐚𝐝𝐲? The AI landscape is evolving faster than ever. The secret to building smarter, faster AI apps isn’t just about picking a powerful model. It’s about mastering the ecosystem surrounding it. 𝐇𝐞𝐫𝐞’𝐬 𝐲𝐨𝐮𝐫 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐜𝐡𝐞𝐚𝐭 𝐬𝐡𝐞𝐞𝐭 𝐟𝐨𝐫 2025 - 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐭𝐨𝐨𝐥𝐬 𝐜𝐚𝐧 𝐦𝐚𝐤𝐞 𝐨𝐫 𝐛𝐫𝐞𝐚𝐤 𝐲𝐨𝐮𝐫 𝐬𝐮𝐜𝐜𝐞𝐬𝐬. • 1. LLM Models: Claude, DeepSeek, Mistral, OpenAI, Gemini, LLAMA, xAI - the brilliant minds powering AI solutions today. Choose wisely; your model is the brain of your app. • 2. Vector Databases: FAISS, Pinecone, ChromaDB, Weaviate, Redis. These handle ultra-fast semantic searches, making sense of vast data with lightning speed. • 3. LLM Application Frameworks: Langchain, LlamaIndex, Haystack, OpenLLM, Kernel. They connect your model with tools, memory, and retrieval - turning complex AI workflows into streamlined systems. • 4. No-Code / Low-Code AI Platforms: N8N, Flowise, Langflow, RelevanceAI, Replit, Chatbase. Create powerful AI apps visually - zero coding needed. Ideal for makers and product teams. • 5. Generative AI (Text to Image): Midjourney, DALLE, KreaAI, LeonardoAI, Gemini. Generate stunning, branded imagery on demand. Perfect for storytelling and marketing. • 6. Multi-Agent & AI Agent Systems: CrewAI, AutoGen, MetaGPT, Manus.im, Camel AI, Superagent. Automate workflows and decision-making by orchestrating intelligent AI agents. Every tool plays a critical role. Missing even one could slow your path to innovation. 📌 Access leading AI models like GPT-4o, Llama, and more in one place, Sign up here : https://thealpha.dev 📌 Join Our community for latest AI updates: https://lnkd.in/gNbAeJG2 follow Vishnu N C for more insights

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#OpenSource is just like open science: shared, collaborative, and the foundation of progress. 🚀 At PyData Paris 2025, I had the privilege of moderating the panel “Open-source Business” with Yann Lechelle (CEO, :probabl., former CEO of Scaleway) and Sylvain Corlay (CEO, QuantStack) at the Cité des sciences et de l'industrie. Both leaders are deeply embedded in projects like scikit-learn and Project Jupyter—the invisible infrastructure used daily by millions from scientists to business analysts. Our conversation unpacked what it really takes to grow sustainable businesses on top of open source.: • No one-size-fits-all: Every project needs its own model; hybrids are the norm. • Community = momentum. Every user is an advocate. Marketing spend shrinks when you invest in shared value • Safeguard the team: Visibility comes with risks; support and protection are non-negotiable. • Diversify funding. From VC to contracts to public investment (like France’s support of scikit-learn), sustainability depends on many levers. • Play the long game. The most critical projects are often the least glamorous. Openness as strategy. Beyond code: open science, data, source, weights, standards, hardware. A European playbook for digital sovereignty—with France and Germany catalyzing all 27 EU countries 🇪🇺 🗽. Sustainable open-source businesses balance community values with commercial viability—and “openness” is not ideology; it’s an industrial strategy that lets society understand and control its destiny. Thanks to Yann and Sylvain for a candid, deeply practical discussion. Looking forward to Yann's book "ouvertarisme.fr" that talks about how openness (from open science to open source to open models) can become an industrial policy. In fact, it has become one. We need to seize the occasion without falling into cynicism nor "open washing”.

