✨ 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐑𝐀𝐆 𝐯𝐬 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐑𝐀𝐆 ✨ The MuleSoft AI Chain (MAC) Project provides a suite of powerful connectors that allow you to build any RAG workflows, from standard and advanced to agentic RAG. Whether you need to create an agent-based architecture or optimize RAG for performance, the MAC Project gives you the low-level operations you need to customize and combine tasks based on your specific requirements. 🧠⚙️ Here's how it works: 1️⃣ Flexible Operations: The MAC Project connectors let you define and refine user queries, generate multiple alternative results, and even run scatter-gather tasks efficiently. 2️⃣ Custom Workflows: Whether you're building standard RAG or advanced Agentic RAG, the MAC Project connectors allow you to orchestrate complex AI workflows with ease. 3️⃣ Simple Implementation: The beauty of the MAC Project is in its ease of use— you can start building AI solutions with minimal effort by leveraging connectors that handle everything from message transformations to performing retrieval-augmented generation (RAG). Below is a short demo comparing the 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐑𝐀𝐆 technique with the 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐑𝐀𝐆 technique in MuleSoft and how it improves the results. There are various other ways to perform 𝘈𝘥𝘷𝘢𝘯𝘤𝘦𝘥 𝘙𝘈𝘎, in this video, we are using the LLM to refine the original query with 3 alternative queries and retrieve through similarity search relevant data, which is prepared and sent to the LLM. And yes, you can even do it better than Advanced RAG by bringing the agentic entity into the decision-making process for context retrieval. The Agentic RAG MuleSot demo is being prepared by Mihael Bosnjak to be posted soon. Curious to learn more? Check out the documentation at mac-project.ai/docs and explore how the MAC Project can transform your AI solutions! 🚀✨ 𝐂𝐡𝐞𝐜𝐤𝐨𝐮𝐭 𝐯𝐚𝐫𝐢𝐨𝐮𝐬 𝐝𝐞𝐦𝐨𝐬 𝐨𝐧 𝐑𝐀𝐆 𝐰𝐢𝐭𝐡 𝐌𝐮𝐥𝐞𝐒𝐨𝐟𝐭 𝐀𝐈 𝐂𝐡𝐚𝐢𝐧 📺 𝘎𝘦𝘯𝘦𝘳𝘢𝘭 𝘒𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘚𝘵𝘰𝘳𝘦𝘴 by Mihael Bosnjak: https://lnkd.in/edAnpsTT 📺 𝘞𝘦𝘣 𝘊𝘰𝘯𝘵𝘦𝘯𝘵 𝘵𝘰 𝘒𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 by Yogesh Mudaliar: https://lnkd.in/eUrhS_G2 📺 𝘗𝘰𝘥𝘤𝘢𝘴𝘵 𝘵𝘰 𝘒𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 by Beauty Beauty Mishra: https://lnkd.in/ezPxf-di 📺 𝘊𝘢𝘮𝘦𝘳𝘢 (𝘐𝘮𝘢𝘨𝘦) 𝘵𝘰 𝘒𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 by Jonathan Chen: https://lnkd.in/ebRrZu8A 📺 𝘔𝘢𝘳𝘬𝘦𝘵𝘪𝘯𝘨 𝘱𝘶𝘳𝘱𝘰𝘴𝘦 by Burak Tas: https://lnkd.in/eKHpGfAU 📺 𝘊𝘰𝘮𝘣𝘪𝘯𝘪𝘯𝘨 𝘔𝘈𝘊 𝘗𝘳𝘰𝘫𝘦𝘤𝘵 𝘤𝘰𝘯𝘯𝘦𝘤𝘵𝘰𝘳𝘴 𝘸𝘪𝘵𝘩 𝘋𝘢𝘵𝘢 𝘊𝘭𝘰𝘶𝘥 by Alick Wong: https://lnkd.in/efbrpxvj #rag #retrieval #augmented #generation #mulesoft
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Scaling Businesses Faster with AI Agents | Expert in Agentic AI & Cloud Native Solutions for businesses | Founder at JUTEQ | Driving Innovation, Leadership, and Growth | From Ideas to Innovations—Let’s Make it Happen! 🤝
