Andreas Horn's Linkedin Analytics

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!

𝗧𝗵𝗶𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 𝗜 𝘄𝗶𝘀𝗵 𝗜 𝗵𝗮𝗱 𝘄𝗵𝗲𝗻 𝗜 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀! ⬇️ Built together with Rakesh Gohel (aka Mr. AI Agent) — and now yours! We broke it down into 7 essential steps to go from zero to scalable, production-ready agents: 1. 𝗣𝗶𝗰𝗸 𝗮𝗻 𝗟𝗟𝗠 ➜ Choose a model that reasons well, supports step-by-step logic, and gives consistent outputs. Tip: Llama, Claude Opus, or Mistral are great for open-weight setups. 2. 𝗕𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗟𝗼𝗴𝗶𝗰 ➜ Should it reflect before answering? Plan or act directly? What if it gets stuck? Start simple with ReAct or Plan–then–Execute. Don’t overcomplicate. 3. 𝗪𝗿𝗶𝘁𝗲 𝗶𝘁𝘀 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 ➜ Define how it should respond, when to use tools, and what formats to reply in. Reusable prompt templates are your friend here — they scale better than hardcoded flows. 4. 𝗔𝗱𝗱 𝗠𝗲𝗺𝗼𝗿𝘆 ➜ LLMs forget. Your agent can’t. Use sliding windows, summaries, or memory frameworks like MemGPT or ZepAI to persist key facts and long-term context. 5. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝗧𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗔𝗣𝗜𝘀 ➜ Let the agent do things: query data, call systems, fetch information. Just be explicit about what tools exist and when to use them. 6. 𝗚𝗶𝘃𝗲 𝗶𝘁 𝗮 𝗝𝗼𝗯 Bad prompt: “Be helpful.” Good prompt: “Summarize customer feedback and suggest improvements.” Narrow scope wins. The tighter the job, the smarter the agent. 7. 𝗦𝗰𝗮𝗹𝗲 𝘁𝗼 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗧𝗲𝗮𝗺𝘀 ➜One gathers data. One interprets. One formats results. You don’t need a super-agent. You need a smart team — built for specific tasks. 𝗢𝗻𝗲 𝗶𝗺𝗮𝗴𝗲. 𝗦𝗲𝘃𝗲𝗻 𝘀𝘁𝗲𝗽𝘀. 𝗜𝗻𝗳𝗶𝗻𝗶𝘁𝗲 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀! From solo agents to orchestration-ready systems — this is how you scale with intent. Image below. Save it. Use it. You can find more info in the comments! (Note: The entire roadmap is not exhaustive and can differ according to different use cases) ♻️ Share this to help your network level up.

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!

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!

𝗛𝗼𝘄 𝗜𝘁 𝗪𝗼𝗿𝗸𝘀: 𝗧𝗵𝗲 𝗥𝗘𝗔𝗟 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗔𝗜 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗔𝗴𝗲𝗻𝘁𝘀, 𝗮𝗻𝗱 𝗠𝗖𝗣. ⬇️ This image illustrates the difference with surprising clarity. Let’s break it down: 1. (𝗔𝗜) 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: 𝗙𝗼𝗹𝗹𝗼𝘄𝘀 𝗰𝗹𝗲𝗮𝗿 𝗶𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 ➜ An AI workflow is like a recipe. It runs in a fixed order: An email arrives → the content is summarized → a task is created → the plan is sent via Slack. It’s linear, predictable, and doesn’t adapt. No decisions. No context-awareness. Just automation. 2. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀: 𝗔𝗰𝗰𝗼𝗺𝗽𝗹𝗶𝘀𝗵 𝗴𝗼𝗮𝗹𝘀 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀𝗹𝘆 ➜ An AI agent doesn’t need step-by-step instructions. You give it a goal — for example, “Plan my day” — and it figures out how to get there. It accesses tools, checks your calendar, moves meetings, finds focus time, and adapts the schedule based on what matters. It makes decisions based on context — not just predefined logic. 3. 𝗠𝗖𝗣: 𝗘𝗻𝗮𝗯𝗹𝗲𝘀 𝗿𝗲𝗮𝗹 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 ➜ The Model Context Protocol (MCP) is the key enabler. It gives the agent secure, real-time access to apps like Calendar, Notion, Slack, and Perplexity. This unlocks cross-app coordination, memory, and adaptive behavior. Not just running commands — but reasoning across systems. 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝘀𝘁𝗲𝗽𝘀 ➜ 𝗔𝗴𝗲𝗻𝘁𝘀 𝗽𝘂𝗿𝘀𝘂𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀 ➜ 𝗔𝗻𝗱 𝗠𝗖𝗣 𝗶𝘀 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝘁𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝘁𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲!

𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝗮𝗻 𝗔𝗜 𝗦𝗧𝗥𝗔𝗧𝗘𝗚𝗬 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆? This is one of the clearest roadmap (Gartner) you’ll ever get to build your own: ⬇️ 1. 𝗔𝗜 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗚𝗼𝗮𝗹 𝗦𝗲𝘁𝘁𝗶𝗻𝗴 (𝗧𝗵𝗲 𝗖𝗼𝗿𝗲): This is your strategic north star — where you define your ambition and guide every downstream decision. • Drivers → Why are you doing this? Clarifies the business/tech forces pushing AI forward. • Vision → Where is this going long-term? Provides inspiration and direction across teams. • Alignment → Is everyone rowing in the same direction? Ensures synergy. • Risks → What could go wrong? Sets the baseline for governance and responsible AI. • Adoption → Who will actually use it? Anticipates friction and enables change management. 📍 This is the master blueprint — Without this, you’re just building disconnected POCs. No clear target = no impact. 2. 𝗔𝗹𝗶𝗴𝗻𝗲𝗱 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 (𝗠𝗮𝗸𝗲 𝗜𝘁 𝗙𝗶𝘁 𝗬𝗼𝘂𝗿 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀): This is where your AI ambition meets the reality of your broader enterprise. • Business Strategy → AI must serve the core business goals — not exist as a side project. • IT Strategy → Ensures your infrastructure can support scalable AI. • R&D Strategy → Aligns innovation with AI capabilities and funding priorities. • D&A Strategy → Without data strategy, no AI strategy will scale. • (...) Strategy → ... 📍 Connect AI to the real levers of power in your organization — so it doesn’t get siloed or shut down. 3. 𝗔𝗜 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 (𝗠𝗮𝗸𝗲 𝗜𝘁 𝗥𝗲𝗮𝗹): Once you know what you want to do, this defines how you’ll deliver it at scale. • Governance → Sets up ethical, legal, and operational oversight from day one. • Data → Builds the pipelines and quality foundations for smart AI. • Engineering → Equips you with the technical backbone for deployment. • Technology → Selects the right tools, platforms, and architecture. • Literacy → Ensures the workforce can actually work with AI. 📍 This is your AI engine room — without it, strategy stays theoretical. 4. 𝗔𝗜 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 (𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝘁𝗵𝗲 𝗩𝗮𝗹𝘂𝗲): Now it’s time to build — but with structure and intent. • Ideation/Prioritization** → Surfaces the best use cases, aligned with strategy. • Use Cases → Translates goals into concrete applications and MVPs. • Change Management → Drives real adoption beyond pilots. • Value/Cost Management → Measures success and ensures scalability. 📍 This is where value is realized — where strategy finally touches the customer and the business. 𝗬𝗼𝘂𝗿 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝘀𝗵𝗼𝘂𝗹𝗱 𝘄𝗼𝗿𝗸 𝗹𝗶𝗸𝗲 𝘆𝗼𝘂𝗿 𝘁𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸: 𝗙𝘂𝗹𝗹𝘆 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱, 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗮𝗻𝗱 𝗯𝘂𝗶𝗹𝘁 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲! 𝗙𝗼𝘂𝗻𝗱 𝘁𝗵𝗶𝘀 𝘂𝘀𝗲𝗳𝘂𝗹? Here you can find edge-level insights on AI agents, automation workflows, and enterprise-ready AI stacks. Fully free! Subscribe here: https://lnkd.in/dbf74Y9E

