Andreas Horn's Linkedin Analytics

McKinsey & Company 𝗷𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝘁𝗵𝗲𝗶𝗿 𝗹𝗮𝘁𝗲𝘀𝘁 𝗣𝗢𝗩 𝗼𝗻 𝗔𝗜 𝗔𝗚𝗘𝗡𝗧𝗦! ⬇️ It's a very insightful read that doesn't just skim the surface and looks at the topic more from a strategic and business perspective than from a technical perspective. The paper goes deep into the core question: How will AI agents reshape business operations, and how quickly can your company adapt to stay ahead? According to McKinsey, this is the 𝗠𝗔𝗜𝗡 question every business need to answer today if they want to remain competitive tomorrow. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: ⬇️ ➜ 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗗𝗿𝗶𝘃𝗲 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: AI agents are no longer just a tech trend; they are at the heart of reshaping business operations. The real challenge is how quickly companies can adapt their processes to leverage AI-driven automation and decision-making. ➜ 𝗖𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆-𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗡𝗲𝗲𝗱𝘀: AI agents need to be tailored to the unique challenges of different industries. Whether it’s healthcare, finance, or manufacturing, customizing AI agents to solve specific industry problems can unlock higher efficiency and value. ➜ 𝗦𝘁𝗿𝗼𝗻𝗴 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗔𝗿𝗲 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹: Unlike traditional automation, AI agents require robust governance to ensure ethical behavior and compliance with regulations. Businesses need to establish clear policies to mitigate risks and build trust in AI systems. ➜ 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝗛𝘂𝗺𝗮𝗻𝘀 𝗮𝗻𝗱 𝗔𝗜: AI agents should enhance, not replace, human work. The real power lies in how AI agents can work alongside human expertise to free up employees for higher-level strategic tasks, making businesses more agile and competitive. ➜ 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗔𝗿𝗲 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹: For AI agents to succeed, they must seamlessly integrate with existing data systems. Companies need to invest in scalable data infrastructure to handle the growing complexity of AI systems, ensuring they can expand as needed across various business functions. --- 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗺𝗶𝗴𝗵𝘁 𝗯𝗲𝗰𝗼𝗺𝗲 𝘁𝗵𝗲 𝗸𝗲𝘆 𝘁𝗼 𝘀𝘁𝗮𝘆𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲. 𝗠𝗰𝗞𝗶𝗻𝘀𝗲𝘆'𝘀 𝗿𝗲𝗽𝗼𝗿𝘁 𝗺𝗮𝗸𝗲𝘀 𝗶𝘁 𝗰𝗹𝗲𝗮𝗿: 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝘁𝗵𝗮𝘁 𝗴𝗲𝘁 𝗮𝗵𝗲𝗮𝗱 𝗼𝗳 𝘁𝗵𝗲 𝗰𝘂𝗿𝘃𝗲 𝗻𝗼𝘄 𝘄𝗶𝗹𝗹 𝗿𝗲𝗮𝗽 𝘁𝗵𝗲 𝗿𝗲𝘄𝗮𝗿𝗱𝘀 𝗼𝗳 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆, 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗺𝗮𝗿𝗸𝗲𝘁 𝗱𝗼𝗺𝗶𝗻𝗮𝗻𝗰𝗲. Full report below or here: https://lnkd.in/dGXETSJY ⬇️ Enjoy!

"𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀!" 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗗𝗿𝗲𝗮𝗺: ➜ Deploy AI Agents ➜ Automate everything ➜ Enjoy efficiency 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: ➜ Messy, siloed, unreliable data ➜ AI hallucinations & compliance nightmares ➜ Enterprise AI initiatives stall as organizations spend more time fixing data issues than realizing AI-driven value. The Hard Truth: AI (agents) aren't failing—data strategies are. AI Agents are only as effective as the data beneath them. Without governed, high-quality data, AI adoption becomes an expensive experiment instead of a strategic advantage. Important to fix the data first. Kudos for this image to Armand Ruiz!

𝗗𝗼𝗻’𝘁 𝗱𝗲𝘃𝗲𝗹𝗼𝗽 𝗮𝗻 𝗔𝗜 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗽𝗮𝘁𝗵𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆! ⬇️ And don’t build your AI strategy in pure isolation. It seems to me that there are more and more enterprise AI roadmaps or AI-first company roadmaps. And yes, many of these strategies are well-informed, ambitious, and technically promising. But one foundational issue keeps surfacing: a lack of readiness in the underlying data infrastructure. AI — whether applied or generative — cannot deliver value without a solid, accessible, and trusted data ecosystem. And yet, many organizations are still underinvesting in this area, treating data as a side concern rather than the foundation it is. If your data landscape is fragmented, inaccessible, or of poor quality, no AI model — no matter how advanced — will produce meaningful results. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗿𝗲𝗲 𝗰𝗼𝗻𝗰𝗿𝗲𝘁𝗲 𝘀𝘁𝗲𝗽𝘀 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝘀 𝗰𝗮𝗻 𝘁𝗮𝗸𝗲 𝘁𝗼 𝗯𝗲𝘁𝘁𝗲𝗿 𝗮𝗹𝗶𝗴𝗻 𝘁𝗵𝗲𝗶𝗿 𝗱𝗮𝘁𝗮 𝗮𝗻𝗱 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀: ⬇️ 1. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗮𝗻 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗺𝗼𝗱𝗲𝗹. ➜ Establish a unified governance framework that spans both data management and AI oversight. This ensures consistency across how data is acquired, maintained, and used — enabling AI systems to operate transparently, compliantly, and effectively. 2. 𝗜𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝗮 𝗱𝗮𝘁𝗮-𝗳𝗶𝗿𝘀𝘁 𝗰𝘂𝗹𝘁𝘂𝗿𝗲. ➜ Technology alone won’t fix fragmented data. Organizations must embed data literacy across functions, prioritize clean and reusable data assets, and ensure cross-functional teams understand the strategic value of data in AI development. 3. 𝗔𝗹𝗶𝗴𝗻 𝗔𝗜 𝗮𝗻𝗱 𝗱𝗮𝘁𝗮 𝘁𝗲𝗮𝗺𝘀 𝘂𝗻𝗱𝗲𝗿 𝘀𝗵𝗮𝗿𝗲𝗱 𝗴𝗼𝗮𝗹𝘀. ➜ Too often, AI initiatives are launched without proper collaboration with data teams. Ensure alignment through shared KPIs, integrated project planning, and clear ownership over the full data-to-AI lifecycle. Take for example GenAI: Both fine-tuning large models and implementing retrieval-augmented generation (RAG) approaches rely on the same prerequisites: high-quality, accessible, well-governed data. 𝗘𝗶𝘁𝗵𝗲𝗿 𝘄𝗮𝘆, 𝗻𝗼 𝗴𝗼𝗼𝗱 𝗱𝗮𝘁𝗮 = 𝗻𝗼 𝗴𝗼𝗼𝗱 𝗔𝗜. 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮 𝘁𝗲𝗰𝗵 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲 — 𝗶𝘁’𝘀 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻. 𝗔𝗻𝗱 𝗶𝘁 𝗺𝘂𝘀𝘁 𝗿𝗲𝘀𝘁 𝗼𝗻 𝗮 𝗱𝗮𝘁𝗮 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘁𝗵𝗮𝘁’𝘀 𝗱𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲 𝘄𝗶𝘁𝗵 𝗶𝘁. 𝗜𝗳 𝘆𝗼𝘂’𝗿𝗲 𝗻𝗼𝘁 𝗶𝗻𝘃𝗲𝘀𝘁𝗶𝗻𝗴 𝗶𝗻 𝗯𝗼𝘁𝗵 𝗮𝘁 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝘁𝗶𝗺𝗲, 𝘆𝗼𝘂’𝗿𝗲 𝗻𝗼𝘁 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗳𝗼𝗿 𝗶𝗺𝗽𝗮𝗰𝘁. 𝗕𝘂𝗶𝗹𝗱 𝗯𝗼𝘁𝗵 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝗶𝗻 𝘀𝘆𝗻𝗰. 𝗧𝗵𝗮𝘁’𝘀 𝘄𝗵𝗲𝗿𝗲 𝗿𝗲𝗮𝗹 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝗰𝗿𝗲𝗮𝘁𝗲𝗱!

