While the understanding of the Model Context Protocol (MCP) for agentic integration is still evolving, most of its current applications are centered around providing ‘runtime tools’ to AI agents along with relevant context. In one of my recent projects, we were using an SDK for DDS which was extremely helpful at providing a comprehensive collection of tools, libraries, documentation, code samples, etc. I was wondering if it was worth the effort to turn the SDK from a static library into an intelligent, interactive, and adaptive tool - empowering developers and testers to build and validate apps/utilities faster, safer, and with higher quality. One way of doing so would be to add a Model Context Layer to the SDK, which enables a structured, machine-readable representation of the SDK’s concepts, templates, and domain-specific knowledge that helps the LLM agent to generate valid code for a very consistent and accurate usage of the SDK. This provides high precision and consistency, with added benefits like cutting down onboarding time and learning curve, and bridging knowledge silos that usually form across different user groups like service development teams, V&V teams, etc. This should also facilitate easier SDK upgrade cycles, and adoption across user groups. What are your thoughts on this? #MCP #Agentic-AI #SDK-intelligence #high-precision-code-generation Arkid Mitra Sunil Kulkarni Mukund Dharwadkar Abhishaik Srivastava Vidya Singh Mohan Kulkarni Navya Mishra Arvind Gupta Devansu Kasyap
How to make SDKs more intelligent and interactive
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𝐀𝐠𝐞𝐧𝐭𝐊𝐢𝐭 𝐯𝐬 𝐧8𝐧: 𝐓𝐰𝐨 𝐏𝐚𝐭𝐡𝐬 𝐭𝐨 𝐒𝐦𝐚𝐫𝐭𝐞𝐫 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 🚀 With 𝐭𝐡𝐞 𝐥𝐚𝐮𝐧𝐜𝐡 𝐨𝐟 𝐀𝐠𝐞𝐧𝐭𝐊𝐢𝐭 by OpenAI and the maturity of n8n, developers and automation architects now have two powerful options for orchestrating 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬. 🔹 𝐀𝐠𝐞𝐧𝐭𝐊𝐢𝐭 focuses on AI-native workflows, offering built-in evaluation, versioning, and visual tools to speed up agent creation. 🔹 𝐧8𝐧 remains the go-to choice for general automation, giving more flexibility, self-hosting options, and a massive integration ecosystem. In short: ▪️ Choose AgentKit for AI-centric agents with 𝐬𝐞𝐚𝐦𝐥𝐞𝐬𝐬 𝐎𝐩𝐞𝐧𝐀𝐈 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧. ▪️ Choose n8n for 𝐛𝐫𝐨𝐚𝐝𝐞𝐫 𝐚𝐧𝐝 𝐜𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐛𝐥𝐞 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧𝐬 across multiple tools and data sources. As the AI ecosystem evolves, there may be room for 𝐬𝐲𝐧𝐞𝐫𝐠𝐢𝐞𝐬 — e.g. using AgentKit inside n8n workflows, or exporting n8n logic to agent systems. What’s clear: We’re entering a 𝐧𝐞𝐰 𝐩𝐡𝐚𝐬𝐞 where automating workflows converge in powerful ways. 𝐖𝐡𝐚𝐭 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤? Are you leaning toward one or the other for your next project? 🙌 #AI #Automation #AgentKit #n8n #OpenAI #Developers #WorkflowAutomation