Let's figure out the differences between Agentic RAG and AI Agents Here's how different they are from each other... I have been working on both AI Agents and RAG systems for a long time. So much so that we've even built our own AI Agent application for Cloud Architecture. However, a few new integrations of both fields have stirred a new wave of confusion. But before we look at differences let's check out the known similarities 📌 Similarities between Agentic RAG and AI Agents: ↳ Goal-oriented: Agentic RAG and AI agents are designed to achieve a goal and perform sub-tasks. ↳ Modular design: Both systems have modular design, making debugging easy. ↳ Multi-LLM support: Both can use Multiple LLMs for agentic capabilities. ↳ Tool Use: Both rely heavily on external tools and APIs for executing tasks beyond their inherent abilities. ↳ Human-in-Loop: Both systems require some human oversight for iterative improvement. ↳ Capability: Both systems can achieve significant performance enhancements with additional agents 📌 Now the differences: 1. Core Function: ↳ Agentic RAG - Modular Agents + RAG system for task-specific retrieval/generation ↳ AI Agents - General purpose LLM-based agents to automate niche tasks across domains. 2. Autonomy: ↳ Agentic RAG - Built-in agents enabling full retrieval/generation automation in RAG pipelines ↳ AI Agents - Primarily driven by user input, allowing autonomous decisions 3. Knowledge bases: ↳ Agentic RAG - Utilizes prompt pools as internal knowledge bases. Each sub-agent has its pool containing task-specific knowledge about historical patterns and trends. ↳ AI Agents - Relies on structured queries and rule-based reasoning with knowledge base 4. Dynamic Data retrieval: ↳ Agentic RAG - Integrated Agentic retrieval/generation for real-time data ↳ AI Agents - Optional tool-based retrieval system 5. Memory: ↳ Agentic RAG - employs adaptive memory. guides retrieval based on past interactions and stored data. ↳ AI Agents: Can utilize long-term memory with VectorDB and semantic search, and short-term working memory for ongoing tasks 6. Reasoning and actions: ↳ Agentic RAG - A single or hierarchical master agent utilizes the ReAct prompting technique for step-by-step thinking and tool usage. ↳ AI Agents - This can be controlled by system prompts and/or symbolic plans (SOPs) defining states, transitions, and actions. 7. Use cases: ↳ Agentic RAG - Forecasting, anomaly detection, imputation, classification ↳ AI Agents - Roleplay, CRM and device-specific task automation But what is the difference between Large Actions Models and AI Agents? Check the comment to learn more What are your views on both Agentic RAG and AI Agents? Let me know in the comments below 👇 Please make sure to, ♻️ Share 👍 React 💭 Comment to help more people learn P.S Check comments for references
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Rakesh, your breakdown of the RAG Strategy Model for AI agents is insightful and resonates deeply with the strategic approach we take at Aries Systems Consulting LLC. By integrating Robust AI frameworks, Accurate data pipelines, and Governance-focused DevSecOps, we empower AI agents to operate securely and deliver actionable insights across diverse industries. Our Innovating AI Systems Solutions business model emphasizes hybrid cloud infrastructures, MLOps, and AI-driven automation to ensure AI agents align with business goals while remaining scalable and secure. Let's continue to explore how we can synergize our methodologies to enhance the practical application of AI agents in enterprise transformation!