𝗢𝗽𝗲𝗻𝗔𝗜’𝘀 𝘂𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗴𝘂𝗶𝗱𝗲 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗷𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱! ⬇️ If you're serious about AI Agents, this is the guide you've been waiting for. It’s packed with everything you need to build powerful AI agents. It follows a very hands-on approach that cuts down your time and avoids the common mistakes most developers make. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝘁𝗼𝗽 10 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗳𝗿𝗼𝗺 𝗢𝗽𝗲𝗻𝗔𝗜'𝘀 𝗚𝘂𝗶𝗱𝗲: ➜ Agents = Autonomy: Unlike simple chatbots, agents are autonomous systems that can handle complex tasks, make decisions, and manage workflows without constant human input. They go beyond pre-programmed responses. ➜ When Should You Build an Agent? If your task requires nuanced decision-making or complex data handling, like fraud detection, claims processing, or automated content moderation, building an agent is your solution. ➜ Key Components of an Agent: Every agent relies on three crucial elements: a reasoning model (for decision-making), tools (for action), and instructions (for guiding behavior). Ensure these components are designed robustly for efficiency. ➜ Tools Empower Agents: Tools allow agents to interact with the external world, whether querying databases, making API calls, or sending emails. They significantly expand an agent's capabilities beyond just processing language. ➜ Clear Instructions Lead to Success: Avoid ambiguity in the instructions. The more specific and detailed your instructions are, the better the agent will perform, especially for complex tasks or edge cases. ➜ Start Simple, Then Scale: Start with a single-agent system to solve one task. Only expand to multi-agent systems when the complexity of the problem demands it. Managers can supervise multiple agents but start small for better control. ➜ Guardrails Are Essential: Build safety layers into your agents. Ensure they operate within desired parameters by setting up guardrails to prevent risky or undesirable behaviors—especially when dealing with sensitive data or high-stakes tasks. ➜ Incorporate Human Oversight: For high-risk operations, include human oversight. A "human-in-the-loop" approach allows for corrective actions before mistakes or undesirable outcomes occur, ensuring your agent stays on track. ➜ Iterate and Improve: Don’t expect perfection at first. Launch small, validate with real users, and continuously improve. Agents evolve and become more valuable with each iteration as they learn and adapt to new tasks. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗶𝘀 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗮 𝗳𝘂𝘁𝘂𝗿𝗲 𝗰𝗼𝗻𝗰𝗲𝗽𝘁—𝗶𝘁’𝘀 𝘁𝗵𝗲 𝗸𝗲𝘆 𝘁𝗼 𝗱𝗿𝗶𝘃𝗶𝗻𝗴 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗰𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝘁𝗼𝗱𝗮𝘆. Download below or access here: https://lnkd.in/d_ayffvZ ENJOY!

𝗧𝗵𝗲 𝗺𝗼𝘀𝘁 𝘂𝗻𝗱𝗲𝗿𝗿𝗮𝘁𝗲𝗱 𝗽𝗮𝗿𝘁 𝗼𝗳 𝗔𝗜 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁? 𝗞𝗻𝗼𝘄𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀! You can’t build efficient AI systems—or any scalable software product—without understanding how data is organized, stored, and retrieved. These data structures power nearly everything we do online — from scrolling social media to using AI assistants and navigating digital maps. 𝘏𝘦𝘳𝘦 𝘢𝘳𝘦 10 𝘥𝘢𝘵𝘢 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦𝘴 𝘺𝘰𝘶 𝘴𝘩𝘰𝘶𝘭𝘥 𝘬𝘯𝘰𝘸 𝘢𝘯𝘥 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘶𝘴𝘦 𝘦𝘷𝘦𝘳𝘺 𝘥𝘢𝘺 (y𝘦𝘴, 𝘦𝘷𝘦𝘯 𝘪𝘧 𝘺𝘰𝘶’𝘷𝘦 𝘯𝘦𝘷𝘦𝘳 𝘸𝘳𝘪𝘵𝘵𝘦𝘯 𝘢 𝘭𝘪𝘯𝘦 𝘰𝘧 𝘤𝘰𝘥𝘦): ⬇️ 1. 𝗟𝗶𝘀𝘁 – 𝗧𝘄𝗶𝘁𝘁𝗲𝗿 𝗳𝗲𝗲𝗱𝘀:  ➜ Think of a simple chain of posts. Each tweet follows the last one. This is a list: organized, sequential, and easy to keep scrolling through. 2. 𝗔𝗿𝗿𝗮𝘆 – 𝗠𝗮𝘁𝗵 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀, 𝗹𝗮𝗿𝗴𝗲 𝗱𝗮𝘁𝗮 𝘀𝗲𝘁𝘀: ➜ Arrays are like organized drawers. Each item is in a fixed position. They’re used in calculations, spreadsheets, and AI models where fast access to numeric data is key. 3. 𝗦𝘁𝗮𝗰𝗸 – 𝗨𝗻𝗱𝗼/𝗥𝗲𝗱𝗼 𝗶𝗻 𝗪𝗼𝗿𝗱 𝗲𝗱𝗶𝘁𝗼𝗿𝘀: ➜ Ever hit "undo" in Microsoft Word? That’s a stack at work. Last action goes on top, first to be undone. Like a stack of plates! 4. 𝗤𝘂𝗲𝘂𝗲 – 𝗣𝗿𝗶𝗻𝘁𝗲𝗿 𝗷𝗼𝗯𝘀, 𝘂𝘀𝗲𝗿 𝗮𝗰𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗴𝗮𝗺𝗲𝘀: ➜ A queue works like a line at the bakery. First in, first out. It’s how your print jobs are handled—or how game moves are processed one by one. 5. 𝗛𝗲𝗮𝗽 – 𝗧𝗮𝘀𝗸 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴:  ➜ Heaps prioritize. They’re used to decide which task runs first on your computer or which ride you get in Uber’s backend scheduling system. 6. 𝗧𝗿𝗲𝗲 – 𝗛𝗧𝗠𝗟 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝘀, 𝗔𝗜 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝘁𝗿𝗲𝗲𝘀: ➜ A tree is perfect for structured decisions. Websites use them to load content. AI uses them to make decisions (yes, like “is this a cat or not?”). 7. 𝗦𝘂𝗳𝗳𝗶𝘅 𝗧𝗿𝗲𝗲 – 𝗦𝗲𝗮𝗿𝗰𝗵 𝗶𝗻 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝘀:  ➜ Need to find a word fast in a massive document? That’s what suffix trees do. Also used in code autocomplete and text processing. 8. 𝗚𝗿𝗮𝗽𝗵 – 𝗦𝗼𝗰𝗶𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀, 𝗽𝗮𝘁𝗵𝗳𝗶𝗻𝗱𝗶𝗻𝗴:   ➜ Facebook friendships? Google Maps routes? Both run on graphs. They connect nodes (people, places) with relationships (friendship, roads). 9. 𝗥-𝗧𝗿𝗲𝗲 – 𝗙𝗶𝗻𝗱𝗶𝗻𝗴 𝘄𝗵𝗮𝘁'𝘀 𝗰𝗹𝗼𝘀𝗲 𝗯𝘆:  ➜ Ever search “restaurants near me”? R-Trees help systems figure out what’s physically nearby. Used in mapping apps and autonomous vehicle sensors. 10. 𝗛𝗮𝘀𝗵 𝗧𝗮𝗯𝗹𝗲 – 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀: ➜ Want lightning-fast data access? Hash tables map keys to values instantly. Used in browser caches, password lookups, and even AI memory storage. 𝗘𝘃𝗲𝗿𝘆 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻—𝗳𝗿𝗼𝗺 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝘁𝗼 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗰𝗮𝗿𝘀—𝘂𝘀𝗲𝘀 𝘁𝗵𝗲𝘀𝗲 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 𝘁𝗼 𝘀𝘁𝗼𝗿𝗲, 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲, 𝗮𝗻𝗱 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻. Kudos to ByteByteGo for this brilliant overview!