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Introducing AgentKit, a comprehensive set of tools for building, deploying, and optimizing agents 🚀. With AgentKit, developers can now design workflows visually and embed agentic UIs faster using new building blocks like Agent Builder and ChatKit 🛠️. Agent Builder provides a visual canvas for composing logic with drag-and-drop nodes, connecting tools, and configuring custom guardrails 📈. It supports preview runs, inline eval configuration, and full versioning—ideal for fast iteration. Builders can get started with a blank canvas or with prebuilt templates 📁. OpenAI is also launching a Connector Registry for enterprises to govern and maintain data across multiple workspaces and organizations 📊. The Connector Registry consolidates data sources into a single admin panel across ChatGPT and the API. Developers can also enable Guardrails, an open-source, modular safety layer that helps protect agents against unintended or malicious behavior 🛡️. Deploying chat UIs for agents can be surprisingly complex, but ChatKit makes it simple to embed chat-based agents that feel native to your product 📱. Building reliable, production-ready agents requires rigorous performance evaluations, and we’re now adding four new capabilities to Evals to make it even easier to build evals 📊. Here are some key features of AgentKit: 1️⃣ Agent Builder: a visual canvas for composing logic 2️⃣ ChatKit: a simple way to embed chat-based agents 3️⃣ Connector Registry: a single admin panel for governing data 4️⃣ Guardrails: an open-source safety layer for protecting agents 5️⃣ Evals: a tool for testing prompts and measuring model behavior With AgentKit, developers can build more efficient and reliable agents, and we’re excited to see the innovative applications that will be built with these tools 🤖. Try out AgentKit today and start building the future of artificial intelligence! #ArtificialIntelligence #AgentKit #OpenAI #MachineLearning #Innovation #Technology #Development Reference: [https://lnkd.in/gcMeU5vV] 🌐 stalin.dev #StalinThangaraj
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𝐎𝐩𝐞𝐧𝐀𝐈 𝐣𝐮𝐬𝐭 𝐫𝐚𝐢𝐬𝐞𝐝 𝐭𝐡𝐞 𝐛𝐚𝐫 𝐟𝐨𝐫 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧. With the launch of 𝐀𝐠𝐞𝐧𝐭𝐊𝐢𝐭, OpenAI isn’t adding another developer tool — it’s redefining what 𝒂𝒖𝒕𝒐𝒎𝒂𝒕𝒊𝒐𝒏 actually means. For years, platforms like 𝐧𝟖𝐧, 𝐙𝐚𝐩𝐢𝐞𝐫, 𝐀𝐢𝐫𝐭𝐚𝐛𝐥𝐞, 𝐚𝐧𝐝 𝐑𝐞𝐥𝐚𝐲 have connected APIs and executed workflows. Now, OpenAI is building 𝒂𝒈𝒆𝒏𝒕𝒔 𝒕𝒉𝒂𝒕 𝒄𝒂𝒏 𝒕𝒉𝒊𝒏𝒌, 𝒓𝒆𝒂𝒔𝒐𝒏, 𝒂𝒏𝒅 𝒐𝒓𝒄𝒉𝒆𝒔𝒕𝒓𝒂𝒕𝒆 — 𝒏𝒐𝒕 𝒋𝒖𝒔𝒕 𝒂𝒖𝒕𝒐𝒎𝒂𝒕𝒆. 𝐀𝐠𝐞𝐧𝐭𝐊𝐢𝐭 𝐢𝐬 𝐚 𝐟𝐮𝐥𝐥-𝐬𝐭𝐚𝐜𝐤 𝐬𝐲𝐬𝐭𝐞𝐦 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝, 𝐝𝐞𝐩𝐥𝐨𝐲, 𝐚𝐧𝐝 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝒗𝒊𝒔𝒖𝒂𝒍𝒍𝒚, 𝒔𝒆𝒄𝒖𝒓𝒆𝒍𝒚, 𝒂𝒏𝒅 𝒏𝒂𝒕𝒊𝒗𝒆𝒍𝒚. 𝐊𝐞𝐲 𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 𝐨𝐟 𝐀𝐠𝐞𝐧𝐭𝐊𝐢𝐭: • 𝐀𝐠𝐞𝐧𝐭 𝐁𝐮𝐢𝐥𝐝𝐞𝐫 → drag-and-drop reasoning workflows • 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐨𝐫 𝐑𝐞𝐠𝐢𝐬𝐭𝐫𝐲 → unified access to data sources (Google Drive, Teams, etc.) • 𝐄𝐯𝐚𝐥𝐬 → continuous learning and feedback • 𝐂𝐡𝐚𝐭𝐊𝐢𝐭 → embeddable chat UI for any app In short — OpenAI has bundled what developers once had to assemble manually — LLMs, orchestration, UI, and evaluation — into 𝒐𝒏𝒆 𝒊𝒏𝒕𝒆𝒈𝒓𝒂𝒕𝒆𝒅 𝒄𝒐𝒏𝒕𝒓𝒐𝒍 𝒑𝒍𝒂𝒏𝒆. 𝐀𝐠𝐞𝐧𝐭𝐊𝐢𝐭 𝐛𝐥𝐮𝐫𝐬 𝐭𝐡𝐞 𝐥𝐢𝐧𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐚𝐧𝐝 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞. Automation tools 𝒆𝒙𝒆𝒄𝒖𝒕𝒆. Agents 𝒅𝒆𝒄𝒊𝒅𝒆. The next evolution of automation isn’t 𝒏𝒐-𝒄𝒐𝒅𝒆. It’s 𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠-𝐟𝐢𝐫𝐬𝐭 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 — workflows that 𝒕𝒉𝒊𝒏𝒌, 𝒂𝒅𝒂𝒑𝒕, 𝒂𝒏𝒅 𝒍𝒆𝒂𝒓𝒏. #OpenAI #AgentKit #AIagents #n8n #AIAutomation #WorkflowAutomation #AIPlatforms #ProductManagement #AIRevolution #BuildWithAI
