Mastering LLM (Large Language Model)

Discover Crawl4AI - Open-source Web Crawler for LLMs! We are thrilled to announce Crawl4AI, an open-source Python library designed to revolutionize web crawling and data extraction for AI and large language models (LLMs)! 🚀 𝗪𝗵𝘆 𝗖𝗿𝗮𝘄𝗹4𝗔𝗜? Crawl4AI simplifies the process of gathering clean, structured data from the web, making it a powerful tool for developers and researchers working with LLMs and Generative AI. 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • High-performance asynchronous architecture. • LLM-friendly output formats: JSON, HTML, Markdown. • Supports multi-browser crawling (Chromium, Firefox, WebKit). • Advanced extraction strategies: cosine clustering, LLM-driven methods, CSS selectors. • Flexible chunking: topic-based, regex, sentence. • Media extraction (images, audio, video) and delayed content handling. • Seamless session management for complex crawls. • Dockerized setup and scalable architecture for cloud deployment. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀: • 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗗𝗮𝘁𝗮 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻: With semantic matching and fast schema-based extraction. • 𝗠𝗮𝗿𝗸𝗱𝗼𝘄𝗻 & 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Generates clean Markdown with heuristic-based noise removal. • 𝗕𝗿𝗼𝘄𝘀𝗲𝗿 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Managed/remote browser control, dynamic crawling with screenshots & metadata retrieval. • 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 & 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Ready-to-deploy configurations, API server, and robust error handling. Whether you're building intelligent web agents or need high-quality data for your AI models, Crawl4AI has you covered. #llm #llms #AI #crawl4ai

Agentic RAG is a future of RAG but its only useful when you understand when to use it. Understand more about AgenticRAG with our course here with 6 real-time case studies - https://lnkd.in/d3_Cs5Kw Image credits: Avi Chawla

In our latest Coffee Break Series, we look at Anthropic's new blog on building effective agents and workflows for large language models (LLMs). 𝗔𝗴𝗲𝗻𝘁𝘀 𝘃𝘀 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 - 𝗔𝗴𝗲𝗻𝘁𝘀: Systems where LLMs dynamically control their processes, ideal for open-ended problems requiring multiple steps and decision-making autonomy. - 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: Predefined systems with structured paths, ensuring precision and clarity for decomposable tasks. 𝗖𝗼𝗿𝗲 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗘𝘅𝗽𝗹𝗼𝗿𝗲𝗱: 1. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗖𝗵𝗮𝗶𝗻𝗶𝗻𝗴: Break down tasks into sequential steps for enhanced accuracy. 2. 𝗥𝗼𝘂𝘁𝗶𝗻𝗴: Direct diverse inputs to specialized tasks for better performance. 3. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Accelerate tasks and ensure robustness by leveraging multiple perspectives. 4. 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿-𝗪𝗼𝗿𝗸𝗲𝗿𝘀: Dynamic task delegation for complex and unpredictable subtasks. 5. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗼𝗿-𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗿: Feedback loops for iterative improvements in performance. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗕𝗹𝗼𝗰𝗸𝘀 𝗼𝗳 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 We explore how augmentations like retrieval, tools, and memory enhance LLMs, emphasizing tailoring solutions to specific use cases and designing intuitive interfaces. 𝗧𝗵𝗿𝗲𝗲 𝗞𝗲𝘆 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝗳𝗼𝗿 𝗔𝗴𝗲𝗻𝘁𝘀: 1. Maintain simplicity in design. 2. Ensure transparency in planning steps. 3. Craft robust agent-computer interfaces with comprehensive testing and documentation. Bonus for AI Professionals: Interested in sharpening your skills? Check out our LLM Interview Course and learn about the future of AgenticRAG with LlamaIndex. Links in the comments. #AI #LLM #Agents #Workflows #Innovation #Anthropic #CoffeeBreakSeries

2 days to the new year! It’s time to spotlight 𝗟𝗟𝗠 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 & 𝗔𝗻𝘀𝘄𝗲𝗿𝘀, the most transformative course of the year for aspiring GenAI professionals! 🎉 In 2024, this comprehensive series empowered learners to tackle real interview questions from 𝘁𝗼𝗽 𝘁𝗲𝗰𝗵 𝗴𝗶𝗮𝗻𝘁𝘀 like 𝗚𝗼𝗼𝗴𝗹𝗲, 𝗡𝘃𝗶𝗱𝗶𝗮, 𝗢𝗽𝗲𝗻𝗔𝗜, 𝗮𝗻𝗱 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁. Covering 14 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗰𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗲𝘀—from prompt engineering to fine-tuning and agentic systems—it prepared learners to not just crack interviews but truly master 𝗚𝗲𝗻𝗔𝗜 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀. From securing jobs with 150% 𝘀𝗮𝗹𝗮𝗿𝘆 𝗵𝗶𝗸𝗲𝘀 to landing roles at 𝗱𝗿𝗲𝗮𝗺 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀, learners raved about the practical, hands-on approach and expert guidance that elevated their interview game. Here’s to you making 2025 the year of your GenAI career breakthrough! 🚀 Join the 10K+ learners who’ve transformed their careers with Mastering LLM. Explore the series here 👉 - https://lnkd.in/dXPadNSN #MasteringLLM #LLMCareer #GenAI #CareerSuccess #AIProfessionals

