Pairrot

✨ The State of AI Agents in 2024: Adoption and Trends ✨ AI agents are systems powered by large language models (LLMs) that can autonomously decide the flow of an application, often combining reasoning and action to complete tasks. Much like autonomous vehicles, AI agents operate on a spectrum of capabilities, from simple task execution to more complex, multi-step workflows. In 2024, AI agents are no longer experimental—they’re becoming essential tools across industries. A recent survey of over 1,300 professionals reveals how businesses are adopting these systems, the challenges they face, and the trends shaping the future of AI agents. 🔹 Adoption Trends 51% of organizations are already using AI agents in production, with mid-sized companies leading the charge at 63%. 78% of respondents plan to deploy agents soon, with interest spanning tech and non-tech industries alike. 🔹 Key Use Cases Agents are being used for research and summarization (58%), personal productivity (53%), and customer service (46%), showcasing their versatility in both routine and complex tasks. Companies are evolving beyond basic chat-based systems to advanced multi-agent collaboration frameworks. 🔹 Challenges and Controls Performance reliability is the biggest hurdle, followed by the need for technical expertise and time to deploy effectively. Guardrails like tracing tools and human oversight are critical, especially for enterprises managing risk and compliance. As AI agents mature, their potential to reshape workflows and drive efficiency is clear. However, adoption requires careful planning to balance innovation with reliability and control. 📊 Read more about this survey on LangChain: https://lnkd.in/gbybb9aH What’s your take on the future of AI agents? Let’s discuss. #AI #AIAgents #ArtificialIntelligence #TechnologyTrends #LangChain

I'm incredibly honored to have received an Innovation Award from UNIZO with Pairrot on the Day of the Entrepreneur! 🏆 This recognition highlights the journey of turning innovative ideas into impactful solutions, and it motivates me to keep pushing boundaries. 🚀 A big congratulations to my fellow award winners: Koenraad Boel, Gilles Van Eecke, Evelyne Minnaert, and Tomas De Nul. Your stories of creativity and perseverance are truly inspiring! It's amazing to see how innovation takes so many forms and drives positive change across industries. A warm thank you to Rudi De Kerpel for his inspiring talk on innovation. Your words reminded us all of the importance of staying curious and following our intuition to drive meaningful change. 💡 Here’s to more innovation, collaboration, and growth in the future! 🚀✨ #Innovation #Entrepreneurship #Unizo #Pairrot #InnovationAward #DagVanDeOndernemer

🤔 Does Your RAG System Speak the Right Language? When building a Retrieval-Augmented Generation (RAG) system, your choice of embedding model can make or break the system’s success. I recently put this to the test by comparing an English-only embedding model with a multilingual one. The results? Eye-opening. 👉 One model struggled to retrieve relevant information. 👉 The other delivered precise, context-rich insights—all because it understood the language and nuances of the data. The lesson: Not all embeddings are created equal. Choosing the right model isn’t just a detail—it’s the foundation of an accurate and reliable RAG system. Are you building a non-English RAG system? This is a step you can’t afford to overlook. Curious about how embedding models stack up? Check out my blog for the full story: https://lnkd.in/eSfHwXFh #AI #RAGSystems #MultilingualAI #Embeddings #GenerativeAI #Innovation

Building AI agents is exciting, but deploying them for real-world impact? That’s where the challenge begins! In my latest article, I dive into the full process of deploying an AI agent using LangGraph, LangServe, and AWS. 🚀 👉 https://lnkd.in/e98QXa_R #AI #AIDeployment #MachineLearning #LangGraph #AWS #TechInsights"

At Pairrot, we are proud to be part of this important project, 'cAIre,' in collaboration with Yaël Slaghmuylder, postdoctoral researcher at Ghent University. Our goal is to raise awareness and provide much-needed support for cancer survivors dealing with chronic complaints after treatment. By leveraging AI, we aim to make a difference through innovation and care. Help us make 'AI als gids voor kankeroverlevers' a reality by casting your vote before 3 November. 🔗 https://lnkd.in/ebm7icCf