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The first time I saw Model Context Protocols (MCPs), I screamed: “This is the end of automation as we used to know it.” Most automation used to be deterministic and routine. MCPs flipped that, AI agents are more ubiquitous, context-aware, and powerful. At IntentForms, this is a big part of our roadmap. We’re exploring what automation really means for people who collect data with forms—where context, validation, and follow-up actions matter. We’re bringing MCPs closer to your data: 1. Agents that understand your form context in real time 2. Automated workflows triggered by intent (actions), not just rules 3. Secure, auditable actions across the tools you already use The goal is empowering our users to go from “response received” to “task completed” without manual busywork, while keeping them in the loop where necessary. If you’re building with forms, surveys, or high-volume data collection and want to test MCP-powered automation, let’s talk. #MCPs #MCP #ArtificialIntelligence #IntentForms #Automation
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The Future Won’t Be Coded — It’ll Be Connected. In today’s fast-evolving tech world, the most powerful developers aren’t the ones who just write the best code — They’re the ones who know how to connect systems, APIs, and intelligence across platforms. From .NET to AI integrations, from microservices to LLM-powered assistants — the real power now lies in how efficiently you make systems talk to each other. 💭 Imagine: • An AI model that analyzes user behavior from your database, • Triggers a real-time notification through your backend API, • And logs the result into a cloud-based report — all in milliseconds. That’s not “just development.” That’s smart orchestration. ⸻ 🔥 The future of development isn’t just code — it’s connectivity, context, and creativity. If you’re a developer, start thinking like an integrator — because those who connect the dots will define tomorrow. #AI #Developers #Innovation #TechTrends #DotNet #APIs #LLM #Automation #FutureOfWork
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⚡ 𝐎𝐩𝐞𝐧𝐀𝐈 𝐉𝐮𝐬𝐭 𝐊𝐢𝐥𝐥𝐞𝐝 𝐧𝟖𝐧 𝐚𝐧𝐝 𝐙𝐚𝐩𝐢𝐞𝐫? 𝐌𝐞𝐞𝐭 𝐭𝐡𝐞 𝐍𝐞𝐰 𝐒𝐡𝐞𝐫𝐢𝐟𝐟 𝐢𝐧 𝐓𝐨𝐰𝐧: 𝐀𝐠𝐞𝐧𝐭 𝐁𝐮𝐢𝐥𝐝𝐞𝐫 🤖🔧 Forget stitching together fragile automations. Forget Franken-prompts duct-taped to APIs. OpenAI’s Agent Builder is here — and it might just be the Zapier killer nobody saw coming. 🧠 What Is It? A visual drag-and-drop interface to build full AI agents — 📌 Define typed inputs and outputs 📌 Start from templates or build from scratch 📌 Preview flows with live data 📌 Embed anywhere using ChatKit 📌 Or… download the SDK and host it yourself 💥 Why This Changes the Game: ✅ No-code meets agentic AI ✅ No more brittle glue code ✅ Real workflows with memory, logic, and tools ✅ Native deployment without third-party glue platforms 🔥 Devs, Builders, Automators — This Is Big. You’re no longer just triggering APIs. You’re building thinking agents that run end-to-end logic, reason with context, and act with autonomy. 🔁 Reshare if you’ve ever hacked together tools in Zapier, n8n, or Make. 💬 Comment below: What’s the first workflow you’ll build in Agent Builder? #OpenAI #AgentBuilder #AIAgents #NoCode #Automation #AgenticAI #LLMInfra #AIWorkflow #ZapierAlternative #n8n #ChatGPT #PromptEngineering #AIUX #AIIntegration #TechInnovation #ProductivityStack #DeveloperTools #FutureOfWork #SDK #VisualDev #LLMTools #AgentOrchestration #AIProduct #AutomationTools #OpenAIAgent #AgentOps