𝗢𝗽𝗲𝗻𝗔𝗜’𝘀 𝘂𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗴𝘂𝗶𝗱𝗲 𝘁𝗼 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝗮𝗻𝗱 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗔𝗜 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝗷𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱! ⬇️ [𝘈𝘯𝘥 𝘺𝘰𝘶 𝘚𝘏𝘖𝘜𝘓𝘋 𝘳𝘦𝘢𝘥 𝘪𝘵 𝘪𝘧 𝘺𝘰𝘶'𝘳𝘦 𝘸𝘰𝘳𝘬𝘪𝘯𝘨 𝘪𝘯 𝘦𝘯𝘵𝘦𝘳𝘱𝘳𝘪𝘴𝘦 𝘈𝘐.] 𝗧𝗵𝗲 𝗺𝗮𝗷𝗼𝗿 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗳𝗮𝗰𝗲 𝘁𝗼𝗱𝗮𝘆: 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝗮𝗻𝗱 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗔𝗜 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝘁𝗮𝗻𝗴𝗶𝗯𝗹𝗲 𝘃𝗮𝗹𝘂𝗲. As AI continues to transform industries, it’s not enough to simply implement technology—you need a strategic approach to find and scale the right use cases. 𝗔𝘀 𝗮 𝘀𝘂𝗺𝗺𝗮𝗿𝘆, 𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝗳𝗶𝘃𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲 𝗮𝘄𝗮𝘆𝘀: 𝗔𝗜 𝗦𝗵𝗼𝘂𝗹𝗱 𝗕𝗲 𝗟𝗲𝗱 𝗯𝘆 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽: ➜ AI adoption requires more than just a tech team—it needs leadership at the helm. Success hinges on clear vision and top-down commitment. Without strong leadership support, AI initiatives often fail to scale and deliver. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗛𝗶𝗴𝗵-𝗜𝗺𝗽𝗮𝗰𝘁, 𝗟𝗼𝘄-𝗘𝗳𝗳𝗼𝗿𝘁 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀: ➜ Start with low-effort, high-impact use cases. These quick wins build momentum and deliver immediate value. Using the “Impact/Effort Framework,” companies can prioritize projects that offer the most benefit with minimal complexity. 𝗘𝗺𝗽𝗹𝗼𝘆𝗲𝗲 𝗘𝗺𝗽𝗼𝘄𝗲𝗿𝗺𝗲𝗻𝘁 𝗶𝘀 𝗞𝗲𝘆 𝘁𝗼 𝗔𝗜 𝗦𝘂𝗰𝗰𝗲𝘀𝘀: ➜ Empower your employees to spot AI opportunities. By training them on basic AI concepts, companies can harness their creativity to integrate AI into their daily workflows, driving efficiencies at scale. 𝗧𝗮𝗶𝗹𝗼𝗿 𝗔𝗜 𝘁𝗼 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆-𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀:   ➜ AI isn’t one-size-fits-all. Customize it for your industry’s specific challenges, whether in healthcare, finance, or manufacturing. Industry-tailored AI solutions are more effective and create greater operational efficiencies. 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 𝗮𝗻𝗱 𝗦𝗰𝗮𝗹𝗲 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀:   ➜ Start small and iterate. Begin with narrow applications—like content creation or data analysis—and scale them to address larger, multi-step workflows across departments. This iterative approach ensures steady growth and avoids overwhelming the organization. Scaling AI use cases is not just about technology; it’s about aligning AI with business goals and fostering innovation. Only with the right strategy, companies can unlock AI’s full potential!

𝗜𝗳 𝘆𝗼𝘂 𝗳𝗼𝗹𝗹𝗼𝘄 𝘁𝗵𝗲 𝗻𝗲𝘄𝘀, 𝘆𝗼𝘂’𝘃𝗲 𝗽𝗿𝗼𝗯𝗮𝗯𝗹𝘆 𝘀𝗲𝗲𝗻 𝗶𝘁 𝗮𝗹𝗹: 𝗔𝗜 𝗶𝘀 𝗯𝗼𝗼𝗺𝗶𝗻𝗴. 𝗔𝗜 𝗶𝘀 𝗼𝘃𝗲𝗿𝗵𝘆𝗽𝗲𝗱. 𝗔𝗜 𝘄𝗶𝗹𝗹 𝘀𝗮𝘃𝗲 𝘂𝘀. 𝗔𝗜 𝘄𝗶𝗹𝗹 𝗱𝗲𝘀𝘁𝗿𝗼𝘆 𝗷𝗼𝗯𝘀. The Stanford University AI Index 2025 cuts through all of it. Produced by the Institute for Human-Centered Artificial Intelligence, it’s one of the most respected and data-driven reports on the state of AI today. Over 400+ pages of concrete insights — from technical benchmarks and real-world adoption to policy shifts, economic impact, education, and public sentiment. 𝗧𝗵𝗲 2025 𝗲𝗱𝗶𝘁𝗶𝗼𝗻 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝗹𝗮𝘀𝘁 𝘄𝗲𝗲𝗸. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 12 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: 1. 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀 𝗮𝗿𝗲 𝗯𝗲𝗶𝗻𝗴 𝗰𝗿𝘂𝘀𝗵𝗲𝗱. ➝ AI performance on complex reasoning and programming tasks surged by up to 67 percentage points in just one year. 2. 𝗔𝗜 𝗶𝘀 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝘀𝘁𝘂𝗰𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗮𝗯. ➝ 223 FDA-approved AI medical devices. Over 150,000 autonomous rides weekly from Waymo. This is mainstream adoption. 3. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗶𝘀 𝗴𝗼𝗶𝗻𝗴 𝗮𝗹𝗹-𝗶𝗻.  ➝ $109B in U.S. private AI investment. 78% of organizations using AI. Productivity gains are no longer theoretical. 4. 𝗧𝗵𝗲 𝗨.𝗦. 𝗹𝗲𝗮𝗱𝘀 𝗶𝗻 𝗾𝘂𝗮𝗻𝘁𝗶𝘁𝘆—𝗖𝗵𝗶𝗻𝗮’𝘀 𝗰𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝘂𝗽 𝗼𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆.  ➝ Chinese models now rival U.S. models on MMLU, HumanEval, and more. Global AI is becoming a multi-polar game. 5. 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝗶𝘀 𝗹𝗮𝗴𝗴𝗶𝗻𝗴 𝗯𝗲𝗵𝗶𝗻𝗱 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻. ➝ Incidents are rising, but standardized RAI benchmarks and audits are still rare.   Governments are stepping in faster than vendors. 6. 𝗚𝗹𝗼𝗯𝗮𝗹 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝗺 𝗶𝘀 𝗿𝗶𝘀𝗶𝗻𝗴—𝗯𝘂𝘁 𝗻𝗼𝘁 𝗲𝘃𝗲𝗻𝗹𝘆.   ➝ 83% of people in China are optimistic about AI. In the U.S., that number is just 39%. 7. 𝗔𝗜 𝗶𝘀 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗰𝗵𝗲𝗮𝗽𝗲𝗿, 𝘀𝗺𝗮𝗹𝗹𝗲𝗿, 𝗮𝗻𝗱 𝗳𝗮𝘀𝘁𝗲𝗿.  ➝ The cost of GPT-3.5-level inference dropped 280x in two years. Open-weight models are nearly matching closed ones. 8. 𝗚𝗼𝘃𝗲𝗿𝗻𝗺𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗶𝗻𝘃𝗲𝘀𝘁𝗶𝗻𝗴.  ➝ From Canada’s $2.4B to Saudi Arabia’s $100B push—states aren’t watching from the sidelines anymore. 9. 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗲𝘅𝗽𝗮𝗻𝗱𝗶𝗻𝗴—𝗯𝘂𝘁 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 𝗹𝗮𝗴𝘀. ➝ Access is improving, but infrastructure gaps and lack of teacher training still limit global reach. 10. 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗶𝘀 𝗱𝗼𝗺𝗶𝗻𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁.   ➝ 90% of top AI models now come from companies—not academia. The gap between top players is shrinking fast. 11. 𝗔𝗜 𝗶𝘀 𝘀𝗵𝗮𝗽𝗶𝗻𝗴 𝘀𝗰𝗶𝗲𝗻𝗰𝗲.   ➝ AI-driven breakthroughs in physics, chemistry, and biology are earning Nobel Prizes and Turing Awards. 12. 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗿𝗲𝗺𝗮𝗶𝗻𝘀 𝘁𝗵𝗲 𝗰𝗲𝗶𝗹𝗶𝗻𝗴.   ➝ Despite all the progress, models still struggle with logic-heavy tasks. Precision is still a challenge. You can download the full report FREE here: https://lnkd.in/dzzuE5tN