1.6M Views! At Mastering LLM (Large Language Model), we believe in teaching LLM every day. Our journey started in March 2024, and since then, we have been consistently growing, with 1.6M impressions across our learning content on LinkedIn this year. With 11K+ reactions & 1.6K comments & resharing, we continuously share knowledge of LLMs across the community. Thank you for making 45K big community and your support! #MasteringLLM #AI #CommunityGrowth #LLMs

At Mastering LLM (Large Language Model), In 2024, we introduced 20 pivotal concepts through our renowned "Coffee Break Concepts" series, making complex ideas accessible and engaging. Here's a recap of our journey so far: - Volume 1: Difference between bi-encoders & cross encoders -https://lnkd.in/dq45iTCa - Volume 2: Tired of Poor RAG results? Follow this for improvements -https://lnkd.in/dH-6jf6H - Volume 3: How LLMs are trained? A simple guide to understand LLM training -https://lnkd.in/ddZnyTBG - Volume 4: How Agentic RAG Solves problems with Current RAG limitations -https://lnkd.in/dhiUcpd9 - Volume 5: Common Agentic Workflow Patterns -https://lnkd.in/d7Z3XaaK - Volume 6: What is ReAct Prompting? Most important piece in agentic frameworks -https://lnkd.in/dNYFWjQx - Volume 7: Understanding CrewAI -https://lnkd.in/dzeS-w27 - Volume 8: Query Analysis Techniques -https://lnkd.in/dzeS-w27 - Volume 9: How Much GPU Is Needed To Serve A Large Language Model (LLM) -https://lnkd.in/d6sY5FtW - Volume 10: How to calculate GPU needed to train a transformer-based LLM model -https://lnkd.in/dZbzVwHz - Volume 11: Best Practices for RAG Pipeline -https://lnkd.in/db7Whczd - Volume 12: How to choose the right LLM model for your use case? -https://lnkd.in/dSUkjR5F - Volume 13: How did LinkedIn build its AI search engine? -https://lnkd.in/dYGVbrmV - Volume 14: Why LLM Inference Optimization Matters -https://lnkd.in/dR7XnzVf - Volume 14.1: Understanding transformer inference -https://lnkd.in/dM-ahRag - Volume 15: Caching methods in large language models (LLMs) -https://lnkd.in/d3mqdBiN - Volume 16: Enhance Your Retrieval-Augmented Generation (RAG) Pipeline with Contextual Retrieva -https://lnkd.in/d-6BrESh - Volume 17: 11 Chunking Methods for RAG—Visualized and Simplified -https://lnkd.in/dGveTCiQ - Volume 18: Mastering LLM Coffee Break Series #18: Jailbreaking LLMs Explained! -https://lnkd.in/dbkwTFpd - Volume 19: Will Long-Context LLMs Make RAG Obsolete? -https://lnkd.in/dcKesRcb Each volume has been crafted to provide the knowledge you need to stay ahead in the dynamic field of AI and LLMs. Get ready for more important concepts in 2025! #MasteringLLM #AI #MachineLearning #LLM #TechEducation #CoffeeBreakConcepts

📄 Introducing 𝗠𝗮𝗿𝗸𝗜𝘁𝗗𝗼𝘄𝗻 by Microsoft: A versatile, open-source Python tool that converts various file formats into Markdown, streamlining data preprocessing for tasks like indexing and text analysis. 💡 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝗲𝗱 𝗙𝗼𝗿𝗺𝗮𝘁𝘀: • Documents: PDF, PowerPoint, Word, Excel • Images: Extracts EXIF metadata and performs OCR • Audio: Retrieves EXIF metadata and transcribes speech • Web Content: HTML pages • Data Files: CSV, JSON, XML • Archives: Processes contents of ZIP files 𝗪𝗵𝘆 𝗨𝘀𝗲 𝗠𝗮𝗿𝗸𝗜𝘁𝗗𝗼𝘄𝗻? 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝘀 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻: Converts diverse file types into a consistent Markdown format, facilitating seamless integration into data pipelines. 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝘀 𝗧𝗲𝘅𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: Provides clean, structured text suitable for natural language processing and machine learning applications. 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Customize and extend the tool to meet your specific project requirements. 📂 Explore the GitHub Repository: MarkItDown (In the comments) 𝗛𝗮𝘃𝗲 𝘆𝗼𝘂 𝘁𝗿𝗶𝗲𝗱 𝗠𝗮𝗿𝗸𝗜𝘁𝗗𝗼𝘄𝗻 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀? 𝗦𝗵𝗮𝗿𝗲 𝘆𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀 𝗮𝗻𝗱 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀 𝗯𝗲𝗹𝗼𝘄! #DataScience #AI #OpenSource #Python #TextAnalysis #Microsoft #Innovation

