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🔍 ** What are Key Components of a Multi-Agent System ** 🔍 Irrespective of exact implementation, the core components of these agents can be simplified into two main areas (see diagram below): 1. **Agent Capabilities** 🛠️ - [Tools] **Acting**: Utilizing tools and executing tasks. - [Memory] **Adapting**: Learning and recalling information. - [Planning] **Reasoning**: Planning, deducing, etc. 2. **Agent Interactions** 🤝 - **Orchestration**: The sequence in which agents act as tasks progress and how they communicate (send/receive messages). These capabilities and interaction methods are then driven by generative AI models. For instance, the selection of the right tool, planning, determining how and when to reuse previous data in short/long term memory, defining action order/sequences can be driven by an AI model. Tools like #autogen led by @chiwang and the broader team make implementing these concepts relatively straightforward. We will be sharing more tidbits like this to better illustrate emerging patterns with multi-agent systems. What core behaviors or components are missing? #MultiAgentSystems #AI #Collaboration #Technology References [1] Multi-Agent LLM Applications | A Review of Current Research, Tools, and Challenges https://lnkd.in/eQajE3_Q [2] AutoGen on Github https://lnkd.in/gCnu2Cii 

This sets the stage for AI projects in the second half of '24: While I enjoyed my 🌴, zero-shot performance of AI agents made a leap from 19% to 82% success rate – I take 0% credit for this achievement. We're seeing the first #Netlight clients move on from #RAG to AI automation use cases. Because automation has direct, measurable impact on operational efficiency. Automation relies on agents, very different from RAG systems. Today, the hard thing is to make AI agents reliable enough for production, which limits real-world use cases. MultiOn and Stanford University researchers achieved a leap improvement with a new approach: Agent Q. The new blueprint for AI agents? In short, it combines 3 steps: 1️⃣ Guided Search with Monte Carlo Tree Search (MCTS) -> explore actions and web pages 2️⃣ AI self-critique -> refine decision making 3️⃣ Direct Preference Optimization -> fine-tune the LLM to learn from on exploration Here is the full paper: https://lnkd.in/dbRGKh7Z 🤙 PM me if you want to discuss your automation use cases. #AI #Automation #Innovation #Practice #Netlight #Consulting #AgentQ

🤖 Day 3: Building AI Agents! 🤖 Kaggle Today’s focus was on Generative AI agents — these are systems that combine LLMs with external data and tools to create intelligent, autonomous agents capable of handling real-world tasks. My key takeaways: Function calling: I learned how to give LLMs access to real-world tools like databases (e.g., SQL) to answer questions with live data. LangGraph: I explored how to build an agentic ordering system for a café, demonstrating how AI can take orders and interact with customers effectively. AI agents are no longer just passive assistants — they can actively interact with users and external systems to perform complex tasks. This opens up exciting possibilities for creating interactive AI applications that can understand and respond to dynamic environments. 🧠 #AI #GenerativeAI #MachineLearning #ArtificialIntelligence #AIAgents #TechInnovation

𝗫𝗔𝗜: 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗔𝗜 #explainableai, as its name suggests, aims to demystify the complex decision-making process of artificial intelligence systems by providing #understandable #explanations on the model outputs. Instead of blindly accepting AI outputs, explainable AI algorithms offer transparency through numerical evaluations and coefficients, akin to interpreting regression results. #𝙺𝚎𝚢 𝙵𝚎𝚊𝚝𝚞𝚛𝚎𝚜: ‣ Define XAI as the process of understanding and interpreting #AI model decisions. ‣ Highlight its importance in understanding model predictions for various applications like #insurance, #banking, #advertisements, and #healthcare. #𝚃𝚢𝚙𝚎𝚜 𝚘𝚏 𝚇𝙰𝙸 ‣ Discuss model-based approaches which rely on understanding the structure of simpler models like #RandomForest and #XGBoost. ‣ Explain post hoc black box and white box approaches, focusing on techniques that analyze the entire model as an input-output system. #𝙸𝚗𝚝𝚎𝚛𝚙𝚛𝚎𝚝𝚊𝚋𝚒𝚕𝚒𝚝𝚢 𝙼𝚎𝚝𝚑𝚘𝚍𝚜: ‣ Detail model-agnostic interpretability methods like #LIME that simplify complex models into understandable formats. ‣ Explain how #SHAP computes feature contributions and how PDP plots feature dependencies to aid interpretation. #𝙲𝚘𝚍𝚎: #Lime, a notable technique within explainable AI providing prediction probabilities, utilizes surrogate #models like #randomforest to break down predictions into interpretable components, facilitating comprehension even for non-technical users. #explainableai #lime #shap #decisiontree #randomforest #bagging #algorithms #xgboost #boosting #algorithms #artificialintelligence #prediction #probability

