From Automation to AI: How I Transformed My Workflow

I've been reflecting on where real value comes from in modern tech products, and I keep coming back to this: AI is a multiplier, not the value generator. Let me be blunt - nobody invests in a chatbot for the sake of a chatbot. The value lies in the functions it accesses and what it can actually do (Agents = smaller, task-specific LLMs that actually accesses APIs, guided by a smart reasoning layer). Here's the math I see: - Engineering = Summation. Value scales linearly with every well-built API, every secure endpoint, every compliant integration you add to your system. - AI = Multiplication factor. Layer it on top, and suddenly you're seeing 3x-4x returns on that foundation. And if we think about modern tech systems this way, we naturally see that multiplying zero still gives you zero. Without robust, secure, compliant APIs and solid infrastructure underneath, AI brings nothing to the table. But what happens the moment you add AI on top of a couple of well-functioning, compliant APIs? You automate workflows, eliminate friction, and deliver what feels like massive value to users. And indeed it is massive value - but it's Engineering × AI, not AI alone. Users see the magic of automation and naturally attribute it to AI. But strip away the underlying engineering excellence - the ground-tested APIs, the security layers, the infrastructure that just works - and that magic disappears. The cake matters more than the cherry. Don't get me wrong: you need the cherry on top. AI is non-negotiable in today's landscape. But you also need the rest of the cake - the OG, critical engineering work that makes everything possible. Next time we look at the value chain, let's be honest about where value actually originates. More often than not, it's not AI getting things done - it's the engineering foundation that enables AI to do its job. #ai #engineering #valuechain #businessvalue

Creating an AI agent begins long before writing a single line of code. The first and most crucial step is understanding the problem thoroughly—what challenge are we trying to solve, and what value will automation bring? Next, we need to identify the tasks that can be automated. Not everything needs AI—some tasks are better left manual, while others can benefit from efficiency, accuracy, or scale. Once we know what to automate, it’s essential to create a blueprint of the overall workflow. Mapping out the end-to-end process helps visualize dependencies, data flow, and key decision points, ensuring that the AI agent integrates seamlessly and adds maximum value. This structured approach ensures that the AI agent we build is impactful, scalable, and aligned with real business needs. In short: 1️⃣ Understand the problem. 2️⃣ Identify automation opportunities. 3️⃣ Create a workflow blueprint. 4️⃣ Build with purpose. A well-thought foundation is the key to building smarter AI agents.

Here’s an insight about AI I wish I’d known sooner: Don’t automate chaos. I had a time-consuming task and outlined the problem to Claude to help think about how I’d build an agent. In most of my Claude projects, I specify a “board of advisors” in the special instructions to help generate insights, and in this particular project one of those advisors is Ben Horowitz who highlighted a critical flaw in my plan. My project tracking (what I’m trying to automate) is a mess. Automating that mess is just going to scale my mess 10x. Let that sink in. The reason why so many AI projects fail is because they expose a truth we don’t want to admit—our processes are flawed. Classic GIGO — garbage in, garbage out. Before you jump into building that agent or AI automation, take some time to examine your process. Clean up the mess. THEN automate the maintenance.

🚀 How to Create AI Agents Using Claude AI Agents are transforming how businesses automate reasoning, decision-making, and task execution and Claude is quickly becoming one of the most powerful tools to build them. Here’s a simple breakdown of how to get started 👇 1. Define the Agent’s Purpose Decide what your agent should do customer support, data analysis, workflow automation, or content generation. Claude thrives when it has clear, goal-driven instructions. 2. Choose the Right Framework Claude can integrate seamlessly with frameworks like LangChain or LlamaIndex. These tools let you connect Claude to APIs, databases, and other systems so your agent can act, not just chat. 3. Use Prompt Chaining Instead of a single prompt, design step-by-step reasoning flows. Example: → Step 1: Understand the query → Step 2: Search relevant data → Step 3: Generate a structured answer This gives Claude the ability to reason and act with precision. 4. Add Memory & Context Store important data from past interactions in a database or vector store (like Pinecone or Weaviate). Claude’s responses improve when it recalls prior actions or decisions essential for autonomous behavior. 5. Connect Tools & APIs Use Claude with function-calling capabilities or integrate through Anthropic’s API to give it “hands” the ability to execute code, read files, send emails, or access external systems. 6. Test, Iterate, and Monitor Every great agent evolves through testing. Measure outputs, refine prompts, and monitor decision accuracy to ensure your Claude-powered agent performs reliably. The Result? An intelligent, task-performing AI system that doesn’t just respond, it acts. Have you tried building with Claude yet? What use case do you see AI agents transforming first? Let us know in the comment section below 👇 #ClaudeAI #AIagents #Anthropic #AgenticAI #LangChain #Automation #AIintegration #Innovation #Lab7ai