𝗗𝗼 𝘆𝗼𝘂 𝗸𝗻𝗼𝘄 𝗵𝗼𝘄 𝗺𝘂𝗰𝗵 𝗶𝘁 𝗰𝗼𝘀𝘁𝘀 𝘁𝗼 𝘁𝗿𝗮𝗶𝗻 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠𝘀)? ⬇️ AI training costs are exploding lately. The Stanford 2025 AI Index Report has released the latest AI training numbers last week and they are crazy: → Original Transformer Model: $930 → GPT-3: $4.3M → GPT-4: $78.4M → Llama 3.1-405B - $170M → Gemini 1.0 Ultra - $192M Training LLMs from scratch costs millions and these numbers are currently climbing even higher even higher with the development of the newest models. This is why primarily Big Tech companies and well-funded startups can afford to undertake such projects. But why is this the case? 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗲𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻: 1️⃣ 𝗗𝗮𝘁𝗮: → Curating TBs of data and extensive pre-processing are needed. This involves collecting, cleaning, and organizing data to ensure the model trains on high-quality information. This task is resource-intensive, requiring significant time and manpower. 2️⃣ 𝗔𝗜 𝗧𝗮𝗹𝗲𝗻𝘁 𝗮𝗻𝗱 𝗦𝗸𝗶𝗹𝗹𝘀: → Developing LLMs requires top researchers, with compensation at companies like OpenAI rumored up to $10M. A team of machine learning, data science, and linguistic experts is essential. They design neural networks, manage training processes, and assess performance. The significant cost of hiring and retaining this skilled workforce is crucial. 3️⃣ 𝗔𝗜 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗣𝗼𝘄𝗲𝗿: → Training and developing LLMs is incredibly expensive due to the vast computational resources required, with models like GPT-4 needing thousands of GPUs running for months (!). This extensive use of GPUs, combined with the need for continuous fine-tuning and experimentation, significantly drives up both the hardware and operational costs. 𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀? Take existing LLM models and enhance them with your enterprise data using techniques like RAG or fine-tuning.

Boston Consulting Group (BCG) 𝗷𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝘁𝗵𝗲𝗶𝗿 𝗹𝗮𝘁𝗲𝘀𝘁 𝗣𝗢𝗩 𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 (𝗠𝗖𝗣)! The paper unpacks how autonomous agents are evolving, where they are already delivering real value, and why protocols like MCP (Model Context Protocol) and A2A (Agent-to-Agent Communication) are 𝗘𝗦𝗦𝗘𝗡𝗧𝗜𝗔𝗟 to scaling them securely and reliably across enterprises. If you are looking for an insightful read on why the real disruptive technology isn't AI, but AI Agents, this is a great paper! 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝘀𝗲𝘃𝗲𝗻 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: 1. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗴𝗲𝗻𝘁𝘀 𝗔𝗿𝗲 𝗠𝗼𝘃𝗶𝗻𝗴 𝗙𝗿𝗼𝗺 𝗖𝗼𝗻𝗰𝗲𝗽𝘁 𝘁𝗼 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: ➜ Early deployments are already delivering 30–90% improvements in speed, productivity, and cost across coding, compliance, and supply chain domains. 2. 𝗠𝗖𝗣 𝗜𝘀 𝗕𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗕𝗮𝗰𝗸𝗯𝗼𝗻𝗲 𝗼𝗳 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗔𝗴𝗲𝗻𝘁𝘀: ➜ The Model Context Protocol (MCP) is the new open standard adopted by Anthropic, OpenAI, Microsoft, Google, and Amazon to expose tools, prompts, and resources reliably. 3. 𝗔𝗴𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗜𝘀 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀𝗶𝗻𝗴 𝗥𝗮𝗽𝗶𝗱𝗹𝘆: ➜ Agents today can automate tasks up to one hour long — and this limit is doubling every seven months, pushing toward multi-day autonomous workflows by the end of the decade. 4. 𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 𝗠𝘂𝘀𝘁 𝗕𝗲 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆-𝗙𝗶𝗿𝘀𝘁: ➜ Security challenges grow as agents gain system access. OAuth, RBAC, permission isolation, eval-driven development, and real-time monitoring are mandatory to deploy agents safely. 5. 𝗧𝗵𝗲 𝗥𝗶𝘀𝗲 𝗼𝗳 𝗔𝗴𝗲𝗻𝘁-𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀: ➜ Platforms like Azure Foundry, Vertex AI, Bedrock Agents, and Lindy are positioning themselves as the orchestration layer to create, manage, and scale enterprise agent ecosystems. 6. 𝗙𝗿𝗼𝗺 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝘁𝗼 𝗙𝘂𝗹𝗹𝘆 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗴𝗲𝗻𝘁𝘀: ➜ Enterprises are shifting from prompt chaining (rigid workflows) to fully autonomous agents capable of observing, reasoning, and acting dynamically based on real-world feedback. 7. 𝗠𝗖𝗣 𝗮𝗻𝗱 𝗔2𝗔 𝗪𝗶𝗹𝗹 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗘𝗰𝗼𝗻𝗼𝗺𝘆: ➜ MCP connects agents to tools and data. A2A (Agent-to-Agent communication) will enable agents to negotiate, collaborate, and coordinate across systems — forming true multi-agent networks. The agent economy is currently build— with real protocols, real deployments, and real technical foundations. Understanding it today means shaping the competitive advantage of tomorrow! You can download the document below! ENJOY!