𝗪𝗲 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗺𝗼𝘃𝗲 𝗯𝗲𝘆𝗼𝗻𝗱 𝗰𝗮𝗹𝗹𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗮𝗻 “𝗟𝗟𝗠.” ⬇️ In 2025, the AI landscape has evolved far beyond just large language models. Knowing which model to use for your specific use case — and how — is becoming a strategic advantage. Let’s break down the 8 most important model types and what they’re actually built to do: ⬇️ 1. 𝗟𝗟𝗠 – 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 → Your ChatGPT-style model. Handles text, predicts the next token, and powers 90% of GenAI hype. 🛠 Use case: content, code, convos. 2. 𝗟𝗖𝗠 – 𝗟𝗮𝘁𝗲𝗻𝘁 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗠𝗼𝗱𝗲𝗹 → Lightweight, diffusion-style models. Fast, quantized, and efficient — perfect for real-time or edge deployment. 🛠 Use case: image generation, optimized inference. 3. 𝗟𝗔𝗠 – 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗔𝗰𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹 → Where LLM meets planning. Adds memory, task breakdown, and intent recognition. 🛠 Use case: AI agents, tool use, step-by-step execution. 4. 𝗠𝗼𝗘 – 𝗠𝗶𝘅𝘁𝘂𝗿𝗲 𝗼𝗳 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 → One model, many minds. Routes input to the right “expert” model slice — dynamic, scalable, efficient. 🛠 Use case: high-performance model serving at low compute cost. 5. 𝗩𝗟𝗠 – 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 → Multimodal beast. Combines image + text understanding via shared embeddings. 🛠 Use case: Gemini, GPT-4o, search, robotics, assistive tech. 6. 𝗦𝗟𝗠 – 𝗦𝗺𝗮𝗹𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 → Tiny but mighty. Designed for edge use, fast inference, low latency, efficient memory. 🛠 Use case: on-device AI, chatbots, privacy-first GenAI. 7. 𝗠𝗟𝗠 – 𝗠𝗮𝘀𝗸𝗲𝗱 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 → The OG foundation model. Predicts masked tokens using bidirectional context. 🛠 Use case: search, classification, embeddings, pretraining. 8. 𝗦𝗔𝗠 – 𝗦𝗲𝗴𝗺𝗲𝗻𝘁 𝗔𝗻𝘆𝘁𝗵𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 → Vision model for pixel-level understanding. Highlights, segments, and understands *everything* in an image. 🛠 Use case: medical imaging, AR, robotics, visual agents. Understanding these distinctions is essential for selecting the right model architecture for specific applications, enabling more effective, scalable, and contextually appropriate AI interactions. While these are some of the most prominent specialized AI models, there are many more emerging across language, vision, speech, and robotics — each optimized for specific tasks and domains. LLM, VLM, MoE, SLM, LCM → GenAI LAM, MLM, SAM → Not classic GenAI, but critical building blocks for AI agents, reasoning, and multimodal systems 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E Kudos for the graphic goes to Generative AI !

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝘀 𝗰𝗼𝗺𝗶𝗻𝗴 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝘀𝘆𝘀𝘁𝗲𝗺 — 𝗮𝗻𝗱 𝗺𝗼𝘀𝘁 𝗯𝗮𝗻𝗸𝘀 𝗮𝗿𝗲𝗻’𝘁 𝗿𝗲𝗮𝗱𝘆! ⬇️ At IBM, we just released a new report unpacking what happens when autonomous agents hit regulated industries (interesting read also for people from other industries) — and why financial institutions must rethink governance, architecture, and control from the ground up. Agentic AI isn’t some far-off vision: It’s already planning, executing, and escalating decisions — across onboarding, fraud detection, loan approvals, compliance, and more. From the report, these 6 insights stand out:️ ⬇️ 1. 𝗟𝗲𝗴𝗮𝗰𝘆 𝗰𝗼𝗻𝘁𝗿𝗼𝗹𝘀 𝘄𝗼𝗻’𝘁 𝘀𝘂𝗿𝘃𝗶𝘃𝗲 𝘁𝗵𝗲 𝘀𝗵𝗶𝗳𝘁: ➜ When agents start executing KYC flows, approving loans, or detecting fraud — real-time governance becomes non-negotiable. The paper outlines 30+ control layers and guardrails needed before you go live. 2. 𝗠𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗲 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗿𝗶𝘀𝗸. ➜ Principal agents orchestrate task agents and service agents in complex chains. One misaligned goal? You get drift, bias, or worse — strategic deception. 3. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗶𝘀 𝗽𝗼𝘄𝗲𝗿 — 𝗮𝗻𝗱 𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆. → Unlike stateless systems, agentic AI remembers. It accumulates knowledge and can act on outdated or misaligned goals unless strict reset, audit, and expiration policies are in place, because financial institutions must adhere to strict data retention and usage policies 4. 𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗿𝗶𝘀𝗸𝘀? 𝗗𝗲𝗰𝗲𝗽𝘁𝗶𝗼𝗻, 𝗯𝗶𝗮𝘀, 𝗮𝗻𝗱 𝗱𝗮𝘁𝗮 𝗺𝗶𝘀𝘂𝘀𝗲. → The report outlines chilling examples: agents hiding intentions, using personas that amplify bias, or misusing PII without oversight. The solution? Real-time monitoring, not post-hoc patching. 5. 𝗧𝗵𝗲 𝗮𝗴𝗲𝗻𝘁 𝗲𝗰𝗼𝗻𝗼𝗺𝘆 𝗻𝗲𝗲𝗱𝘀 𝗿𝗲𝗴𝗶𝘀𝘁𝗿𝗶𝗲𝘀, 𝗻𝗼𝘁 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀. → Every agent must be tracked like a microservice: with metadata, access rights, capabilities, and audit logs. Think of it as DevOps meets AI Governance. 6. 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝗶𝘀 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻. 𝗜𝘁'𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲. → “Compliance by design” isn’t a nice-to-have — it’s the only way to deploy agents that regulators, auditors, and boards won’t shut down on day one. 𝗧𝗵𝗲 𝗳𝘂𝗹𝗹 𝗽𝗮𝗽𝗲𝗿 𝗼𝗳𝗳𝗲𝗿𝘀 𝗮 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗯𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝗳𝗼𝗿 𝘁𝗵𝗼𝘀𝗲 𝘀𝗲𝗿𝗶𝗼𝘂𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗔𝗜 𝗶𝗻 𝗳𝗶𝗻𝗮𝗻𝗰𝗲. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗲 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗶𝗻 𝗺𝗼𝘁𝗶𝗼𝗻 — 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗰𝗵𝗼𝗶𝗰𝗲𝘀 𝘄𝗲 𝗺𝗮𝗸𝗲 𝗻𝗼𝘄 𝘄𝗶𝗹𝗹 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝘄𝗲 𝗹𝗲𝗮𝗱 𝘁𝗵𝗲𝗶𝗿 𝗱𝗲𝘀𝗶𝗴𝗻 𝗼𝗿 𝗿𝗲𝗮𝗰𝘁 𝘁𝗼 𝘁𝗵𝗲𝗶𝗿 𝗰𝗼𝗻𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀. Full report and commentary in the comments. Enjoy!