Scaling Businesses Faster with AI Agents | Expert in Agentic AI & Cloud Native Solutions for businesses | Founder at JUTEQ | Driving Innovation, Leadership, and Growth | From Ideas to Innovations—Let’s Make it Happen! 🤝
Let's figure out the differences between Agentic RAG and AI Agents Here's how different they are from each other... I have been working on both AI Agents and RAG systems for a long time. So much so that we've even built our own AI Agent application for Cloud Architecture. However, a few new integrations of both fields have stirred a new wave of confusion. But before we look at differences let's check out the known similarities 📌 Similarities between Agentic RAG and AI Agents: ↳ Goal-oriented: Agentic RAG and AI agents are designed to achieve a goal and perform sub-tasks. ↳ Modular design: Both systems have modular design, making debugging easy. ↳ Multi-LLM support: Both can use Multiple LLMs for agentic capabilities. ↳ Tool Use: Both rely heavily on external tools and APIs for executing tasks beyond their inherent abilities. ↳ Human-in-Loop: Both systems require some human oversight for iterative improvement. ↳ Capability: Both systems can achieve significant performance enhancements with additional agents 📌 Now the differences: 1. Core Function: ↳ Agentic RAG - Modular Agents + RAG system for task-specific retrieval/generation ↳ AI Agents - General purpose LLM-based agents to automate niche tasks across domains. 2. Autonomy: ↳ Agentic RAG - Built-in agents enabling full retrieval/generation automation in RAG pipelines ↳ AI Agents - Primarily driven by user input, allowing autonomous decisions 3. Knowledge bases: ↳ Agentic RAG - Utilizes prompt pools as internal knowledge bases. Each sub-agent has its pool containing task-specific knowledge about historical patterns and trends. ↳ AI Agents - Relies on structured queries and rule-based reasoning with knowledge base 4. Dynamic Data retrieval: ↳ Agentic RAG - Integrated Agentic retrieval/generation for real-time data ↳ AI Agents - Optional tool-based retrieval system 5. Memory: ↳ Agentic RAG - employs adaptive memory. guides retrieval based on past interactions and stored data. ↳ AI Agents: Can utilize long-term memory with VectorDB and semantic search, and short-term working memory for ongoing tasks 6. Reasoning and actions: ↳ Agentic RAG - A single or hierarchical master agent utilizes the ReAct prompting technique for step-by-step thinking and tool usage. ↳ AI Agents - This can be controlled by system prompts and/or symbolic plans (SOPs) defining states, transitions, and actions. 7. Use cases: ↳ Agentic RAG - Forecasting, anomaly detection, imputation, classification ↳ AI Agents - Roleplay, CRM and device-specific task automation But what is the difference between Large Actions Models and AI Agents? Check the comment to learn more What are your views on both Agentic RAG and AI Agents? Let me know in the comments below 👇 Please make sure to, ♻️ Share 👍 React 💭 Comment to help more people learn P.S Check comments for references
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But before we look at differences, let's check out the known similarities: 📌 Similarities between Agentic RAG and AI Agents: ↳ Goal-oriented: Agentic RAG and AI agents are designed to achieve a goal and perform sub-tasks. ↳ Modular design: Both systems have modular design, making debugging easy. ↳ Multi-LLM support: Both can use Multiple LLMs for agentic capabilities. ↳ Tool Use: Both rely heavily on external tools and APIs for executing tasks beyond their inherent abilities. ↳ Human-in-Loop: Both systems require some human oversight for iterative improvement. ↳ Capability: Both systems can achieve significant performance enhancements with additional agents.
