Hisham Ramzi

🤖 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫'𝐬 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐟𝐨𝐫 𝟐𝟎𝟐𝟓 Artificial Intelligence is not just about chatbots anymore. The future belongs to 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 – autonomous systems powered by 𝐥𝐚𝐫𝐠𝐞 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐦𝐨𝐝𝐞𝐥𝐬 (𝐋𝐋𝐌𝐬) that can plan, make decisions, and complete tasks without step-by-step instructions. 📌 Why 2025 is called the “Year of AI Agents”? Because AI agents are being used everywhere – from healthcare to finance, from customer support to software development. If you are a beginner and want to start learning AI Agents in 2025, here is a 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐫𝐨𝐚𝐝𝐦𝐚𝐩 𝐢𝐧 𝐞𝐚𝐬𝐲 𝐬𝐭𝐞𝐩𝐬 👇 🛠 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫’𝐬 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐭𝐨 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 1️⃣ 𝐒𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Python is the main language for building AI systems. Learn coding basics, loops, functions, and libraries like pandas, numPY, and openAI. 2️⃣ 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐁𝐚𝐬𝐢𝐜𝐬 Know how AI creates text, images, and code. This builds your foundation for how LLMs work. 3️⃣ 𝐋𝐞𝐚𝐫𝐧 𝐋𝐋𝐌 𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬 Understand GPT, Claude, Llama – how they are trained and how they generate human-like responses. 4️⃣ 𝐌𝐚𝐬𝐭𝐞𝐫 𝐏𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 Good prompting = strong AI agents. Learn to guide AI for better answers. 5️⃣ 𝐒𝐭𝐮𝐝𝐲 𝐀𝐠𝐞𝐧𝐭 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 Autonomy, decision-making, memory, reasoning – the DNA of agents. 6️⃣ 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐀𝐠𝐞𝐧𝐭 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬 Single-agent vs multi-agent, tool-using agents, and inter-agent communication. 7️⃣ 𝐋𝐞𝐚𝐫𝐧 𝐀𝐠𝐞𝐧𝐭 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 (𝐂𝐫𝐞𝐰𝐀𝐈 / 𝐏𝐡𝐢𝐝𝐚𝐭𝐚) These frameworks help you build real AI agents quickly. 8️⃣ 𝐁𝐮𝐢𝐥𝐝 𝐒𝐢𝐧𝐠𝐥𝐞-𝐀𝐠𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 Start simple – e.g., a research agent that summarizes reports. 9️⃣ 𝐁𝐮𝐢𝐥𝐝 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 Example: One agent researches, another writes, and a third edits. 🔟 𝐂𝐫𝐞𝐚𝐭𝐞 𝐀𝐠𝐞𝐧𝐭 𝐀𝐩𝐩𝐬 Turn your agents into usable apps for business or personal productivity. 📚 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 1️⃣ 𝐑𝐀𝐆 𝐁𝐚𝐬𝐢𝐜𝐬 (𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧) Learn how to connect agents with documents & databases. 2️⃣ 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐑𝐀𝐆 Combine retrieval with reasoning – the backbone of enterprise AI. 3️⃣ 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 (𝐀𝐮𝐭𝐨𝐆𝐞𝐧 / 𝐋𝐚𝐧𝐠𝐆𝐫𝐚𝐩𝐡) Use them to design complex agent workflows. 4️⃣ 𝐁𝐮𝐢𝐥𝐝 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐀𝐠𝐞𝐧𝐭 𝐀𝐩𝐩𝐬 From customer service bots to financial advisors to project managers. 5️⃣ 𝐋𝐞𝐚𝐫𝐧 𝐀𝐠𝐞𝐧𝐭 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 & 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 Measure accuracy, reliability, performance. 6️⃣ 𝐃𝐞𝐩𝐥𝐨𝐲 𝐀𝐠𝐞𝐧𝐭 𝐀𝐩𝐩𝐬 Launch your AI agents on the cloud for real users. 💡 The future is clear: 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐰𝐢𝐥𝐥 𝐛𝐞𝐜𝐨𝐦𝐞 𝐭𝐡𝐞 𝐧𝐞𝐰 𝐰𝐨𝐫𝐤𝐟𝐨𝐫𝐜𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐞𝐜𝐨𝐧𝐨𝐦𝐲. 🔥 Don’t just learn AI. Learn to build 𝐚𝐠𝐞𝐧𝐭𝐬 – the real game changers.

