OpenAI's AgentKit: A No-Code AI Workflow Tool

The Future of Automation is Agentic! Traditional automation was built on rules. OpenAI’s new AgentKit is built on reasoning. In my latest Medium blog, I explored how AgentKit transforms automation — moving from rigid “if-this-then-that” workflows to adaptive, context-aware AI systems that learn, reason, and act. Here’s what I cover ✅ How AgentKit redefines automation logic ✅ Why adaptive reasoning matters ✅ Real-world use cases for AI-driven workflows ✅ What this shift means for engineers and creators If you’re curious about how AI agents will reshape productivity and development, this one’s for you. 👉 Read the full article here: https://lnkd.in/g4xYsTFn #learnwithsampath #ArtificialIntelligence #OpenAI #Automation #AIagents #FutureOfWork #GenerativeAI

Reposting my comment(thoughts) on AI hype - how real? I think, the journey started in isolation using AI mainly for getting quick required code/answers, almost like a faster alternative to reading through Stack Overflow. Then came more meaningful chatbots that used knowledge bases through basic RAG implementations. Things started to get better when tools were integrated and standardized using protocols like MCP or tool APIs. This was the point where we could finally use LLMs with real project context and connect them to our production runtime environments. Now, I am observing while coding agents the real value will come from coding multiple domain-specific agents(with personas) with their defined roles and responsibilities. Where LLMs, domain understanding (with good balanced memory), and tools work together seamlessly within defined boundaries to achieve goals just like we humans do within in our organizations. #aiagents #taskautomation

🚀 Day 1 of Launch Week: AI Impact is here Every company is investing in AI tools for devs. But very few know what’s actually happening after that. How are engineers really using them? How are they affecting delivery speed, quality, and collaboration? That’s what we wanted to understand. And measure. So today we’re launching Pullpo’s AI Impact Module, which combines AI usage data with engineering signals to reveal how AI tools influence delivery speed and quality. To start, we’re giving FREE access to a quick form that generates a personalized AI Impact Report for your team. Link in the comments! 🎥 Here’s a short video showing how it works ↓

Recent industry data shows marketers are being asked to do more with less… Budgets are down, and efficiency is now the KPI that matters! The solution: repeatable, instrumented AI workflows. (Not another person who has to learn the stack) OpenAI just shipped AgentKit, a toolkit for building, embedding, and measuring agent workflows (visual builder, embeddable chat, and stronger eval tools). This lowers the friction to go from idea → production.

The Future Isn’t About Learning How to Build AI Agents — It’s About Knowing What to Automate For the last couple of years, the spotlight has been on how to build AI agents — the coding, the frameworks, the prompts, the tools. But the real opportunity ahead isn’t technical. It’s strategic. We’ve entered a new era where building and deploying AI agents is becoming easier by the day. Platforms like OpenAI’s AgentKit, Anthropic’s Console, and others are abstracting away the complexity. You no longer need a team of engineers to launch an intelligent workflow. You just need clarity on what problem actually matters. The future winners in business won’t be those who can “build AI,” but those who can see where AI fits. The real skill now is understanding: Where are the repetitive processes? Which decisions are data-driven and pattern-based? Where do human bottlenecks slow down growth? Once you know what to automate, the how is a solved problem. This shift means business leaders, not just technologists, need to think in terms of AI leverage — how to turn knowledge of their operations into scalable, intelligent systems. The next generation of AI-driven businesses won’t be built by those who understand machine learning — they’ll be built by those who understand their business deeply enough to know where intelligence adds value.