𝗢𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗠𝗢𝗦𝗧 𝗱𝗶𝘀𝗰𝘂𝘀𝘀𝗲𝗱 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: 𝗛𝗼𝘄 𝘁𝗼 𝗽𝗶𝗰𝗸 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗟𝗟𝗠 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲? The LLM landscape is booming and choosing the right LLM is now a business decision, not just a tech choice. One-size-fits-all? Forget it. Nearly all enterprises today rely on different models for different use cases and/or industry-specific fine-tuned models. There’s no universal “best” model — only the best fit for a given task. The latest LLM landscape (see below) shows how models stack up in capability (MMLU score), parameter size and accessibility — and the differences REALLY matter. 𝗟𝗲𝘁'𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻: ⬇️ 1️⃣ 𝗚𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘀𝘁 𝘃𝘀. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁: - Need a broad, powerful AI? GPT-4, Claude Opus, Gemini 1.5 Pro — great for general reasoning and diverse applications. - Need domain expertise? E.g. IBM Granite or Mistral models (Lightweight & Fast) can be an excellent choice — tailored for specific industries. 2️⃣ 𝗕𝗶𝗴 𝘃𝘀. 𝗦𝗹𝗶𝗺: - Powerful, large models (GPT-4, Claude Opus, Gemini 1.5 Pro) = great reasoning, but expensive and slow. - Slim, efficient models (Mistral 7B, LLaMA 3,IBM Granite, RWWK models) = faster, cheaper, easier to fine-tune. Perfect for on-device, edge AI, or latency-sensitive applications. 3️⃣ 𝗢𝗽𝗲𝗻 𝘃𝘀. 𝗖𝗹𝗼𝘀𝗲𝗱 - Need full control? Open-source models (LLaMA 3, Mistral, Llama) give you transparency and customization. - Want cutting-edge performance? Closed models (GPT-4, Gemini, Claude) still lead in general intelligence. 𝗧𝗵𝗲 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆? There is no "best" model — only the best one for your use case, but it's key to understand the differences to make an informed decision: - Running AI in production? Go slim, go fast. - Need state-of-the-art reasoning? Go big, go deep. - Building industry-specific AI? Go specialized and save some money with SLMs. I love seeing how the AI and LLM stack is evolving, offering multiple directions depending on your specific use case. --- ➡️ 𝗜𝗳 𝘆𝗼𝘂 𝗳𝗼𝘂𝗻𝗱 𝘁𝗵𝗶𝘀 𝘂𝘀𝗲𝗳𝘂𝗹, 𝘆𝗼𝘂’𝗹𝗹 𝗹𝗼𝘃𝗲 𝗺𝘆 𝗻𝗲𝘄 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗪𝗲𝗲𝗸𝗹𝘆 𝗱𝗿𝗼𝗽𝘀 𝗼𝗻 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁’𝘀 𝗻𝗲𝘅𝘁. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲: https://lnkd.in/dWMsApuD