Scaling Businesses Faster with AI Agents | Expert in Agentic AI & Cloud Native Solutions for businesses | Founder at JUTEQ | Driving Innovation, Leadership, and Growth | From Ideas to Innovations—Let’s Make it Happen! 🤝
Let's figure out the differences between Agentic RAG and AI Agents Here's how different they are from each other... I have been working on both AI Agents and RAG systems for a long time. So much so that we've even built our own AI Agent application for Cloud Architecture. However, a few new integrations of both fields have stirred a new wave of confusion. But before we look at differences let's check out the known similarities 📌 Similarities between Agentic RAG and AI Agents: ↳ Goal-oriented: Agentic RAG and AI agents are designed to achieve a goal and perform sub-tasks. ↳ Modular design: Both systems have modular design, making debugging easy. ↳ Multi-LLM support: Both can use Multiple LLMs for agentic capabilities. ↳ Tool Use: Both rely heavily on external tools and APIs for executing tasks beyond their inherent abilities. ↳ Human-in-Loop: Both systems require some human oversight for iterative improvement. ↳ Capability: Both systems can achieve significant performance enhancements with additional agents 📌 Now the differences: 1. Core Function: ↳ Agentic RAG - Modular Agents + RAG system for task-specific retrieval/generation ↳ AI Agents - General purpose LLM-based agents to automate niche tasks across domains. 2. Autonomy: ↳ Agentic RAG - Built-in agents enabling full retrieval/generation automation in RAG pipelines ↳ AI Agents - Primarily driven by user input, allowing autonomous decisions 3. Knowledge bases: ↳ Agentic RAG - Utilizes prompt pools as internal knowledge bases. Each sub-agent has its pool containing task-specific knowledge about historical patterns and trends. ↳ AI Agents - Relies on structured queries and rule-based reasoning with knowledge base 4. Dynamic Data retrieval: ↳ Agentic RAG - Integrated Agentic retrieval/generation for real-time data ↳ AI Agents - Optional tool-based retrieval system 5. Memory: ↳ Agentic RAG - employs adaptive memory. guides retrieval based on past interactions and stored data. ↳ AI Agents: Can utilize long-term memory with VectorDB and semantic search, and short-term working memory for ongoing tasks 6. Reasoning and actions: ↳ Agentic RAG - A single or hierarchical master agent utilizes the ReAct prompting technique for step-by-step thinking and tool usage. ↳ AI Agents - This can be controlled by system prompts and/or symbolic plans (SOPs) defining states, transitions, and actions. 7. Use cases: ↳ Agentic RAG - Forecasting, anomaly detection, imputation, classification ↳ AI Agents - Roleplay, CRM and device-specific task automation But what is the difference between Large Actions Models and AI Agents? Check the comment to learn more What are your views on both Agentic RAG and AI Agents? Let me know in the comments below 👇 Please make sure to, ♻️ Share 👍 React 💭 Comment to help more people learn P.S Check comments for references
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This is an EXCELLENT breakdown explaining Agentic RAG vs. AI Agents.
Scaling Businesses Faster with AI Agents | Expert in Agentic AI & Cloud Native Solutions for businesses | Founder at JUTEQ | Driving Innovation, Leadership, and Growth | From Ideas to Innovations—Let’s Make it Happen! 🤝
Let's figure out the differences between Agentic RAG and AI Agents Here's how different they are from each other... I have been working on both AI Agents and RAG systems for a long time. So much so that we've even built our own AI Agent application for Cloud Architecture. However, a few new integrations of both fields have stirred a new wave of confusion. But before we look at differences let's check out the known similarities 📌 Similarities between Agentic RAG and AI Agents: ↳ Goal-oriented: Agentic RAG and AI agents are designed to achieve a goal and perform sub-tasks. ↳ Modular design: Both systems have modular design, making debugging easy. ↳ Multi-LLM support: Both can use Multiple LLMs for agentic capabilities. ↳ Tool Use: Both rely heavily on external tools and APIs for executing tasks beyond their inherent abilities. ↳ Human-in-Loop: Both systems require some human oversight for iterative improvement. ↳ Capability: Both systems can achieve significant performance enhancements with additional agents 📌 Now the differences: 1. Core Function: ↳ Agentic RAG - Modular Agents + RAG system for task-specific retrieval/generation ↳ AI Agents - General purpose LLM-based agents to automate niche tasks across domains. 