Just read about something pretty interesting - Anthropic has released this new open-source standard called the Model Context Protocol (MCP). 🚀 It's basically tackling one of the big headaches in AI right now: getting AI assistants to properly connect with different data systems. What caught my attention is how it could change the way we handle AI integrations. ✨ Instead of having to deal with a bunch of different systems and protocols, developers can use this one standard to connect their data sources with AI tools. Block and Apollo are already using it, and apparently it's helping their AI systems work with information more effectively. I like that they've made it open-source too - seems like they really want people to build on it and improve it together, rather than keeping it locked down. 🤝 If you work with AI systems, it's definitely worth checking out! 👀 Could save a lot of headaches when it comes to integrations and making AI tools actually useful in real-world applications. Been seeing more stuff like this lately - seems like we're slowly moving towards AI that can work with our existing systems in a more practical way. Pretty cool to see where this might lead! 💡 #AI #TechNews #Innovation #OpenSource #MCP #FutureOfTech #ArtificialIntelligence #DevTools

If you're living in an AI echo chamber and would like to hear some contrarian views on topics like RAG, LLM fine-tuning, Open Source vs. Closed Source models, etc., you should join Anant Bhardwaj and me in a technical Webinar on December 5th... guaranteed to get 🌶️ #Instabase #CompoundAI #LLMs #AI #RAG

Exciting times in the #AI universe! 🌍 Otto, a new and intuitive tool, revolutionizes our interaction with AI agents using simple table structures. Imagine: what if setting up your AI-driven workflows 🛠️ wasn't a dreaded steep climb but a smooth, almost intuitive process? Otto proposes the tantalizing possibility of transforming exhaustive manual labor into highly effective, AI-led efficiencies. Key features include a user-responsive interface with precision filters, synchronous processing that fast-tracks time-consuming tasks, and an AI language translation that sifts through a variety of data. 🔁🔍 Efficient search and quick, bulk data imports in tandem with the smart add function make navigation through massive data pools easy and precise. On top of all this, Otto generously gives detailed ground-level insights by assessing various data dependencies, thereby clearing pathways toward strategic decision making.💡 But here's the magic: Among a list of resource/prompts friendly pre-loaded table formats to kickstart your workday. Here's to embracing change and pushing the boundaries of #IntelligentAutomation! Remember, networking transcends transactional encounters—it's about growing into continual learners and collaborators. Keep striving boldly for #Innovation. 👩🚀 To dive deeper into what Otto has to offer, click ⏩ https://lnkd.in/g7wBUDJ Tracker sign-up: 🐝 #AI #Analytics #DataAnalysis #WorkflowAutomation

In a world where data is the new oil , having robust Data + AI solutions gives you a significant competitive edge. Harnessing the power of data and AI means you’re not just keeping up with the competition – you’re leading the pack. 🦾 Make more informed decisions, faster. 🦾 Understand your customers like never before. 🦾 Automate routine tasks and optimise your operations. 🦾 Stay ahead of the curve with innovative AI applications. Don't be the last to grow - embrace Data and AI now! 🚀💡 https://lnkd.in/gFY7PHSB #datasolutions #databricks #databricksplatform #databricksconsultingpartner #digitalmarketingagencymelbourne #dataandAI #artificialintelligence #intelligentautomation #dataAI

How can you master prompts with LLMs?🚀 1️⃣ Be specific: Clear prompts lead to accurate outputs. 2️⃣ Use examples: Show the AI what you expect. 3️⃣ Define formats: Structured responses save time. 4️⃣ Tap into your data: Combine AI with real-time info using techniques like Retrieval-Augmented Generation (RAG). 5️⃣ Simplify tasks: Break down complex requests for better results. For everyday tasks, mastering prompt engineering is helpful. For building AI applications, it’s essential. Happy prompting! 💡 #PrivateEquity #CorporateDevelopment #MergersAndAcquisitions #ExternalGrowth #BuildUpStrategy #BoltOnAcquisitions #BuyAndBuild #MergerCircle 

II recently had the opportunity to dive into the whitepaper "Generative AI Agents" by Julia Wiesinger, Patrick Marlow, and Vladimir Vuskovic, which provides deep insights into how AI agents extend the capabilities of language models. Here are the key takeaways: 🧩 Building Blocks of Generative AI Agents: ●Agents leverage tools to access real-time data, suggest actions, and autonomously execute complex tasks beyond the capabilities of a standalone model. ●The Orchestration Layer is at the core, using reasoning techniques like ReAct, Chain-of-Thought, and Tree-of-Thoughts to guide decisions and actions. 🔧 Tools for Interacting with the World: ●Extensions enable agents to interact with APIs and access real-time info. ●Functions offer developers more control by breaking tasks into manageable parts. ●Data Stores provide access to structured and unstructured data, enabling agents to make data-driven decisions. 🚀 The Future of AI Agents: The future is promising, with increasingly sophisticated tools and enhanced reasoning capabilities. Agent chaining—combining specialized agents for specific tasks—will be key to solving complex, real-world problems across industries. 💡 Iterative Development: Building effective AI agents requires an iterative approach. Experimentation and refinement are crucial to creating solutions tailored to business needs and unique use cases. This whitepaper gives a roadmap for the next frontier in AI, where intelligent agents will revolutionize industries by combining specialized expertise and solving tasks that were once impossible. #GenerativeAI #AIagents #CognitiveArchitecture #AIInnovation #FutureOfAI