Why MVPs in AI should be built to fail fast

🚀 The AI paradigm is shifting fast. 🚀 Google just dropped a major milestone! 📘 “Introduction to Agents”, the first paper in their 5-part series a formal blueprint for developers, architects, and product leaders building real, production-grade agentic systems. Here’s the core anatomy of every AI Agent: 🧠 Model (The Brain): The reasoning engine that drives decision-making. 🖐️ Tools (The Hands): Real-world interfaces for action like RAG, APIs, web calls, etc. ⚡ Orchestration Layer (The Nervous System): Governs the “Think → Act → Observe” loop that makes agents adaptive. The paper also introduces a Taxonomy of Agentic Systems from: ➡️ Level 1: Connected Problem-Solvers ➡️ Level 3: Collaborative Multi-Agent Systems … and beyond. If you’re building the next generation of intelligent applications, understanding Core Design Choices, Agent Ops, and Enterprise Governance isn’t optional it’s your competitive edge. 👉 Read the full paper to get the frameworks, vocabulary, and mental models for building truly autonomous AI systems. #AIAgents #AgenticSystems #GenerativeAI #LLMs #AIArchitecture #AgentOps #FutureOfAI

Stop Waiting: Your 3-Step Plan to AI Value Now Most AI projects die after the pilot. Not because of tech — because the process was never built for decisions. Here’s how to fix that: Align your approach — Stop chasing “models.” Start solving one business decision that moves your P&L. Example: instead of “AI for Finance,” do “AI for anomaly detection.” Use the team you have — You don’t need an army. Upskill, cross-train, and embed one AI-fluent person inside each squad. Make judgment your edge — not hype. Work with your current stack — Skip big rebuilds. Use low-code tools, fine-tune existing models, and deploy fast wins that pay back inside 30 days. Biggest risk isn’t AI. It’s workflow inertia — trying to fit new tools into old habits. Start small. Move fast. Measure decisions, not demos. #AI #Automation #Workflow #Consulting #Ops

“AI Didn’t Organize My Life. It Organized My Mind.” I used to chase productivity. Now I chase clarity. AI tools didn’t fix my calendar. They fixed my chaos. Notion AI helped me structure my thoughts. Copilot helped me break down messy workflows. Organization isn’t about color-coded dashboards. It’s about emotional architecture. And AI? It’s the scaffolding

The era of “learning AI” is over; it’s time to engineer systems that print leverage. Just wrapped up Day 2 (Nov 9th) of the @Outskill 2-Day Gen AI Mastermind, and here’s the harsh truth: 99% of people talk about AI; the 1% who deploy it are building empires. This wasn’t another “prompt engineering” crash course. It was two days of execution-first frameworks; real workflows, real deployments, and zero fluff. Here are my top 4 technical takeaways from Day 2: Agentic Workflows are the new back-end. Phani Krishna ↗️ broke this down like a compiler in human form; connecting dots across memory, retrieval, state management, and orchestration. The takeaway? Agents aren’t assistants; they’re distributed decision systems that can replace full-time ops when engineered right. No-Code ≠ No Power. K V S Dileep demonstrated how no-code tools (Langflow, Make.com, and Outskill’s internal agent builders) can chain APIs, vector DBs, and model endpoints — letting anyone deploy an MVP-level AI product in under 48 hours. Monetization is an architectural choice. As Vaibhav Sisinty emphasized, you don’t monetize ideas — you monetize deployment speed. From containerized inference endpoints to lean GPU utilization, the goal isn’t perfection, it’s iteration velocity. Custom GPTs are infra modules, not side projects. When tuned with context caching and tool invocation, they stop being toys and start acting like microservices. The new “backend engineer” is whoever can chain a GPT with a function call and ship a live endpoint by Friday. This event wasn’t about hype; it was about leverage. Every session reinforced a single truth: If you can deploy AI systems at scale, you don’t chase opportunities they chase you. So here’s the question for the builders: What’s the one agentic workflow you could automate and monetize before next week? Drop your stack below. #MLOps #GenAI #AIInfrastructure #CustomGPTs #Automation #CareerStrategy #Outskill #AgenticAI

The Real Cost of Tool Stack Chaos (Why collecting is not building) I had a 'holy sh*t' moment last month: I built a functional MVP in under two hours for less than $25 of compute using AI agents. The tools are incredible. The speed is absurd. But that immediate ease of execution is precisely the problem. When you can build anything in an afternoon, the bottleneck shifts from technical ability to intentionality. Most founders are now trapped in Tool Stack Chaos: We chase shiny new AI tools, integrate new productivity apps, and spend half the day organizing data pipelines. We mistake the motion of collecting a tool for the momentum of executing the right task. This "shallow work" makes us feel productive, but it’s just avoidance. We are using a flood of new technology to run from the single, most difficult thing: ruthless discernment. The Founder's Edge is Disintegrating. Ideas are cheap. Knowledge is a commodity. Execution is fast becoming one too. When execution is commoditized, what becomes our competitive edge? It's Discernment. It's your ability to spot the pattern everyone else misses, understand the problem better than your users do, and possess the psychological discipline to execute only the single most important task. Stop celebrating the tools you collect. Start celebrating the clarity you achieve. If you’re ready to break the cycle of Tool Stack Chaos and shift your focus from collecting to executing, I wrote about this in depth. (Link in comments/bio). — #Discipline #AIStrategy #FounderGrowth #Productivity #OneTaskToday

For most of us (including myself), the velocity of AI-driven tools being released can create a sense of overwhelm. "Am I adapting fast enough?" "Will my new workflow become obsolete by the time I wake up tomorrow morning?" "There's so much to choose from. What tool is best for me?" But Tariq D. points out that most of these tools and thoughts are mere distractions from the real truth: The founder's edge isn't in mastering more tools. It's in mastering your focus. AI makes execution a commodity. The differentiator is now discernment. Knowing what not to chase. Knowing which problem is actually worth solving. The flood of tools isn't the enemy....it's the test. Can you stay centered in the chaos long enough to make clear, intentional decisions? That's where real leverage lives. In clarity. Not clutter.