Anthropic 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗮 𝗱𝗲𝗻𝘀𝗲 𝗮𝗻𝗱 𝗵𝗶𝗴𝗵𝗹𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗿𝗲𝗽𝗼𝗿𝘁 𝗼𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝗽𝗮𝗰𝗸𝗲𝗱 𝘄𝗶𝘁𝗵 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀: ⬇️ Not just marketing, BUT a real, practical blueprint for developers and teams building AI agents that actually work. It explains how Claude Code (tool for agentic coding) can function as a software developer: writing, reviewing, testing, and even managing Git workflows autonomously. BUT in my view: The principles and patterns described in this document are not Claude-specific. You can apply them to any coding agent — from OpenAI’s Codex to Goose, Aider, or even tools like Cursor and GitHub Copilot Workspace. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 7 𝗸𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗲𝘁𝘁𝗲𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱: ⬇️ 1. 𝗔𝗴𝗲𝗻𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 ≠ 𝗷𝘂𝘀𝘁 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 ➜ It’s not about clever prompts. It’s about building structured workflows — where the agent can reason, act, reflect, retry, and escalate. Think of agents like software components: stateless functions won’t cut it. 2. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗶𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 ➜ The way you manage and pass context determines how useful your agent becomes. Using summaries, structured files, project overviews, and scoped retrieval beats dumping full files into the prompt window. 3. 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗶𝘀𝗻’𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 ➜ You can’t expect an agent to solve multi-step problems without an explicit process. Patterns like plan > execute > review, tool use when stuck, or structured reflection are necessary. And they apply to all models, not just Claude. 4. 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗮𝗴𝗲𝗻𝘁𝘀 𝗻𝗲𝗲𝗱 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗼𝗼𝗹𝘀 ➜ Shell access. Git. APIs. Tool plugins. The agents that actually get things done use tools — not just language. Design your agents to execute, not just explain. 5. 𝗥𝗲𝗔𝗰𝘁 𝗮𝗻𝗱 𝗖𝗼𝗧 𝗮𝗿𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰 𝘁𝗿𝗶𝗰𝗸𝘀 ➜ Don’t just ask the model to “think step by step.” Build systems that enforce that structure: reasoning before action, planning before code, feedback before commits. 6. 𝗗𝗼𝗻’𝘁 𝗰𝗼𝗻𝗳𝘂𝘀𝗲 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 𝘄𝗶𝘁𝗵 𝗰𝗵𝗮𝗼𝘀 ➜ Autonomous agents can cause damage — fast. Define scopes, boundaries, fallback behaviors. Controlled autonomy > random retries. 7. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝗶𝗻 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 ➜ A good agent isn’t just a wrapper around an LLM. It’s an orchestrator: of logic, memory, tools, and feedback. And if you’re scaling to multi-agent setups — orchestration is everything. Check the comments for the original material! Enjoy! Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents!

𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲’𝘀 𝗧𝗔𝗟𝗞𝗜𝗡𝗚 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀. Very few can explain what they really are — or why they matter. Let’s fix that. Here’s the breakdown. ⬇️ 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁? AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users. They reason, plan, and act — with memory and autonomy. And they operate in a continuous loop: 1️⃣ Think – Process data and context 2️⃣ Plan – Decide how to achieve the goal 3️⃣ Act – Execute via tools, APIs, or interfaces 4️⃣ Reflect – Evaluate results and adapt This feedback loop makes agents adaptive, iterative, and capable of learning. --- 𝗛𝗼𝘄 𝗔𝗴𝗲𝗻𝘁𝘀 𝗪𝗼𝗿𝗸 (left panel): ➜ You delegate a task ➜ The agent takes autonomous action ➜ It connects to tools, APIs, or the web — uses memory, adapts to input ➜ You’re still in control — but it runs on its own Think of it as a smart intern that never sleeps — and keeps improving. --- 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 (middle panel): Different agents, different strengths — just like any team: ➜ Simple Reflex Agents = rule-based triggers ➜ Model-Based = uses memory to guide decisions ➜ Goal-Based = acts with outcomes in mind ➜ Utility-Based = weighs options and tradeoffs ➜ Learning Agents = continuously improve You wouldn’t run a business with just one intern — same goes for agents. --- 𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 (right panel): How you structure your agents matters just as much as what they can do: ➜ Single Agent = task-specific assistant ➜ Multi-Agent = agents coordinate and collaborate ➜ Human-Machine = agents work with humans in the loop --- And this is where most enterprises still struggle — not with the technology, but with governance, security, and trust. AI agents aren’t the future. They’re already here. Most organizations just haven’t figured out how to use them at scale — yet. --- Kudos to ByteByteGo for this amazing graphic!

𝗧𝗵𝗶𝘀 𝗶𝘀 𝗵𝗮𝗻𝗱𝘀 𝗱𝗼𝘄𝗻 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗕𝗘𝗦𝗧 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗵𝗼𝘄 𝗟𝗟𝗠𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸. ⬇️ 𝘓𝘦𝘵'𝘴 𝘣𝘳𝘦𝘢𝘬 𝘪𝘵 𝘥𝘰𝘸𝘯: 𝗧𝗼𝗸𝗲𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 & 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀: - Input text is broken into tokens (smaller chunks).   - Each token is mapped to a vector in high-dimensional space, where words with similar meanings cluster together.  𝗧𝗵𝗲 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺 (𝗦𝗲𝗹𝗳-𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻):   - Words influence each other based on context — ensuring "bank" in riverbank isn’t confused with financial bank.   - The Attention Block weighs relationships between words, refining their representations dynamically.  𝗙𝗲𝗲𝗱-𝗙𝗼𝗿𝘄𝗮𝗿𝗱 𝗟𝗮𝘆𝗲𝗿𝘀 (𝗗𝗲𝗲𝗽 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴)   - After attention, tokens pass through multiple feed-forward layers that refine meaning.   - Each layer learns deeper semantic relationships, improving predictions.  𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 - This process repeats through dozens or even hundreds of layers, adjusting token meanings iteratively.   - This is where the "deep" in deep learning comes in — layers upon layers of matrix multiplications and optimizations.  𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 & 𝗦𝗮𝗺𝗽𝗹𝗶𝗻𝗴 - The final vector representation is used to predict the next word as a probability distribution.   - The model samples from this distribution, generating text word by word.  𝗧𝗵𝗲𝘀𝗲 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝗰𝘀 𝗮𝗿𝗲 𝗮𝘁 𝘁𝗵𝗲 𝗰𝗼𝗿𝗲 𝗼𝗳 𝗮𝗹𝗹 𝗟𝗟𝗠𝘀 (𝗲.𝗴. 𝗖𝗵𝗮𝘁𝗚𝗣𝗧). 𝗜𝘁 𝗶𝘀 𝗰𝗿𝘂𝗰𝗶𝗮𝗹 𝘁𝗼 𝗵𝗮𝘃𝗲 𝗮 𝘀𝗼𝗹𝗶𝗱 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗵𝗼𝘄 𝘁𝗵𝗲𝘀𝗲 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝗰𝘀 𝘄𝗼𝗿𝗸 𝗶𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲, 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀.  Here is the full video from 3Blue1Brown with exaplantion. I highly recommend to read, watch and bookmark this for a further deep dive: https://lnkd.in/dAviqK_6 Enjoy!