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The Automation Decision: n8n or OpenAI’s AgentKit? Which system will drive your future — a robust workflow engine or an AI-native agent platform? OpenAI’s upcoming Agent Builder / AgentKit is being positioned as a direct competitor to n8n and Zapier in the automation space. It introduces reasoning nodes, guardrails, modular connectors, and AI-centric flows — features that n8n has long supported through integrations. Before making a switch, it’s essential to understand how they compare and why many may see them as complementary rather than competitive. n8n: The Flexible Automation Powerhouse - Mature ecosystem: n8n supports OpenAI integrations, allowing for hybrid workflows that combine logic nodes and AI nodes. - Control and flexibility: Build complex logic, loops, conditionals, branching flows, and custom nodes. - Self-host/open model: Many users prefer n8n’s flexibility and control over hosting and costs. - Community and extensions: A wide base of connectors, custom nodes, and an active community. AgentKit / Agent Builder (OpenAI): The AI-First Approach - Reasoning-first architecture: Expected to focus on agents that can think, plan, chain tasks, and act autonomously. - Guardrails & safety baked in: Anticipated built-in safety, validation, and agent performance tools. - Simplicity for new workflows: A visual drag-and-drop agent builder may reduce friction for non-technical users. - Potential overlap & integration: AgentKit is likely to coexist with platforms like n8n rather than fully replace them. Which is “Better”? — It Depends - Complex branching & logic: n8n excels, while AgentKit is expected to support logic but focus on reasoning. - AI-native workflows: n8n offers this via integrations, while AgentKit is expected to have built-in capabilities. - Safety & guardrails: n8n requires manual setup, whereas AgentKit is likely to have these features built-in. #n8n #automation #openAI #need #workFlow #openAIagentkit #AI #codesphereInnovation #Assosiatix -
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This is a very timely idea, especially in the context of medical device software, where development and V&V need to align closely with safety, consistency, and traceability expectations. We’ve started seeing value in embedding structured domain context into tools that interface with LLM agents. Adding a Model Context Layer to an SDK could help agents generate code that’s not just syntactically correct, but semantically aligned with device safety classes, interface contracts, and validation expectations. This can significantly reduce onboarding time, ensure consistent usage across teams, and lower friction during SDK upgrades or regulatory reviews. From a systems standpoint, this also helps close the loop between development, test, and risk management — giving agents enough context to reason about what needs to be tested, what could break downstream, and how to stay compliant. It’s definitely a direction worth investing in as agentic patterns mature.