The Silent Crisis in AI Development AI has accelerated everything — code generation, deployment velocity, experiment cycles. But there’s one thing it hasn’t fixed: testing. Every week, development gets faster. Every release, test coverage falls further behind. And while AI writes new code at lightning speed, it’s still humans trying to prove that it works. This is the Testing Problem — the widening gap between how fast we can build and how slowly we can verify. In my latest video, I break down: * Why this velocity gap is now the biggest risk in modern software delivery * How traditional automation can’t close it * And what needs to change for testing to truly keep pace with AI-powered development 🎥 Watch the full video in the comment section below. #AITesting #QualityEngineering #SoftwareTesting #AgenticAI

AI is finally proving its business value Akkodis just reported real results from AI-driven innovation across industries, including financial services. Efficiency gains are real, but the real story is execution.   Many banks and insurers still experiment with pilots that never scale. The winners will be those who treat AI as an operating model shift, not as a tech add-on. Data governance, change readiness and client-centric design decide success.   Now is the time to move from “try” to “embed.” Do you already measure the business impact of AI in your operations?

You don’t need AI automations yet. What you need is a clear process worth automating. Everyone’s obsessed with “AI workflows.” Zapier this. Notion that. Auto-everything. But here’s what most people miss if your system is still messy, automation will only multiply that mess. Before you automate, audit. I use a simple 3-step test: Repeatability: Have I done this task manually at least 10 times? → If not, it’s too early to automate. Clarity: Can I explain what “done well” looks like in one sentence? → If not, I don’t fully understand the workflow yet. ROI: Does this task steal more than 2 hours a week? → If not, the time saved won’t justify the setup or maintenance. Once a system passes that test, then I automate: Auto-send client welcome emails after onboarding. Generate first-draft post ideas via my content database. Auto-organize project files in Notion based on type. The secret? Automation isn’t the shortcut to productivity. It’s the reward for discipline. Build clarity first. Then let AI amplify what already works not cover what’s broken. Want my N8N (Zapier alternative) workflow setup? Connect with me Comment “SETUP” below I’ll DM you the video walkthrough. #automations #organize #broken #n8n

Remember that knot in your stomach? The one that tightens when you hear "AI will replace everything"? For a while, even in the low-code/no-code world, there were whispers. Would all the automations I build with n8n and Make become obsolete? Would my AI agents just take over entirely, leaving no room for human ingenuity? I've spent years diving deep, sometimes failing, sometimes celebrating breakthroughs, building everything from intelligent CRMs to hyper-efficient chatbots. My journey with tools like n8n, Make, and crafting AI agents has shown me something profound: AI isn't a threat; it's the ultimate co-pilot. A recent report just confirmed what I've been experiencing firsthand. AI isn't here to end low-code/no-code; it's here to supercharge it. It's democratizing development, letting non-developers create alongside seasoned pros. It's optimizing, debugging, and automating the very process of building. Think about it: smarter automations, more adaptable applications, and a massive reduction in barriers for anyone to experiment with AI and Machine Learning. This isn't just theory. We're talking about hyperautomation becoming a reality. Projections say that by 2025, 70% of new enterprise applications will use low-code or no-code technologies. AI is the engine driving that acceleration, making solutions I build with Flowise, Typebot, and various AI APIs more intelligent and responsive than ever. So, what's your take? Are you seeing AI as a catalyst or still feeling that knot of fear? Share your thoughts below, or if you're ready to explore how AI can amplify your low-code/no-code projects, send me a DM. Let's build something smarter together.