𝗥𝗔𝗚 𝗶𝘀 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗷𝘂𝘀𝘁 “𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲 𝗮𝗻𝗱 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲" 𝗼𝗿 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲. 𝗜𝘁’𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺 𝗳𝗼𝗿 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜. ⬇️ By early 2025, over 51% of enterprise GenAI deployments use RAG architectures — up from 31% just a year earlier. And for good reason: it’s powering everything from customer support and legal automation to search and content generation. BUT real-world complexity demands modular, dynamic, and intelligent system architectures — not simplistic pipelines. What started as a simple retrieval pipeline (Naive RAG) is now evolving into the architectural backbone of large-scale, production-grade reasoning systems. Below is one of the clearest overviews of the evolving RAG design space — from Naive setups to Agentic multi-system architectures. 𝗟𝗲𝘁'𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻: ⬇️ 𝗡𝗮𝗶𝘃𝗲 𝗥𝗔𝗚 ➜ Retrieve documents, pass them to the LLM, generate an output. - Fast to build - Fragile when faced with ambiguity, long context, or conflicting information 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗲-𝗮𝗻𝗱-𝗥𝗲𝗿𝗮𝗻𝗸 𝗥𝗔𝗚 ➜ Adds reranking to prioritize the most relevant information before generation. - Improves accuracy and grounding - Reduces risk of hallucinations 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗥𝗔𝗚 ➜ Extends retrieval and reasoning to include text, images, video, and audio. - Critical for industries handling unstructured, diverse data types - Unlocks new applications in healthcare, legal, automotive, and manufacturing 𝗚𝗿𝗮𝗽𝗵 𝗥𝗔𝗚 ➜ Incorporates graph databases for structured reasoning across entities and relationships. - Enables explainable AI - Essential for compliance, auditing, supply chain, and knowledge management 𝗛𝘆𝗯𝗿𝗶𝗱 𝗥𝗔𝗚 ➜ Blends vector search, keyword search, and graph retrieval strategies. - Maximizes robustness and adaptability across use cases - Balances precision and recall for production environments 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 (𝗥𝗼𝘂𝘁𝗲𝗿) ➜ Uses agent-based orchestration to dynamically route queries to specialized tools, indexes, or retrieval strategies. - Intelligent query handling - Core enabler for autonomous workflows 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗥𝗔𝗚 ➜ Multiple agents collaborate, reason, retrieve, and act across distributed systems. - Supports complex planning, tool use, and decision-making - The foundation for enterprise-grade AI orchestration and multi-modal workflows 𝗥𝗔𝗚 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 — 𝗶𝘁’𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲, 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗚𝗲𝗻𝗔𝗜. 𝗘𝗮𝗰𝗵 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘀𝘁𝘆𝗹𝗲 𝘀𝗲𝗿𝘃𝗲𝘀 𝗮 𝗱𝗶𝘀𝘁𝗶𝗻𝗰𝘁 𝗽𝘂𝗿𝗽𝗼𝘀𝗲 — 𝗳𝗿𝗼𝗺 𝘀𝗶𝗺𝗽𝗹𝗲 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝘁𝗼 𝗰𝗼𝗺𝗽𝗹𝗲𝘅, 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. Kudos to Weaviate for this brilliant cheatsheet!

𝗧𝗵𝗲 𝗠𝗢𝗦𝗧 𝗱𝗶𝘀𝗿𝘂𝗽𝘁𝗶𝘃𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗶𝘀 𝗡𝗢𝗧 𝗔𝗜, 𝗶𝘁'𝘀 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀! ⬇️ Because with AI Agents, AI finally gets hands and feet — and can start working on its own. This is the moment AI stops being a tool — and starts acting like a team member. 𝗠𝗔𝗡𝗬 𝗼𝗳 𝘆𝗼𝘂 𝗮𝘀𝗸𝗲𝗱 𝗳𝗼𝗿 𝗮 𝗰𝗹𝗲𝗮𝗿 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗿𝗼𝗮𝗱𝗺𝗮𝗽! So here it is — a practical breakdown of what AI Agents are, why they matter, and how to get started. 2025 will be the year AI Agents move from demo to deployment. And those who understand how to build with them — will define the next era. To help you, I`ve attached a detailed roadmap made in collaboration with Rakesh Gohel (a true expert in the AI agent space!): ⬇️ 📌 𝗟𝗲𝘃𝗲𝗹 1: 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗠𝗟 𝗮𝗻𝗱 𝗚𝗲𝗻𝗔𝗜 - Basics of Python and Typescript Key Concepts to Learn: a. Data types and control structures. b. File I/O operations. c. Introduction to simple networks. - Basics of Machine Learning Key Concepts to Learn: a. Types of Machine Learning (Supervised, Unsupervised, Reinforcement). b. Neural Networks and Deep Learning fundamentals. c. Basics of Reinforcement Learning. - Basics of API Wrappers Key Concepts to Learn: a. Understanding different API types. b. Building GPT-based wrappers. c. Authentication and secure API communication. - Basics of Prompt Engineering Key Concepts to Learn: a. Chain of Thought and Graph of Thought prompting. b. Zero-shot and Few-shot techniques. c. Designing role-based prompts. - Basics of LLMs Key Concepts to Learn: a. Transformer models and MoE (Mixture of Experts). b. Fine-tuning LLMs for custom tasks. c. Understanding context windows. 📌 𝗟𝗲𝘃𝗲𝗹 2: 𝗗𝗲𝗲𝗽 𝗗𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗥𝗔𝗚𝘀 𝗮𝗻𝗱 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 - Basics of RAGs Key Concepts to Learn: a. Embeddings and vector databases. b. Retrieval and generation models. - Basics of AI Agents Key Concepts to Learn: a. Different types of agents and their design patterns. b. Tools and agent memory management. - AI Agent Frameworks Key Concepts to Learn: a. Orchestration, planning, and feedback loops. b. Streaming agent workflows. - Multi-Agent Systems Key Concepts to Learn: a. Communication patterns and hand-offs. b. A2A (Agent-to-Agent) Protocols. - Evaluation and Observability Key Concepts to Learn: a. Metrics for evaluation (latency, stress tests, logging). b. Building observability for scalable AI agents. 𝗧𝗵𝗶𝘀 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 𝘄𝗶𝗹𝗹 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝗺𝗼𝘃𝗲 𝗺𝘂𝗰𝗵 𝗳𝗮𝘀𝘁𝗲𝗿 𝗶𝗻 𝗺𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀𝗅 𝗣𝗟𝗨𝗦: 𝗜’𝘃𝗲 𝗲𝘃𝗲𝗻 𝗮𝘁𝘁𝗮𝗰𝗵𝗲𝗱 𝗳𝗿𝗲𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀 𝘁𝗼 𝗴𝗲𝘁 𝘆𝗼𝘂 𝘀𝘁𝗮𝗿𝘁𝗲𝗱. --- Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agent