𝗧𝗵𝗶𝘀 Stanford University 𝘄𝗲𝗯𝗶𝗻𝗮𝗿 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝘀 𝗽𝗲𝗿𝗳𝗲𝗰𝘁𝗹𝘆 𝗵𝗼𝘄 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘄𝗼𝗿𝗸𝘀 𝗶𝗻 𝗼𝗻𝗲 𝗵𝗼𝘂𝗿. ⬇️ A very short, but on point explanation of the core building blocks of AI Agents. Best of all, it's completely 𝗙𝗥𝗘𝗘 to access! 𝗞𝗲𝘆 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝘀: ⬇️ [00:46] How Language Models actually work [07:00] Prompt engineering tactics that matter [21:07] Why LMs fail — and where they break [23:26] Retrieval-Augmented Generation (RAG) as a fix [26:48] Tool use & function calling [29:22] Agentic behavior: action-taking LMs [39:19] Planning, reflection, multi-agent workflows [38:15] Real-world use cases: software dev, task automation If you’re investing an hour in AI today — make it this one. Watch it. Take notes and get ahead. Webinar: https://lnkd.in/dkdCPBUx

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

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

𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗰𝗵𝗲𝗰𝗸: 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗡𝗢𝗧 𝗷𝘂𝘀𝘁 𝗮 𝗳𝗮𝗻𝗰𝘆 𝗨𝗜 𝗼𝘃𝗲𝗿 𝗖𝗵𝗮𝘁𝗚𝗣𝗧. 𝗧𝗵𝗲𝘆 𝗮𝗿𝗲 𝗱𝗲𝗲𝗽𝗹𝘆 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝘀𝘆𝘀𝘁𝗲𝗺𝘀! ⬇️ 𝘌𝘷𝘦𝘳𝘺𝘰𝘯𝘦’𝘴 𝘵𝘢𝘭𝘬𝘪𝘯𝘨 𝘢𝘣𝘰𝘶𝘵 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴. - "𝘈𝘨𝘦𝘯𝘵𝘴 𝘸𝘪𝘭𝘭 𝘳𝘦𝘱𝘭𝘢𝘤𝘦 𝘫𝘰𝘣𝘴." - "𝘈𝘨𝘦𝘯𝘵𝘴 𝘸𝘪𝘭𝘭 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘦 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨." - "𝘈𝘨𝘦𝘯𝘵𝘴 𝘢𝘳𝘦 𝘵𝘩𝘦 𝘧𝘶𝘵𝘶𝘳𝘦." However, the reality is far more complex. AI agents are not just chat interfaces or simple API calls. These are full-stack engineering beasts that require integration and coordination across multiple layers of technology. The user may see it as simple, but under the hood ("iceberg") it's anything but simple and complex infrastructure. 𝗟𝗲𝘁'𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻: ⬇️ 1️⃣ 𝗙𝗿𝗼𝗻𝘁-𝗲𝗻𝗱 – The user interface, but that’s just the surface. 2️⃣ 𝗠𝗲𝗺𝗼𝗿𝘆 – Managing short-term and long-term context. 3️⃣ 𝗔𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻 – Identity verification, security, and access control. 4️⃣ 𝗧𝗼𝗼𝗹𝘀 – External plugins, search capabilities, integrations. 5️⃣ 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 – Monitoring, logging, and performance tracking. 6️⃣ 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 – Multi-agent coordination, execution, automation. 7️⃣ 𝗠𝗼𝗱𝗲𝗹 𝗥𝗼𝘂𝘁𝗶𝗻𝗴 – Directing queries to the right AI models. 8️⃣ 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝘀 – The LLMs that power the agent’s reasoning. 9️⃣ 𝗘𝗧𝗟 (𝗘𝘅𝘁𝗿𝗮𝗰𝘁, 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺, 𝗟𝗼𝗮𝗱) – Data ingestion and processing pipelines. 🔟 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 – Vector stores and structured storage for knowledge retention. 1️⃣1️⃣ 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲/𝗕𝗮𝘀𝗲 – Compute environments and cloud execution. 1️⃣2️⃣ 𝗖𝗣𝗨/𝗚𝗣𝗨 𝗣𝗿𝗼𝘃𝗶𝗱𝗲𝗿𝘀 – The backbone of AI model execution. In summary, AI agents aren’t just "smart chatbots" — they’re full-stack AI systems requiring seamless orchestration across multiple layers. In addition, there are many companies developing similar technologies right now, fueled by hype and funding. I believe that the market won’t stay this fragmented — consolidation is inevitable (I believe if you come back to this post in 3 years 70% of the companies below will basically not exist anymore). 𝗧𝗵𝗲 𝘄𝗶𝗻𝗻𝗲𝗿𝘀? 𝗧𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝗯𝗿𝗶𝗱𝗴𝗲 𝗔𝗜 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗿𝗲𝗮𝗹 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘃𝗮𝗹𝘂𝗲 𝗯𝘆 𝗺𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗮𝗻𝗱 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝘀𝗲𝗮𝗺𝗹𝗲𝘀𝘀 𝗨𝗫 𝘀𝗶𝗺𝗽𝗹𝗶𝗰𝗶𝘁𝘆 𝗳𝗼𝗿 𝘂𝘀𝗲𝗿𝘀. Kudos to Rakesh Gohel for this excellent visualization!