I recently came across some sharp insights about AI agents and RAG, and they hit hard because they reflect exactly what I’ve been seeing in real-world. Everyone’s talking about these systems right now. 𝗕𝘂𝘁 𝘃𝗲𝗿𝘆 𝗳𝗲𝘄 𝗮𝗿𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝘁𝗵𝗲𝗺 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻. Most of what we see online are demos and decks, not systems that deal with real users, latency, and cost. 𝗨𝗽𝗼𝗻 𝗿𝗲𝗮𝗱𝗶𝗻𝗴 𝗮𝗻𝗱 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗶𝗻𝗴 𝗵𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁’𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗰𝗹𝗲𝗮𝗿👇 1️⃣ 𝗦𝗼𝗹𝗶𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗯𝗲𝗮𝘁𝘀 𝗰𝗹𝗲𝘃𝗲𝗿 𝗽𝗿𝗼𝗺𝗽𝘁𝘀. You can’t fake reliability. If your infra, APIs, and async pipelines aren’t built to scale, your “AI agent” won’t survive contact with production. 2️⃣ 𝗔𝗴𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗰𝗵𝗮𝘁𝗯𝗼𝘁𝘀. They’re process managers, planning, recovering, and adapting to failure. If your agent can’t handle tool errors or cost constraints, it’s not ready for prime time. 3️⃣ 𝗥𝗔𝗚 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀. It’s about how well you retrieve the right context from messy, unstructured data. Bad retrieval results in confident but incorrect outputs. 4️⃣ 𝗦𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗲𝘃𝗲𝗿. This work is no longer about crafting clever prompts. It’s about how you connect models, tools, and memory, and how you monitor, debug, and evolve them. 5️⃣ 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝘁𝗿𝘂𝘁𝗵 𝗰𝗼𝗺𝗲𝘀 𝗼𝘂𝘁. Demos are fun. Production is brutal. Latency budgets, compliance, legacy systems, that’s where things break. 𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆 The companies pulling ahead aren’t the ones talking the most about AI, they’re the ones actually shipping it. 𝘉𝘦𝘤𝘢𝘶𝘴𝘦 𝘣𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘢𝘯𝘥 𝘳𝘶𝘯𝘯𝘪𝘯𝘨 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯-𝘨𝘳𝘢𝘥𝘦 𝘈𝘐 𝘪𝘴𝘯’𝘵 𝘢𝘣𝘰𝘶𝘵 𝘵𝘩𝘦𝘰𝘳𝘺 𝘢𝘯𝘺𝘮𝘰𝘳𝘦. 𝘐𝘵’𝘴 𝘢𝘣𝘰𝘶𝘵 𝘦𝘹𝘦𝘤𝘶𝘵𝘪𝘰𝘯.

I recently came across some sharp insights about AI agents and RAG, and they hit hard because they reflect exactly what I’ve been seeing in real-world. Everyone’s talking about these systems right now. 𝗕𝘂𝘁 𝘃𝗲𝗿𝘆 𝗳𝗲𝘄 𝗮𝗿𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝘁𝗵𝗲𝗺 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻. Most of what we see online are demos and decks, not systems that deal with real users, latency, and cost. 𝗨𝗽𝗼𝗻 𝗿𝗲𝗮𝗱𝗶𝗻𝗴 𝗮𝗻𝗱 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗶𝗻𝗴 𝗵𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁’𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗰𝗹𝗲𝗮𝗿👇 1️⃣ 𝗦𝗼𝗹𝗶𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗯𝗲𝗮𝘁𝘀 𝗰𝗹𝗲𝘃𝗲𝗿 𝗽𝗿𝗼𝗺𝗽𝘁𝘀. You can’t fake reliability. If your infra, APIs, and async pipelines aren’t built to scale, your “AI agent” won’t survive contact with production. 2️⃣ 𝗔𝗴𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗰𝗵𝗮𝘁𝗯𝗼𝘁𝘀. They’re process managers, planning, recovering, and adapting to failure. If your agent can’t handle tool errors or cost constraints, it’s not ready for prime time. 3️⃣ 𝗥𝗔𝗚 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀. It’s about how well you retrieve the right context from messy, unstructured data. Bad retrieval results in confident but incorrect outputs. 4️⃣ 𝗦𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗲𝘃𝗲𝗿. This work is no longer about crafting clever prompts. It’s about how you connect models, tools, and memory, and how you monitor, debug, and evolve them. 5️⃣ 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝘁𝗿𝘂𝘁𝗵 𝗰𝗼𝗺𝗲𝘀 𝗼𝘂𝘁. Demos are fun. Production is brutal. Latency budgets, compliance, legacy systems, that’s where things break. 𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆 The companies pulling ahead aren’t the ones talking the most about AI, they’re the ones actually shipping it. 𝘉𝘦𝘤𝘢𝘶𝘴𝘦 𝘣𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘢𝘯𝘥 𝘳𝘶𝘯𝘯𝘪𝘯𝘨 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯-𝘨𝘳𝘢𝘥𝘦 𝘈𝘐 𝘪𝘴𝘯’𝘵 𝘢𝘣𝘰𝘶𝘵 𝘵𝘩𝘦𝘰𝘳𝘺 𝘢𝘯𝘺𝘮𝘰𝘳𝘦. 𝘐𝘵’𝘴 𝘢𝘣𝘰𝘶𝘵 𝘦𝘹𝘦𝘤𝘶𝘵𝘪𝘰𝘯.