Many of us are confused when to use O1 series model from OpenAI and how do we write prompts to O1 models? Writing prompts to O1 models is very different then writing to other models, a simple example is you do not need to say O1 model to "think step by step". Openai has recently posted 4 best practices to follow to prompt O1 models. Credit: DeepLearning.AI course on O1 model.

🚀 𝗕𝗿𝗶𝗱𝗴𝗶𝗻𝗴 𝘁𝗵𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗚𝗮𝗽𝘀 𝗶𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 AI agents are driving innovation across industries, but their growing complexity introduces new 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀. A recent study highlights four key 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗴𝗮𝗽𝘀 that threaten the security of AI agents and their interactions. Let’s break it down:   --- 1️⃣ 𝗚𝗮𝗽 1: 𝗨𝗻𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝗳 𝗠𝘂𝗹𝘁𝗶-𝗦𝘁𝗲𝗽 𝗨𝘀𝗲𝗿 𝗜𝗻𝗽𝘂𝘁𝘀 AI agents rely on 𝘂𝘀𝗲𝗿 𝗶𝗻𝗽𝘂𝘁𝘀 to perform tasks, but multi-step interactions can be unpredictable.   - Insufficiently described or malicious inputs can trigger 𝗮𝗱𝘃𝗲𝗿𝘀𝗮𝗿𝗶𝗮𝗹 𝗮𝘁𝘁𝗮𝗰𝗸𝘀 like 𝗽𝗿𝗼𝗺𝗽𝘁 𝗶𝗻𝗷𝗲𝗰𝘁𝗶𝗼𝗻 or 𝗷𝗮𝗶𝗹𝗯𝗿𝗲𝗮𝗸𝗶𝗻𝗴.   - These risks can cause unintended actions or cascading errors that compromise agent behavior.   𝗪𝗵𝗮𝘁’𝘀 𝗻𝗲𝗲𝗱𝗲𝗱? Robust mechanisms to ensure inputs are clear, secure, and resistant to exploitation.   --- 2️⃣ 𝗚𝗮𝗽 2: 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗶𝗻 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻𝘀 The internal operations of AI agents, such as 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴, 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴, 𝗮𝗻𝗱 𝘁𝗼𝗼𝗹 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻, are intricate and often implicit.   - 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀 (false outputs) and 𝗯𝗮𝗰𝗸𝗱𝗼𝗼𝗿 𝗮𝘁𝘁𝗮𝗰𝗸𝘀 can emerge due to flaws in the reasoning pipeline.   - Tool misuse during task execution can lead to unsafe or unintended actions.   𝗪𝗵𝗮𝘁’𝘀 𝗻𝗲𝗲𝗱𝗲𝗱? Monitoring and auditing the internal states of AI agents to detect and mitigate threats in real time.   -- 3️⃣ 𝗚𝗮𝗽 3: 𝗩𝗮𝗿𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝗳 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀 AI agents operate in diverse environments—from 𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 like robotics to 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲𝗱 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀 in software.   - Inconsistent behavior across environments can lead to 𝗺𝗶𝘀𝗮𝗹𝗶𝗴𝗻𝗲𝗱 𝗮𝗰𝘁𝗶𝗼𝗻𝘀 or unintended outcomes.   - Physical threats include sensor interference, hardware vulnerabilities, and resource management attacks like 𝗗𝗲𝗻𝗶𝗮𝗹 𝗼𝗳 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 (𝗗𝗼𝗦).   𝗪𝗵𝗮𝘁’𝘀 𝗻𝗲𝗲𝗱𝗲𝗱? Strategies to ensure consistent, reliable, and secure performance across all operational settings.   --- 4️⃣ 𝗚𝗮𝗽 4: 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻𝘀 𝘄𝗶𝘁𝗵 𝗨𝗻𝘁𝗿𝘂𝘀𝘁𝗲𝗱 𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗘𝗻𝘁𝗶𝘁𝗶𝗲𝘀 AI agents often collaborate or compete with 𝗼𝘁𝗵𝗲𝗿 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁 𝘄𝗶𝘁𝗵 𝘁𝗼𝗼𝗹𝘀, or retrieve information from 𝗺𝗲𝗺𝗼𝗿𝘆 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. These interactions introduce additional risks:   - 𝗔𝗴𝗲𝗻𝘁2𝗔𝗴𝗲𝗻𝘁 𝗧𝗵𝗿𝗲𝗮𝘁𝘀: Adversarial behaviors, deception, and secret collusion.   - 𝗠𝗲𝗺𝗼𝗿𝘆 𝗧𝗵𝗿𝗲𝗮𝘁𝘀: Data poisoning, privacy breaches, and synchronization issues in short-term and long-term memory.   𝗪𝗵𝗮𝘁’𝘀 𝗻𝗲𝗲𝗱𝗲𝗱? Proper isolation, auditing, and synchronization of memory and agent interactions to prevent misuse or exploitation.   ---    #ArtificialIntelligence #AI #Cybersecurity #AIagents #Innovation #TechTrends #FutureOfAI