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🤖 Ever wondered how AI actually works? We keep hearing about ChatGPT, Midjourney, or self-driving cars — but what’s really going on behind the scenes? Let’s simplify it 👇 1️⃣ Data is the fuel AI learns from massive amounts of data — text, images, videos, and even code. The more data it has, the smarter it gets. 2️⃣ Training is the brain workout AI models are trained to recognize patterns. They make mistakes, learn from them, and improve — just like humans do through practice. 3️⃣ Machine Learning = Decision Making It’s the part that helps AI predict or decide. 🧩 Example: Gmail’s spam filter learns what looks like junk mail. 4️⃣ Deep Learning = The real magic Neural networks (inspired by the human brain) power things like ChatGPT’s text generation, face recognition, and voice assistants. 5️⃣ AI in Action 🚀 Once trained, AI can write content, code apps, translate languages, or even drive cars — all by recognizing and applying patterns it has learned. ✨ In short: AI doesn’t “think” like humans — it learns from experience and gets better every day. 🚀 Planning to work remotely in 2025? list of 700+ companies hiring for remote roles with great packages! 👉 Check it out: https://lnkd.in/gkcRfEXk 📄 Need an ATS-friendly resume to get shortlisted? Grab this free, high-converting resume template 👉 https://lnkd.in/gx64i6vE 🚀 2025 is the year of SKILLS, not degrees! 💡 The right course can 10x your career, salary, and opportunities. 🔥 These industry-recognized programs are trusted by top companies worldwide. 🎯 Career-Changing Courses You Must Take in 2025! (You’ll regret missing these!) 🔃 7000+ free courses free access: https://lnkd.in/gedz7JjN ✅ Google AI Essentials 👉 https://lnkd.in/guyNfZ_y ✅ Google Data Analytics Professional Certificate 👉https://lnkd.in/gBfiP64c ✅ Google Introduction to Generative AI 👉 https://lnkd.in/gN2iNvEX ✅ Machine Learning Specialization 👉https://lnkd.in/gTYUNj38 ✅ Google Cybersecurity Professional Certificate 👉https://lnkd.in/g8xSS65z ✅ IBM Data Analyst 👉https://lnkd.in/gedz7JjN ✅ IBM Data Science 👉https://lnkd.in/gRKyhjSp ✅ Microsoft Power BI Data Analyst 👉https://lnkd.in/gMiWy9u4 ✅ Introduction to Generative AI 👉https://lnkd.in/gN2iNvEX ✅ Python Basics for Data Analysis 👉https://lnkd.in/gwSnE8a8 ✅ Web Development with HTML, CSS, JavaScript 👉https://lnkd.in/ghefjWSf ✅ Foundations of Project Management 👉 https://lnkd.in/gT5KrCpz ✅ The Structured Query Language (SQL) 👉https://lnkd.in/gsWZcaUg ✅ Data Analysis with R Programming 👉 https://lnkd.in/gR3g42T8 #ArtificialIntelligence #MachineLearning #DeepLearning #AIExplained #FutureOfTech #Innovation

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🚀 AI Agent Trends to Watch in 2025 AI agents are rapidly evolving, with trends in 2025 focusing on increased autonomy, collaboration, memory, and accessibility. These agents are becoming more specialized, moving from generic tools to microservice-based systems for specific tasks. Furthermore, there's a growing emphasis on human-in-the-loop systems for high-stakes decisions and responsible AI development. ▬▬▬▬▬▬▬▬▬▬▬▬▬ The question for 2025 isn’t “Will you use AI?” — it’s “How will your AI agents work together?” 6 key AI agent trends shaping the next year: 1️⃣ Voice Agents – Natural language interfaces that interact with users in real time, making conversations with AI feel effortless. 2️⃣ Agentic RAG – AI agents combining reasoning with real-time retrieval to generate accurate, context-rich answers. 3️⃣ AI Agent Protocols – Streamlined multi-agent communication, enabling different AI systems to “talk” and work together seamlessly. 4️⃣ DeepResearch Agents – Multi-agent collaboration to produce in-depth, well-cited research faster than any single human or AI could. 5️⃣ Coding Agents – Agents that can build, debug, and test applications 10x faster than traditional development cycles. 6️⃣ CUA (Computer Using Agents) – AI that operates a computer like a human, integrating with third-party tools and performing complex tasks end-to-end. 💡 The big shift? AI is moving from single-use assistants to orchestrated ecosystems of specialized agents — each expert in its own role, but even more powerful when working together. ▬▬▬▬▬▬▬▬▬▬▬▬▬ AI agents are no longer a futuristic concept — they’re rapidly becoming specialized, collaborative, and deeply integrated into how we work. Credit: Rakesh Gohel #AI #AIAgent #Technology #Innovation