𝗔𝘁 𝗜/𝗢 2025, Google 𝘀𝗵𝗼𝘄𝗲𝗱 𝘂𝘀 𝘄𝗵𝗮𝘁 𝗔𝗜-𝗳𝗶𝗿𝘀𝘁 𝗥𝗘𝗔𝗟𝗟𝗬 𝗺𝗲𝗮𝗻𝘀. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗚𝗼𝗼𝗴𝗹𝗲 𝗮𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗱: ⬇️ The company's flagship developer event Google I/O 2025 was held last night in Mountain View, California. 𝗧𝗟𝗗𝗥: Google is turning Gemini into the AI operating system for everything — with agents now embedded across Search, Chrome, Workspace, Android, and more. If you don’t have time for the full event, here’s a curated 𝘀𝘂𝗽𝗲𝗿𝗰𝘂𝘁 of the highlights that really matter. 𝗞𝗲𝘆 𝗺𝗼𝗺𝗲𝗻𝘁𝘀 𝗳𝗿𝗼𝗺 𝗚𝗼𝗼𝗴𝗹𝗲 𝗜/𝗢 𝟮𝟬𝟮𝟱: 0:00 𝗜𝗻𝘁𝗿𝗼 – AI-native from the ground up 0:11 𝗚𝗲𝗺𝗶𝗻𝗶 𝗽𝗹𝗮𝘆𝘀 𝗮 𝗣𝗼𝗸𝗲𝗺𝗼𝗻 𝗴𝗮𝗺𝗲 — memory, reasoning, and code 0:30 𝗚𝗼𝗼𝗴𝗹𝗲 𝗕𝗲𝗮𝗺 – Real-time 3D video chat with AI 1:08 𝗚𝗼𝗼𝗴𝗹𝗲 𝗠𝗲𝗲𝘁 – Speech-to-speech translation, live 1:27 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗠𝗮𝗿𝗶𝗻𝗲𝗿 – AI agents that book, plan, filter, decide 2:07 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 – Gemini gets memory and task awareness 2:40 𝗚𝗲𝗺𝗶𝗻𝗶 𝟮.𝟱 𝗣𝗿𝗼 + 𝗙𝗹𝗮𝘀𝗵 – New SOTA models, LMArena leader 4:57 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗔𝘀𝘁𝗿𝗮 – Multimodal, fast-response agent that sees and hears 5:32 𝗔𝗜 𝗠𝗼𝗱𝗲 – Overlay for restaurants, bookings, prices, events 7:10 𝗦𝗵𝗼𝗽𝗽𝗶𝗻𝗴 – Track, compare, and auto-buy with Google Pay 8:34 𝗚𝗲𝗺𝗶𝗻𝗶 𝗟𝗶𝘃𝗲 – Screen sharing + live AI guidance 8:59 𝗗𝗲𝗲𝗽 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗴𝗲𝗻𝘁 – Upload files, get insights 9:12 𝗖𝗮𝗻𝘃𝗮𝘀 – Live, collaborative AI whiteboard 9:31 𝗚𝗲𝗺𝗶𝗻𝗶 𝗶𝗻 𝗖𝗵𝗿𝗼𝗺𝗲 – AI understands and acts on any webpage 9:51 𝗜𝗺𝗮𝗴𝗲𝗻 𝟰 – Next-gen image generation 10:23 𝗩𝗲𝗼 𝟯 – Ultra-realistic video model 11:01 𝗟𝘆𝗿𝗶𝗮 𝟮 – AI-powered music composition 11:56 𝗙𝗹𝗼𝘄𝘀 – Multimodal, promptable AI video creation 12:39 𝗔𝗻𝗱𝗿𝗼𝗶𝗱 𝗫𝗥 – AI-first spatial computing 12:57 𝗦𝗮𝗺𝘀𝘂𝗻𝗴 𝗠𝗼𝗼𝗵𝗮𝗻 – Google’s XR headset revealed 13:16 𝗟𝗶𝘃𝗲 𝗴𝗹𝗮𝘀𝘀𝗲𝘀 𝗱𝗲𝗺𝗼 – Gemini + XR = real-time AI overlay Super insightful and forward-looking: Google’s AI strategy just went full stack. Even if some of these projects don’t make it past the prototype stage, the direction is obvious: AI is being integrated into everything. LLMs — Gemini, in this case — are rapidly becoming the new operating system and everything will be powered by AI Agents across all products. Full keynote: https://lnkd.in/dPFFtyZ9 Supercut: https://lnkd.in/d-eBNGjw Enjoy watching!