𝗢𝗽𝗲𝗻𝗔𝗜 𝗷𝘂𝘀𝘁 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝘁𝗵𝗲𝗶𝗿 𝗼𝗳𝗳𝗶𝗰𝗶𝗮𝗹 𝗚𝗣𝗧-4.1 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗴𝘂𝗶𝗱𝗲! It provides a detailed guide on how to steer GPT-4.1 with precision, including examples, tips, and advanced techniques. You can access the full version for free below. ⬇️ 𝗜𝗻 𝘀𝘂𝗺𝗺𝗮𝗿𝘆, 𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗸𝗲𝘆 𝗲𝗹𝗲𝗺𝗲𝗻𝘁𝘀: ➜ Be Clear with Your Instructions: GPT-4.1 is really good at following directions, but only if you're specific. The more clear and direct your prompt, the better the response. ➜ Break Down Complex Tasks: If you're working on something complicated, ask GPT-4.1 to “think step by step.” It helps the model give more accurate and thoughtful answers. ➜ Use Structure: If you need to share a lot of info, use clear structure—like markdown or bullet points. This helps GPT-4.1 understand and organize the info better. ➜ Format Your Prompts with Clear Sections: Structure your prompts for easier comprehension:   - Role and Objective   - Instructions (with subcategories)   - Reasoning Steps   - Output Format   - Examples   - Final instructions ➜ Put Important Instructions at the Start and End: For longer prompts, put your key instructions both at the beginning and the end. This helps the model stay on track. ➜ Guide It with Reminders: If you're designing a workflow or solving a problem, include reminders like “keep going until it’s fully resolved” or “plan carefully before acting.” This keeps the model focused. ➜ Use the Token Window Wisely: GPT-4.1 can handle a huge amount of text, but too much at once can slow it down. Be strategic about how much context you provide. ➜ Balance Internal and External Knowledge: For factual questions, tell GPT-4.1 to either “only use the provided context” or to mix that context with general knowledge. This helps you get the most accurate results. 𝗜𝗻 𝘀𝗵𝗼𝗿𝘁: 𝗧𝗵𝗲 𝗸𝗲𝘆 𝘁𝗼 𝘂𝘀𝗶𝗻𝗴 𝗚𝗣𝗧-4.1 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆 𝗶𝘀 𝗰𝗹𝗲𝗮𝗿, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 𝘁𝗵𝗮𝘁 𝗴𝘂𝗶𝗱𝗲 𝗶𝘁 𝘁𝗼𝘄𝗮𝗿𝗱 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗮𝗻𝘀𝘄𝗲𝗿. 𝗜𝘁’𝘀 𝗮𝗹𝗹 𝗮𝗯𝗼𝘂𝘁 𝗮𝘀𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘄𝗮𝘆! Access it here or download it below: https://lnkd.in/dCm6DeFW

𝗧𝗵𝗶𝘀 𝗶𝘀 𝗵𝗼𝘄 𝗚𝗲𝗻𝗔𝗜 𝗳𝗶𝗻𝗱𝘀 𝗺𝗲𝗮𝗻𝗶𝗻𝗴 𝗶𝗻 𝘂𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝘁𝗲𝘅𝘁. ⬇️ And yes it all starts with vector databases — not magic. This is the mechanism that powers AI Agent memory, RAG and semantic search. And this diagram below? Nails the entire flow — from raw data to relevant answers. Let's break it down (the explanation shows of how a vector database works — using the simple example prompt: “Who am I): ⬇️ 1. 𝗜𝗻𝗽𝘂𝘁: ➜ There are two inputs: Data = the source text (docs, chat history, product descriptions...) and the query = the question or prompt you’re asking. These are processed in exactly the same way — so they can be compared mathematically later. 2. 𝗪𝗼𝗿𝗱 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 ➜ Each word (like “how”, “are”, “you”) is transformed into a list of numbers — a word embedding. These word embeddings capture semantic meaning, so that for example "bank" (money) and "finance" land closer than "bank" (river). This turns raw text into numerical signals. 3. 𝗧𝗲𝘅𝘁 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 ➜ Both data and query go through this stack: - Encoder: Transforms word embeddings based on their context (e.g. transformers like BERT). - Linear Layer: Projects these high-dimensional embeddings into a more compact space. -ReLU Activation: Introduces non-linearity — helping the model focus on important features. The output? A single text embedding that represents the entire sentence or chunk. 4. 𝗠𝗲𝗮𝗻 𝗣𝗼𝗼𝗹𝗶𝗻𝗴 ➜ Now we take the average of all token embeddings — one clean vector per chunk. This is the "semantic fingerprint" of your text. 5. 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 ➜ All document vectors are indexed — meaning they’re structured for fast similarity search. This is where vector databases like FAISS or Pinecone come in. 6. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 (𝗗𝗼𝘁 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 & 𝗔𝗿𝗴𝗺𝗮𝘅) ➜ When you submit a query.: The query is also embedded and pooled into a vector. The system compares your query to all indexed vectors using dot product — a measure of similarity. Argmax finds the closest match — i.e. the most relevant chunk. This is semantic search at work. - Keyword search finds strings. - Vector search finds meaning. 7. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 ➜ All document vectors live in persistent vector storage — always ready for future retrieval and use by the LLM. This is basically the database layer behind: - RAG - Semantic search - Agent memory - Enterprise GenAI apps - etc. 𝗜𝗳 𝘆𝗼𝘂’𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠𝘀 — 𝘁𝗵𝗶𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝘆𝗼𝘂’𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗼𝗻. Kudos to Tom Yeh for this brilliant visualization!

𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗰𝗮𝗿𝗲𝗲𝗿 𝗺𝗶𝘀𝘁𝗮𝗸𝗲 𝗶𝗻 2025? 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗔𝗜 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗮𝗽𝗽𝗹𝘆 𝘁𝗼 𝘆𝗼𝘂. Mastering AI isn’t optional anymore. It’s the difference between leading and being replaced. Regardless of your professional role, it's crucial to have a grasp of the fundamentals. BUT most professionals struggle to break into AI because they lack a structured learning approach. They either drown in theory or jump in without fundamentals. 𝗧𝗵𝗶𝘀 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝘁𝗵𝗮𝘁! 𝗟𝗲𝘁'𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻: 1. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗔𝗜 – Know the difference between ML, Deep Learning, and Generative AI. 2. 𝗠𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 – Probability, statistics, linear algebra. AI is built on math. 3. 𝗞𝗻𝗼𝘄 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 – GPT, Llama, Gemini. Understand how they work, not just how to use them. 4. 𝗕𝘂𝗶𝗹𝗱 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘀𝘁𝗮𝗰𝗸 – Python, Langchain, VectorDB. AI is an engineering discipline. 5. 𝗧𝗿𝗮𝗶𝗻 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 – Data collection, tokenization, evaluation. No black boxes. 6. 𝗕𝘂𝗶𝗹𝗱 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 – Automate workflows, integrate human oversight, build real-world applications. 7. 𝗚𝗲𝗻𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 – GANs, DALL·E, Midjourney. AI isn’t just about chatbots. 8. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝘁𝗼𝗽 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 – Kaggle, DeepLearning.AI, Nvidia. Learn from those driving the field forward. The best part is that most of the stuff is completely free of charge and you just have to invest your time. Is anything missing from your view? --- Kudos to ByteByteGo for this amazing graphic!