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→ 𝐔𝐧𝐥𝐨𝐜𝐤 𝐭𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐋𝐋𝐌 𝐓𝐨𝐨𝐥𝐬 𝐢𝐧 2025: 𝐀𝐫𝐞 𝐘𝐨𝐮 𝐑𝐞𝐚𝐝𝐲? The AI landscape is evolving faster than ever. The secret to building smarter, faster AI apps isn’t just about picking a powerful model. It’s about mastering the ecosystem surrounding it. 𝐇𝐞𝐫𝐞’𝐬 𝐲𝐨𝐮𝐫 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐜𝐡𝐞𝐚𝐭 𝐬𝐡𝐞𝐞𝐭 𝐟𝐨𝐫 2025 - 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐭𝐨𝐨𝐥𝐬 𝐜𝐚𝐧 𝐦𝐚𝐤𝐞 𝐨𝐫 𝐛𝐫𝐞𝐚𝐤 𝐲𝐨𝐮𝐫 𝐬𝐮𝐜𝐜𝐞𝐬𝐬. • 1. LLM Models: Claude, DeepSeek, Mistral, OpenAI, Gemini, LLAMA, xAI - the brilliant minds powering AI solutions today. Choose wisely; your model is the brain of your app. • 2. Vector Databases: FAISS, Pinecone, ChromaDB, Weaviate, Redis. These handle ultra-fast semantic searches, making sense of vast data with lightning speed. • 3. LLM Application Frameworks: Langchain, LlamaIndex, Haystack, OpenLLM, Kernel. They connect your model with tools, memory, and retrieval - turning complex AI workflows into streamlined systems. • 4. No-Code / Low-Code AI Platforms: N8N, Flowise, Langflow, RelevanceAI, Replit, Chatbase. Create powerful AI apps visually - zero coding needed. Ideal for makers and product teams. • 5. Generative AI (Text to Image): Midjourney, DALLE, KreaAI, LeonardoAI, Gemini. Generate stunning, branded imagery on demand. Perfect for storytelling and marketing. • 6. Multi-Agent & AI Agent Systems: CrewAI, AutoGen, MetaGPT, Manus.im, Camel AI, Superagent. Automate workflows and decision-making by orchestrating intelligent AI agents. Every tool plays a critical role. Missing even one could slow your path to innovation. 🚨 Why settle for less? Use leading AI models like GPT-4o, Llama, and more in one place, Sign up here : https://thealpha.dev 🚨 Join Our community for latest AI updates: https://lnkd.in/gNbAeJG2 follow Amit G for more insights

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Last week, I shared a post on what AI agents are and how to build one — a simple starting point. As I thought more about it, I realized that building real expertise takes more than curiosity. It takes structure, focus, and a plan. So I created one — a 90-day roadmap for anyone who wants to go from understanding the basics to building real-world, multi-agent systems. If you're looking for a practical, hands-on path to upskill in this space, I hope this helps: https://lnkd.in/eVwYWJF3 Let me know if you start following the plan — happy to connect and exchange notes. #AI #Developer #Upskill #AIAgents

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𝐓𝐡𝐢𝐬 𝐰𝐞𝐞𝐤, 𝐈 𝐛𝐫𝐨𝐤𝐞 𝐝𝐨𝐰𝐧 𝐭𝐡𝐞 7 𝐬𝐭𝐞𝐩𝐬 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬, 𝐬𝐡𝐚𝐫𝐞𝐝 𝐰𝐡𝐲 𝐬𝐨 𝐦𝐚𝐧𝐲 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐟𝐚𝐢𝐥, 𝐚𝐧𝐝 𝐦𝐚𝐝𝐞 𝐨𝐧𝐞 𝐭𝐡𝐢𝐧𝐠 𝐜𝐥𝐞𝐚𝐫: Trust, not tech, is the real unlock. But here’s the part we can’t ignore: 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐢𝐬 𝐚 𝐣𝐨𝐮𝐫𝐧𝐞𝐲, 𝐧𝐨𝐭 𝐚𝐧 𝐞𝐯𝐞𝐧𝐭. You can launch the world’s smartest AI agent, but if nobody uses it Or if they only use it because they have to You’re missing the real win. Here’s what I’ve seen move the needle: ↳ Involve end-users before you write a single line of code. ↳ Celebrate small “wins” and early adopters publicly. ↳ Make feedback loops EASY and show people you’re actually listening. ↳ Turn skeptics into champions by giving them ownership, not just training. 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐭𝐫𝐮𝐬𝐭 𝐰𝐢𝐭𝐡 𝐲𝐨𝐮𝐫 𝐭𝐞𝐚𝐦 𝐢𝐬 𝐭𝐡𝐞 𝐛𝐞𝐬𝐭 𝐢𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 𝐲𝐨𝐮’𝐥𝐥 𝐞𝐯𝐞𝐫 𝐦𝐚𝐤𝐞 𝐢𝐧 𝐀𝐈. What’s one small action that’s helped your team embrace new tech? Share your best tip below ♻️ Repost to your LinkedIn followers if AI should be more accessible and follow Timothy Goebel for expert insights on #AI, #Data, & #Innovation. #AI #DigitalTransformation #Leadership #Adoption #ChangeManagement

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