OpenAI just launched Agent Kit, their new workflow builder. It was coded in six weeks, with AI writing 80% of the code. The product itself is a series of drag-and-drop nodes connecting systems together. Zapier and n8n have been doing this for years. But OpenAI gets to introduce this to millions of people who would never have discovered those tools. The channel advantage of being "the AI company" is massive. It's smart business. I can't get around the feeling that this feels technically backwards though. We've been moving toward AI that reasons and adapts on its own - thinking through problems like a brain. Agent Builder brings us back to pre-defined workflows where you map out every step. I understand the appeal. Predictable workflows are easier to trust and audit. Enterprises want to know exactly what their AI will do. It makes sense. But what's getting buried in the announcement is that OpenAI built this entire platform in six weeks, with AI writing 80% of the code. That tells you more about where we are than the product itself. We're at the stage where AI can build the tools that teach people how to use AI. The tools look like a step backwards (and I might be proven wrong here in the longer run). But the way we're building them is accelerating faster than most people realise. #AI #FutureOfWork

You don’t need to be a developer to build AI agents. You don’t need tech skills. You just need 5 minutes and this post. In this quick step-by-step carousel, I’ll show you exactly how to create your first AI Agent using OpenAI’s new Agent Builder. No coding, no setup headaches, no confusion. By the end, you’ll: > Get an idea how AI agents work and what they can do for you. > Have your own AI agent up and running. > Be ready to explore how AI can automate tasks and boost productivity in your business. Follow the slides, build your first AI agent, and see how simple AI creation has become. P.S. If you want to discuss how to lead AI transformation without falling into the trap of AI-technology-first thinking, let’s connect.

Hey, check this out—Agent Builder is like the easiest way to create AI workflows without writing a single line of code. Just drag, drop, and boom, your smart AI assistant is ready to go! Seriously, it’s perfect for automating all those boring tasks.

Hi everyone, thank you for all the comments and interest in Applied AI for MOps! I'm back with a new post (https://lnkd.in/gGRUYMd3) about a hard lesson that taught me a lot about AI implementation and why many AI pilots fail to reach production. After building my account research tool (hundreds of reports generated and counting!), I wanted to make my next AI workflow more sophisticated. I went all-in on a multi-agent system, convinced that more specialized agents would produce more specific, insightful results. But surprisingly, the output actually got worse. I was trying to solve the generic, "Mad Libs" feel that AI can sometimes produce, but my complex 5-agent system just created more generic template outputs. I was pretty confused. In my new post, I'm sharing the full story of what went wrong. It's a deep dive into the "Telephone Game" effect I accidentally created - how "context dilution" stripped all the specific evidence (the specific quotes, the financial numbers) from my prompts until the final output was completely generic. The lesson connects to why so many AI pilots struggle: we're not thinking through what our specific use case actually requires. We can over index in either direction - overcomplicating when simple could work, or oversimplifying when complexity is genuinely needed. I'm sharing the failed experiment and the simpler solution that worked better - the difference between Extraction Tasks (which are great for agents) and Synthesis Tasks (which need full context). I'm curious if others have run into this context dilution problem! Would love for you to read and share your thoughts: https://lnkd.in/gGRUYMd3 #MarketingOps #MOPs #AppliedAI #GenAI #Automation #AIWorkflows #LLMs

🚀 When AI Gets “Creative” with Test Cases… Lately, I’ve been working on an interesting problem — using LLMs to automatically generate test case suites for our project. It’s fascinating how well AI can understand APIs, infer edge cases, and even suggest validation steps… until it doesn’t 😅 Every now and then, the model confidently produces beautifully structured but completely made-up test cases — scenarios that don’t exist anywhere in our specs. Classic case of AI hallucination. It reminded me that large language models are, at their core, great pattern matchers but poor fact-checkers unless guided properly. Here are a few things I’ve found that significantly improve accuracy and reliability: 🧠 Use newer, better-trained models — they hallucinate less and handle domain logic better. ✍️ Be explicit in prompts — tell the model what not to assume and when to say “I don’t know.” 🔁 Make it reason step-by-step — breaking down logic keeps the chain consistent. 📚 Ground responses with real data — feed API specs, docs, or test templates (RAG helps a lot). ✅ Run self-checks and human reviews — catching hallucinations early improves long-term accuracy. The goal isn’t to stop AI from being creative — it’s to channel that creativity within boundaries so it adds value where it matters: reliable, production-ready automation. We’re not there yet, but with layered checks and the right prompting logic, the results are getting remarkably close. #AI #GenerativeAI #MachineLearning #TestAutomation #SoftwareTesting #LLMs #SRE #DevOps #AITesting #AIHallucination