🚀 RAG on Steroids: Self-RAG 🚀 While Retrieval-Augmented Generation (RAG) is a game-changer for AI by pulling in external data to improve responses, it often falls short. Why? RAG can retrieve irrelevant or misleading documents without evaluating their true relevance. This clutters AI outputs and increases the risk of hallucinations. That’s where Self-RAG comes in. It upgrades RAG with self-correction and self-reflection, ensuring AI doesn’t just pull information, but evaluates it. By assessing the relevance of retrieved content and checking for hallucinations, Self-RAG makes responses smarter, cleaner, and more reliable. Want to see how it works? In my latest article, I break down how to build your own Self-RAG AI agent with LangGraph, step by step. Read the full guide on Medium ⬇️ https://lnkd.in/eyYkjcnt #AI #SelfRAG #RAG #LangGraph #TechInnovation #AIDevelopment #MachineLearning #Tech

🚀 Improving AI Retrieval with Contextual Retrieval Techniques In AI applications like customer support bots or legal analysis tools, retrieval accuracy is critical for providing relevant and useful information. Traditional Retrieval-Augmented Generation (RAG) methods often lose important context, leading to incomplete or incorrect responses. Contextual Retrieval addresses this issue by adding context to data chunks before embedding and indexing. Using techniques like Contextual Embeddings and Contextual BM25, retrieval accuracy can improve by 49%. When combined with reranking, this can increase to 67%, significantly enhancing AI performance in handling large knowledge bases. 📊 For example, BM25 ensures precise term matching (e.g., error codes or specific names), while semantic embeddings handle broader contextual relationships. Together, these techniques provide more accurate and comprehensive results in domains like legal research, technical support, or financial analysis. For developers working with large-scale AI systems, Contextual Retrieval offers a scalable solution to improve retrieval performance while maintaining efficiency. Source: https://lnkd.in/esXRvHKp #AI #ContextualRetrieval #MachineLearning #RAG #BM25 #AIEngineering

🎉 Published in AI Advances! 🎉 I’m excited to share that my latest article has been featured in AI Advances. In this piece, I dive into my experiences with LangGraph Studio, a new IDE for building and testing LangChain-based AI agents. As an AI engineer, I’m always looking for tools that can make the development process smoother. I found LangGraph Studio’s visual tools and real-time debugging particularly useful for managing complex workflows, and the integration with Visual Studio Code added a layer of flexibility. If you’re curious about how this tool could fit into your own AI projects, feel free to read more in my article here: https://lnkd.in/e-qWUjVd #AI #LangChain #AIAdvances #AIEngineering #DevTools

🆕 New week, new blog post! 🆕 This time, I’m diving into building AI agents with LangGraph—unlocking the potential of autonomous systems that go beyond basic chatbots. In this post, I guide you through how to create a smart AI agent that can handle multi-step tasks like computing complex calculations using real-world examples. 💡 What you’ll learn: - How to develop stateful, multi-actor AI agents with LangGraph - Using AI tools for automating processes and handling complex workflows - Practical applications of AI agents, such as lead generation and data-driven decisions If you're working with AI, automation, or looking to enhance business workflows with AI agents, this post is packed with insights for you! 🔗 Read the full article here: https://lnkd.in/ej5HB_Bh #AI #LangGraph #AIagents #Automation #MachineLearning #TechInnovation #BusinessAI #IntelligentSystems #AIWorkflow

🚀 Excited to Share Our Latest AI Project! 🤖 We’re thrilled to announce the launch of an AI-powered Knowledge Assistant developed for Assuco Insurance Agency! This cutting-edge solution leverages AI to revolutionize knowledge management and customer service within the insurance sector. Using a Retrieval-Augmented Generation (RAG) approach, this assistant provides fast, context-aware answers by tapping into a vast database of insurance policies and relevant industry resources. 🔍 Project Highlights: 🔹 Seamlessly integrates data from the Sector Catalog (by Portima) and various insurance websites for accurate, real-time information 🔹 Powered by the Claude 3 Sonnet language model from Anthropic for human-like, context-sensitive responses 🔹 Built with a user-centric approach, featuring source verification for transparency and continuous enhancements based on feedback The assistant is live on the Assuco website, making it accessible for both customers and employees to get the precise information they need, improving efficiency and service quality. 💻 Explore it here: https://lnkd.in/e4NFjjsF This project is a strong example of how AI can drive innovation and deliver real value in the insurance industry. Looking forward to seeing how it enhances Assuco’s operations! #AI #InsurTech #Innovation #CustomerExperience #InsuranceTech #RAG