🚀 𝗧𝗵𝗲 𝗔𝗻𝗮𝘁𝗼𝗺𝘆 𝗼𝗳 𝗮𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁: 𝗙𝗿𝗼𝗺 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻 𝘁𝗼 𝗔𝗰𝘁𝗶𝗼𝗻 AI agents are at the forefront of innovation,But what makes these agents so powerful? At their core, an AI agent operates through a well-defined and 𝗶𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 consisting of three interconnected components: --- ### 1️⃣ 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻: 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗜𝗻𝗽𝘂𝘁𝘀 The journey starts with 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻—the agent’s ability to sense, process, and understand inputs from its environment. - Inputs can be in various forms—𝘁𝗲𝘅𝘁, 𝗮𝘂𝗱𝗶𝗼, 𝗶𝗺𝗮𝗴𝗲𝘀, 𝗼𝗿 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗱𝗮𝘁𝗮. - The data often goes through 𝗽𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 techniques like 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 to enhance quality and ensure the "𝗯𝗿𝗮𝗶𝗻" receives meaningful information. - This stage mimics human perception, where raw data is transformed into usable insights, setting the stage for the next step. --- ### 2️⃣ 𝗕𝗿𝗮𝗶𝗻: 𝗧𝗵𝗲 𝗖𝗲𝗻𝘁𝗲𝗿 𝗼𝗳 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 Once inputs are perceived, the agent’s 𝗕𝗿𝗮𝗶𝗻 takes over. This component is powered by 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠𝘀), which are capable of: - 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴: Analyzing information, deducing logical conclusions, and addressing complex queries. - 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴: Strategizing steps to solve tasks effectively. For example, if the agent is tasked with booking flights, it breaks the process into subtasks—searching flights, comparing prices, and finalizing bookings. - 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴: Selecting the best tools, steps, or responses based on its reasoning and planning outcomes. The brain acts as the "𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗲𝗻𝗴𝗶𝗻𝗲" of the AI agent. By combining reasoning and planning, it ensures tasks are approached intelligently and efficiently. --- ### 3️⃣ 𝗔𝗰𝘁𝗶𝗼𝗻: 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 With a clear plan in place, the agent moves to the 𝗔𝗰𝘁𝗶𝗼𝗻 phase. This is where it interacts with external tools, APIs, or systems to execute tasks. - Actions can include 𝗰𝗮𝗹𝗹𝗶𝗻𝗴 𝗔𝗣𝗜𝘀, accessing databases, performing calculations, or even generating outputs like reports, code, or insights. --- ### 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄: 𝗔 𝗕𝗶𝗴𝗴𝗲𝗿 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 🌐 1. 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗢𝘁𝗵𝗲𝗿 𝗔𝗴𝗲𝗻𝘁𝘀: Agents can cooperate or compete to solve more complex problems in multi-agent systems. 2. 𝗠𝗲𝗺𝗼𝗿𝘆: By utilizing 𝘀𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺 𝗺𝗲𝗺𝗼𝗿𝘆 for current tasks and 𝗹𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗺𝗲𝗺𝗼𝗿𝘆 for historical context, agents enhance performance and decision-making. 3. 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻: Whether in 𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝘀𝗽𝗮𝗰𝗲𝘀 like robotics or 𝘃𝗶𝗿𝘁𝘂𝗮𝗹 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀 like software tools. --- 🔍 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝘀𝗲𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆? #ArtificialIntelligence #AI #Innovation #FutureOfWork #AIagents #Technology