Ever wondered how AI agents go from concept to code? Completed AI Agent Fundamentals: From Concept to Code, and here are the key takeaways: - Understanding AI Agents – Core principles, architecture, and why they’re more than just automation. - Design Thinking for Agents – Aligning user needs with intelligent capabilities. - Building Blocks – Intent recognition, context management, and decision-making frameworks. - From Theory to Practice – Turning ideas into functional code with hands-on examples. - Integration & Deployment – Connecting agents to APIs, workflows, and scaling for production. This course reinforced that AI agents aren’t just tools; they’re adaptive systems designed to solve problems intelligently. Excited to apply these learnings to build smarter solutions! Are you exploring AI agents or building intelligent workflows? Let’s connect and share ideas! Thank you, Menakshi Garg for the course recommendation. #AI #Agents #Innovation

🚀 We made a major shift in how we prioritize our roadmap: features aren’t the right unit of progress, intelligence is. 🚀 It's a powerful insight and I've synthesized hours of learnings from Harmonyze below. Traditional roadmaps focused on shipping features. But with AI-native products, we needed to rethink how we measure progress and value. The key insight? When building agentic AI products, engineering teams need an intelligence framework, not just a feature backlog. We developed a numerical scoring system that measures our AI’s intelligence across dimensions specific to our domain: how well it synthesizes information, identifies opportunities, focuses on what matters, recommends concrete steps, and handles ad-hoc questions. This approach has been transformative: ⭐ Engineers now see how their work improves intelligence—not just ships features ⭐ We can objectively assess whether a potential feature moves our intelligence needle ⭐ Product discussions center on “how do we make the system smarter?” rather than “what’s the next feature?” ⭐ Engineering and product teams share a common language about intelligence, not just functionality The best part? This framework gives engineering teams agency to propose solutions that can leapfrog several intelligence levels at once—rather than incrementally working through a feature backlog. Has your team found effective ways to roadmap AI-native products? I’d love to hear what’s working. #ProductDevelopment #AIEngineering #ProductManagement #Roadmapping

💡 𝗚𝗿𝗲𝗮𝘁 𝗶𝗱𝗲𝗮𝘀 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗯𝗶𝗴 𝗯𝘂𝗱𝗴𝗲𝘁𝘀 — 𝘁𝗵𝗲𝘆 𝗻𝗲𝗲𝗱 𝗳𝗮𝘀𝘁 𝗲𝘃𝗶𝗱𝗲𝗻𝗰𝗲. If you’re exploring an AI concept, the objective isn’t scale on day one. It’s to ship 𝗮 𝘂𝘀𝗮𝗯𝗹𝗲 𝗠𝗩𝗣/𝗣𝗢𝗖 that proves value in 𝘄𝗲𝗲𝗸𝘀, 𝗻𝗼𝘁 𝗾𝘂𝗮𝗿𝘁𝗲𝗿𝘀. 𝗪𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀 𝗶𝗻 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲: ◾️ 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝘀𝗺𝗮𝗹𝗹𝗲𝘀𝘁 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲 𝘀𝗹𝗶𝗰𝗲. One workflow, one user, a clear “done” definition. ◾️ 𝗨𝘀𝗲 𝗺𝗶𝗻𝗶𝗺𝗮𝗹-𝗯𝘂𝘁-𝗿𝗲𝗮𝗹 𝗱𝗮𝘁𝗮. Establish the least access required and version the context you use. ◾️ 𝗥𝗲𝘁𝘂𝗿𝗻 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗼𝘂𝘁𝗽𝘂𝘁𝘀. Typed responses (e.g., JSON-style) so the prototype plugs into existing tools cleanly. ◾️ 𝗞𝗲𝗲𝗽 𝗵𝘂𝗺𝗮𝗻 𝗼𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁 𝗲𝘅𝗽𝗹𝗶𝗰𝗶𝘁. Lightweight approvals for sensitive steps; avoid over-engineering. ◾️ 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗯𝗲𝗳𝗼𝗿𝗲/𝗮𝗳𝘁𝗲𝗿. Track task success, end-to-end latency, and cost per successful task to inform go/no-go. 🚀At 𝗔𝗴𝗲𝗻𝘁𝗶𝘃𝗲, we deploy an 𝗔𝗜 𝗪𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲 to move from pitch to prototype quickly — so leaders can see outcomes before committing to full-scale builds. 💬 Interested in a 𝟮–𝟰 𝘄𝗲𝗲𝗸 build plan tailored to your team? 𝗦𝗲𝗻𝗱 𝘂𝘀 𝗮 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 𝗵𝗲𝗿𝗲 𝗼𝗻 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 — 𝗹𝗲𝘁’𝘀 𝘀𝗰𝗼𝗽𝗲 𝗶𝘁. #AIWorkforce #MVP #POC #EnterpriseAI #ProductDelivery #Agentive