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!

99% 𝗼𝗳 𝗽𝗲𝗼𝗽𝗹𝗲 𝗴𝗲𝘁 𝘁𝗵𝗶𝘀 𝘄𝗿𝗼𝗻𝗴: 𝗧𝗵𝗲𝘆 𝘂𝘀𝗲 𝘁𝗵𝗲 𝘁𝗲𝗿𝗺𝘀 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗮𝗻𝗱 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝗻𝘁𝗲𝗿𝗰𝗵𝗮𝗻𝗴𝗲𝗮𝗯𝗹𝘆 — 𝗯𝘂𝘁 𝘁𝗵𝗲𝘆 𝗱𝗲𝘀𝗰𝗿𝗶𝗯𝗲 𝘁𝘄𝗼 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀! ⬇️ Let’s clarify it once and for all: ⬇️ 1. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀: 𝗧𝗼𝗼𝗹𝘀 𝘄𝗶𝘁𝗵 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆, 𝗪𝗶𝘁𝗵𝗶𝗻 𝗟𝗶𝗺𝗶𝘁𝘀 ➜ AI agents are modular, goal-directed systems that operate within clearly defined boundaries. They’re built to: * Use tools (APIs, browsers, databases) * Execute specific, task-oriented workflows * React to prompts or real-time inputs * Plan short sequences and return actionable outputs 𝘛𝘩𝘦𝘺’𝘳𝘦 𝘦𝘹𝘤𝘦𝘭𝘭𝘦𝘯𝘵 𝘧𝘰𝘳 𝘵𝘢𝘳𝘨𝘦𝘵𝘦𝘥 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯, 𝘭𝘪𝘬𝘦: 𝘊𝘶𝘴𝘵𝘰𝘮𝘦𝘳 𝘴𝘶𝘱𝘱𝘰𝘳𝘵 𝘣𝘰𝘵𝘴, 𝘐𝘯𝘵𝘦𝘳𝘯𝘢𝘭 𝘬𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘴𝘦𝘢𝘳𝘤𝘩, 𝘌𝘮𝘢𝘪𝘭 𝘵𝘳𝘪𝘢𝘨𝘦, 𝘔𝘦𝘦𝘵𝘪𝘯𝘨 𝘴𝘤𝘩𝘦𝘥𝘶𝘭𝘪𝘯𝘨, 𝘊𝘰𝘥𝘦 𝘴𝘶𝘨𝘨𝘦𝘴𝘵𝘪𝘰𝘯𝘴 But even the most advanced are limited by scope. They don’t initiate. They don’t collaborate. They execute what we ask! 2. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜: 𝗔 𝗦𝘆𝘀𝘁𝗲𝗺 𝗼𝗳 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 ➜ Agentic AI is an architectural leap. It’s not just one smarter agent — it’s multiple specialized agents working together toward shared goals. These systems exhibit: * Multi-agent collaboration * Goal decomposition and role assignment * Inter-agent communication via memory or messaging * Persistent context across time and tasks * Recursive planning and error recovery * Distributed orchestration and adaptive feedback Agentic AI systems don’t just follow instructions. They coordinate. They adapt. They manage complexity. 𝘌𝘹𝘢𝘮𝘱𝘭𝘦𝘴 𝘪𝘯𝘤𝘭𝘶𝘥𝘦: 𝘳𝘦𝘴𝘦𝘢𝘳𝘤𝘩 𝘵𝘦𝘢𝘮𝘴 𝘱𝘰𝘸𝘦𝘳𝘦𝘥 𝘣𝘺 𝘢𝘨𝘦𝘯𝘵𝘴, 𝘴𝘮𝘢𝘳𝘵 𝘩𝘰𝘮𝘦 𝘦𝘤𝘰𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘰𝘱𝘵𝘪𝘮𝘪𝘻𝘪𝘯𝘨 𝘦𝘯𝘦𝘳𝘨𝘺/𝘴𝘦𝘤𝘶𝘳𝘪𝘵𝘺, 𝘴𝘸𝘢𝘳𝘮𝘴 𝘰𝘧 𝘳𝘰𝘣𝘰𝘵𝘴 𝘪𝘯 𝘭𝘰𝘨𝘪𝘴𝘵𝘪𝘤𝘴 𝘰𝘳 𝘢𝘨𝘳𝘪𝘤𝘶𝘭𝘵𝘶𝘳𝘦 𝘮𝘢𝘯𝘢𝘨𝘪𝘯𝘨 𝘳𝘦𝘢𝘭-𝘵𝘪𝘮𝘦 𝘶𝘯𝘤𝘦𝘳𝘵𝘢𝘪𝘯𝘵𝘺 𝗧𝗵𝗲 𝗖𝗼𝗿𝗲 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲? AI Agents = autonomous tools for single-task execution Agentic AI = orchestrated ecosystems for workflow-level intelligence 𝗡𝗼𝘄 𝗹𝗼𝗼𝗸 𝗮𝘁 𝘁𝗵𝗲 𝗽𝗶𝗰𝘁𝘂𝗿𝗲: ⬇️ 𝗢𝗻 𝘁𝗵𝗲 𝗹𝗲𝗳𝘁: a smart thermostat, which can be an AI Agent. It keeps your room at 21°C. Maybe it learns your schedule. But it’s working alone. 𝗢𝗻 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁: Agentic AI. A full smart home ecosystem — weather-aware, energy-optimized, schedule-sensitive. Agents talk to each other. They share data. They make coordinated decisions to optimize your comfort, cost, and security in real time. That’s the shift = From pure task automation to goal-driven orchestration. From single-agent logic to collaborative intelligence. This is what’s coming = This is Agentic AI. And if we confuse “agent” with “agentic,” we risk underbuilding for what AI is truly capable of. The Cornell University paper in the comments on this topic is excellent! ⬇️