𝗜𝗳 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀, 𝘆𝗼𝘂 𝗡𝗘𝗘𝗗 𝗧𝗢 𝗞𝗡𝗢𝗪 𝘁𝗵𝗶𝘀 𝘀𝗶𝘅 𝗱𝗲𝘀𝗶𝗴𝗻 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀! 🛠️ AI Agents are evolving fast, opening up many possibilities for a new paradigm of applications. In today's world, we use LLMs mostly in zero-shot mode, in which a model generates final output token by token without revising its work. With an agent workflow, we can ask the LLM to iterate over certain output many times. There are many tools and possibilities for AI agents today, creating both exciting opportunities and a lot of noise. To cut through the confusion, here’s a framework of six key design patterns you can leverage to build powerful, scalable agentic applications: Let’s break it down: ⬇️ 1. 𝗥𝗲𝗔𝗰𝘁 𝗔𝗴𝗲𝗻𝘁   Thinks, takes action, looks at the result, repeats.   Classic loop: “Should I Google this?” → Does it → Adjusts.   → Used in most AI products today (like basic chat assistants). 2. 𝗖𝗼𝗱𝗲𝗔𝗰𝘁 𝗔𝗴𝗲𝗻𝘁: Runs real code, not just JSON.   So instead of saying “call API X,” it writes and runs a Python script.   → More powerful. Used in research agents and dev assistants. 3. 𝗠𝗼𝗱𝗲𝗿𝗻 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲: Sends tasks to smart APIs (search, cloud, data), and lets them do the heavy lifting.   The agent mostly routes and formats info.   → Think: a smart middleman between you and powerful services. 4. 𝗦𝗲𝗹𝗳-𝗥𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻: Agent checks its own work.   Did it make a mistake? It catches it, critiques it, and tries again.   → Most AI errors happen **because this step is missing.** 5. 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄:  One agent isn’t doing everything.   You have a planner, a researcher, and a writer — all working together.   → Like a mini team of AIs. More accurate. Less chaos. 6. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚:  This is what powers apps like Perplexity.   The agent looks stuff up (retrieval), thinks about it, uses tools, and gives you a smarter answer.   → Works with real-time data, not just model memory. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗰𝗮𝗻 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺-𝘀𝗼𝗹𝘃𝗶𝗻𝗴, 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲, 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝘁𝗼 𝗺𝗼𝗿𝗲 𝘀𝗼𝗽𝗵𝗶𝘀𝘁𝗶𝗰𝗮𝘁𝗲𝗱 𝗮𝗻𝗱 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗔𝗜 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀. 𝗣𝗿𝗼𝗺𝗽𝘁𝘀 𝗮𝗹𝗼𝗻𝗲 𝘄𝗼𝗻’𝘁 𝗴𝗲𝘁 𝘆𝗼𝘂 𝘁𝗼 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻   → 𝗟𝗟𝗠𝘀 𝗮𝗿𝗲 𝘀𝗺𝗮𝗿𝘁 — 𝗯𝘂𝘁 𝘀𝘁𝗮𝘁𝗶𝗰   → 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝘄𝗶𝗻. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗺𝗮𝘁𝘁𝗲𝗿𝘀. [𝗡𝗼𝘁𝗲 𝘁𝗵𝗮𝘁 𝘁𝗵𝗲𝘀𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗰𝗮𝗻 𝗯𝗲 𝘂𝘀𝗲𝗱 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗲𝘅𝗰𝗹𝘂𝘀𝗶𝘃𝗲𝗹𝘆.] 🛠️ These patterns are adapted from the Agentic Design Patterns blog series from Andrew Ng (find more in the comments). Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents. Kudos to Rakesh Gohel for this excellent visualization!

𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗰𝗼𝗺𝗺𝗼𝗻 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝗶𝗻 2025? Given the pace of adoption, one thing is clear: In 12 months, every enterprise workflow will involve AI agents. Not might. Not could. Will. So it’s worth taking a closer look at how this is already playing out. Here are some of the most common and impactful AI agent use cases we’re seeing across industries in 2025. Let's break it down: ⬇️ 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚: Retrieval-Augmented Generation reimagined. These agents don’t just retrieve knowledge — they evaluate sources, reason over them, and produce contextually grounded answers. Used for internal knowledge assistants, intelligent documentation, and enterprise Q&A. Examples: IBM watsonx, Perplexity AI, Glean etc. 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗔𝗴𝗲𝗻𝘁𝘀:   These agents orchestrate tasks across systems. Triggered by APIs, UI actions, or internal events, they can perform multi-step processes without human involvement. Think automated onboarding flows, approvals, or back-office operations. Very easy to build for everybody. Examples: Make.com, Flowise, n8n, Relevance AI etc. 𝗖𝗼𝗱𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝘀: AI-powered development assistants that go beyond code suggestions. These agents can plan, refactor, debug, and even reason across repositories. Ideal for accelerating software engineering teams or bootstrapping prototypes. Examples: Cursor, Roo Code, Windsurf etc. 𝗧𝗼𝗼𝗹-𝗕𝗮𝘀𝗲𝗱 𝗔𝗴𝗲𝗻𝘁𝘀:   Niche, high-utility agents designed to perform well-defined tasks with specific tools — from sending emails to querying internal search engines. These agents are easy to deploy and integrate into targeted workflows. Examples: Breeze, Clay etc. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗨𝘀𝗲 𝗔𝗴𝗲𝗻𝘁𝘀:  The most ambitious — and most misunderstood. These agents don’t just call APIs. They use the UI. Navigating browsers. Typing into forms. Clicking buttons. Agents that act like humans, powered by models like Claude and GPT-4. 𝗩𝗼𝗶𝗰𝗲 𝗔𝗴𝗲𝗻𝘁𝘀: Where GenAI meets the phone line. These agents handle support calls, internal queries, and sales outreach... all with voice. Examples: ElevenLabs, Vapi, and others etc. This isn’t theoretical — it’s already happening. AI agent use cases are rapidly maturing, moving from prototypes to production across industries. And if your strategy still revolves around chatbots, you’re already behind. 𝗧𝗵𝗲 𝗻𝗲𝘅𝘁 12 𝗺𝗼𝗻𝘁𝗵𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝗯𝘆 𝘁𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝗱𝗲𝘀𝗶𝗴𝗻, 𝗰𝗼𝗺𝗯𝗶𝗻𝗲, 𝗮𝗻𝗱 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝘁𝘆𝗽𝗲𝘀 𝗼𝗳 𝗮𝗴𝗲𝗻𝘁𝘀 𝗶𝗻𝘁𝗼 𝗿𝗲𝗮𝗹, 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗯𝘂𝗶𝗹𝘁. Kudos to Rakesh Gohel for this excellent visualization!

𝗢𝗽𝗲𝗻𝗔𝗜 𝗷𝘂𝘀𝘁 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗕𝗜𝗚 — 𝗮𝗻𝗱 𝗯𝗮𝗿𝗲𝗹𝘆 𝗮𝗻𝘆𝗼𝗻𝗲 𝗶𝘀 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁! Yesterday, I spent a few hours diving into the newly launched "𝗢𝗽𝗲𝗻𝗔𝗜 𝗔𝗰𝗮𝗱𝗲𝗺𝘆". And it's an absolute goldmine of FREE AI education, packed with tutorials, live workshops, labs and real-world case studies. Whether you're just starting or already building with GPTs — there’s definitely something here for you. And it’s all 100% FREE and beginner-friendly tracks (no code needed). Here is some stuff to have an eye on: 𝗨𝗽𝗰𝗼𝗺𝗶𝗻𝗴 𝗪𝗲𝗯𝗶𝗻𝗮𝗿𝘀: – Introduction to ChatGPT: https://lnkd.in/e4dgUbWj – AI in Action: Uses for Work, Learning & Life: https://lnkd.in/efXpXY_9 𝗔𝗿𝗰𝗵𝗶𝘃𝗲𝗱 𝗪𝗲𝗯𝗶𝗻𝗮𝗿𝘀: – ChatGPT 101: A Guide to Your Super Assistant: https://lnkd.in/e6RJMcEC – ChatGPT 102: Using AI to Do Your Best Work: https://lnkd.in/eF4iQfFz – Advanced Prompt Engineering: https://lnkd.in/eb9JGYkY 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗮𝘁 𝗪𝗼𝗿𝗸 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: – ChatGPT Search: https://lnkd.in/e8fRSkPT – ChatGPT for Data Analysis: https://lnkd.in/ezssYnGk – Introduction to GPTs: https://lnkd.in/eiUCDF9u 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗼𝗻 𝗖𝗮𝗺𝗽𝘂𝘀 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: – AI for Academic Success: https://lnkd.in/e9hPwRsF   – AI for Career Prep: Resumes & Interviews: https://lnkd.in/ezK62jzQ 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗕𝘂𝗶𝗹𝗱 𝗛𝗼𝘂𝗿𝘀 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻:   – Fine-Tuning: https://lnkd.in/e2iqWD7J – Assistants & Agents: https://lnkd.in/em6FBu2Q Link to the academy: https://lnkd.in/d8GK4sC4 Definitely very interesting to see that OpenAI is now also building their own learning ecosystem. ENJOY!