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It's now WAY easier to build multi-agentic workflows in n8n: introducing the AI Agent Tool node 👇 - Previously for multi-agent scenarios, you needed to use the Subworkflow tool. This splits your usecase across multiple workflow canvases. There's definitely some benefits to this modularity, especially if one of your sub-agents gets used across different usecases. But it's definitely more cumbersome for building, and not so useful if your sub-agent is usecase specific. - Since [email protected], the new AI Agent Tool node lets a parent AI Agent use an Agent as a tool. Tested and working numerous levels deep without any degradation. So it's very viable right now to have Agents consuming Agents consuming Agents - proper agentic architecture, not just a serial chain of agents. - In my walkthrough video, I show a great example: a child Research Agent uses GPT 4.1-nano - roughly 37x cheaper than the parent agent that is using Claude Sonnet. The cheap model can parse the token-heavy results from perplexity and just send it's parent a summary of that, saving tons on token costs. - While cost control is one benefit, the real win is being able to focus each agent on doing one thing really well (and making it waaay easier to isolate, debug, iterate on specific functionalities of your agentic system). This came up in my demo: Research Agent was hallucinating, that was a problem isolated to that single agent. So I was able to iterate on Research Agent prompts etc without touching the (already working) logic of parent agent. Now imagine that benefit in a production system with 5+ agents. 🎥 catch the full walkthrough on YouTube (link in comments) #AIAgents #NewFeature #NowThatsFlowgramming

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Feeling Left Out of the AI Conversation? -------------------------------------------------------------- A lot of my friends are asking me where to start their AI learning journey, and they feel left out of the current discussions. You’re not alone; that confusion and eagerness is exactly where I started. The simplest and most powerful way to begin is by using the tools themselves: ChatGPT, Gemini, or any other conversational AI. Think of these chat interfaces as your personal, infinitely patient tutor. Simply tell the AI your current knowledge level and what specific skill you want to achieve. Ask it to generate a step-by-step learning approach tailored just for you. As you work through the steps, learn interactively by asking follow-up questions to adjust the depth and direction of your study. This direct, iterative method is the most effective way to cross the AI knowledge gap. Stop feeling left behind—your personal AI learning journey starts with you blocking time to ask your first question.

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While many are still optimizing single-agent loops, the substantive work has moved to the orchestration of heterogeneous multi-agent systems. The diagram shows a foundational workflow, but scaling it reveals the real engineering problems. The primary bottleneck is no longer the reasoning capacity of the core LLM. It's the architectural overhead required for robust inter-agent communication, state synchronization, and fault tolerance. We're moving from building actors to designing systems. The key challenges are not in prompt engineering, but in: 1./ Orchestration Latency & Cost Every meta-level instruction or coordination step from an orchestrator agent introduces a significant tax in both latency and token consumption. The core challenge is designing systems that minimize this overhead, favoring more decentralized execution. 2./ Communication Protocols There is no standardized protocol for agent-to-agent negotiation, task handoff, and conflict resolution. Ad-hoc solutions are brittle. The development of a robust and efficient communication layer is critical for creating resilient systems. 3./ System Observability Debugging a single agent's reasoning is complex. Tracing causality and identifying failure points within a swarm of interacting agents exhibiting emergent behavior is an order of magnitude harder. This is a critical barrier to deploying these systems in production for mission-critical tasks. The objective has shifted. We're past building a single, competent agent. The goal now is architecting for collective intelligence at scale, where the value is an emergent property of the system, not the capability of any single node. The most defensible moat in the next few years won't be the model, it will be the orchestration framework.

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🚀 LLMs are everywhere — but have you ever wondered how they actually get trained under the hood? Everyone’s talking about Large Language Models and GPUs these days. While the focus is often on the AI algorithms themselves, the infrastructure that makes this training possible is equally fascinating (and often overlooked). Let’s break it down 👇 🔹 Why GPUs (not CPUs)? CPUs are great at handling diverse tasks and switching contexts quickly, but they struggle when it comes to massive parallel math operations. GPUs, on the other hand, are designed for exactly that — crunching millions (or billions) of computations in parallel. 🔹 How training jobs are split across GPUs When someone kicks off a training job with, say, a million parameters, the workload isn’t handled by one GPU alone. Instead, software frameworks (like MPI) break the job into smaller chunks. Each GPU takes on a slice of the work, computes its results, and then — here’s the key part — needs to share those results with other GPUs. 🔹 The magic of communication This back-and-forth between GPUs is powered by collective communication libraries and requires lightning-fast data transfer. Ordinary networks aren’t enough. That’s where RDMA (Remote Direct Memory Access) and InfiniBand networks step in, enabling GPUs to talk to each other at high speed with ultra-low latency. ✨ Next time you read about an LLM trained on thousands of GPUs, remember: it’s not just raw compute that powers AI — it’s the orchestra of GPUs, networks, and software playing in harmony. 👉 What is the most overlooked piece of the AI stack in your view?

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