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!

𝗧𝗵𝗶𝘀 𝗶𝘀 𝗵𝗮𝗻𝗱𝘀-𝗱𝗼𝘄𝗻 𝘁𝗵𝗲 𝗕𝗘𝗦𝗧 𝗮𝗻𝗱 𝗦𝗜𝗠𝗣𝗟𝗘𝗦𝗧 𝗶𝗹𝗹𝘂𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗔𝗜 𝘆𝗼𝘂'𝗹𝗹 𝗲𝘃𝗲𝗿 𝘀𝗲𝗲! ⬇️ A lot of companies picture AI as a straightforward process: Feed in data, sprinkle some AI, and BOOM → value appears! If only reality were that simple... 😅 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗥𝗲𝗮𝗹 𝗦𝘁𝗼𝗿𝘆 𝗼𝗳 𝗔𝗜: What actually happens behind the scenes is a complex journey that involves: 1️⃣ Data Sourcing, Cleaning, and Feature Engineering 2️⃣ Data Engineering and Modeling 3️⃣ Training, Evaluating, and Tuning Models 4️⃣ Operationalizing AI to ensure it delivers real-world value And let’s not forget the challenges: 𝗟𝗲𝗴𝗮𝗹, 𝗘𝘁𝗵𝗶𝗰𝗮𝗹, 𝗕𝗶𝗮𝘀, 𝗮𝗻𝗱 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀 that need to be managed every step of the way! AI isn't a magic wand; it's a marathon of meticulous steps! Yes, there's tremendous value in AI. No, it's not as simple as using ChatGPT. #ai #magic #datascience