2. Autonomy: ↳ Agentic RAG - Built-in agents enabling full retrieval/generation automation in RAG pipelines ↳ AI Agents - Primarily driven by user input, allowing autonomous decisions 3. Knowledge bases: ↳ Agentic RAG - Utilizes prompt pools as internal knowledge bases. Each sub-agent has its pool containing task-specific knowledge about historical patterns and trends. ↳ AI Agents - Relies on structured queries and rule-based reasoning with knowledge base 4. Dynamic Data retrieval: ↳ Agentic RAG - Integrated Agentic retrieval/generation for real-time data ↳ AI Agents - Optional tool-based retrieval system 5. Memory: ↳ Agentic RAG - employs adaptive memory. guides retrieval based on past interactions and stored data. ↳ AI Agents: Can utilize long-term memory with VectorDB and semantic search, and short-term working memory for ongoing tasks 6. Reasoning and actions: ↳ Agentic RAG - A single or hierarchical master agent utilizes the ReAct prompting technique for step-by-step thinking and tool usage. ↳ AI Agents - This can be controlled by system prompts and/or symbolic plans (SOPs) defining states, transitions, and actions. 7. Use cases: ↳ Agentic RAG - Forecasting, anomaly detection, imputation, classification ↳ AI Agents - Roleplay, CRM and device-specific task automation But what is the difference between Large Actions Models and AI Agents? Check the comment to learn more What are your views on both Agentic RAG and AI Agents? Let me know in the comments below 👇 Please make sure to, ♻️ Share 👍 React 💭 Comment to help more people learn P.S Check comments for references
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Developer Updates - July 2024 Several updates to share with you as we get ready for Launch Week! https://lnkd.in/eRZtXET7 - Launch Week 12 Join us August 12-16 for our next Launch Week event. We will be announcing a number of new features and giving away some great swag! https://lnkd.in/guZ5hybC - Disable Data API for Your Project You can now disable the Data API when creating a new project with a setting under "Advanced Options". This option bolsters the security of your data by preventing unintentional access from clients. https://lnkd.in/eHu92itG - Custom Schema for Data API Your project's Data API exposes the public schema by default, the most commonly used schema, and can lead to unintentional access to your data. Now, you can dictate which custom schema to expose via the Data API for better security and granular control. https://lnkd.in/eHu92itG - Save Money With Hourly Storage Billing On August 20, 2024, Supabase is changing billing for Storage from daily to hourly for new customers and gradually rolling it out for existing customers shortly thereafter. There are no billing changes for projects who continue to use Storage for the entire month while projects using Branching or Storage for partial months will see a reduction in their bill. https://lnkd.in/ePAzWC2e - Deploy More Functions at No Extra Cost We have increased the number of Edge Functions across all plans at no extra cost and removed usage-based billing to simplify your bill. https://lnkd.in/eyQkcmp3 - Quick product announcements • [Database] Postgres 13 Deprecation Notice • [Auth] Migrate from Auth0 to Supabase Auth • [Branching] You can customize the public environment variable prefix to use any framework • [Docs] Supabase docs now feature global navigation bar - Made with Supabase Krea - Realtime and interactive image generation in the browser, the easiest way to generate with AI Cheat Layer - The most powerful no-code agent editor on the planet. Learn new RPA concepts to build future-proof agents that are impossible in other RPA tools Udio - Generative music, you can even edit segments of the tracks using AI Pika - The idea-to-video AI platform that sets your creativity in motion. MakePodcast - Effortlessly craft professional podcasts in minutes using AI - Community Highlights Using Pre-commit Hook to Upload Local Media to Supabase Bucket Build Library Management System Using React, Shadcn/ui, Supabase and React Query From Scratch Instagram Clone in React Native: Video, Backend with Supabase & Push Notifications It all starts with Postgres (Interview to Paul Copplestone) GraphQL Quickstart with Supabase 13min High Intensity Postgres Workout (NO REPEATS) Read here: https://lnkd.in/eRZtXET7
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Senior Solutions Architect: Specializing in AI & ML Strategy | 20+ Years of Cross-Domain Expertise in Generative AI, MLOps, and Intelligent Automation at EMIDS
Rethinking AI Automation: Why Pipelines Beat Agent Frameworks for Business In the rapidly evolving world of AI and data solutions, I've noticed a trend towards complex agent frameworks like AutoGen, Crew AI, and LangChain's agent systems. Here's why I think they might not be the best approach for most business automation needs: The Demerits of Agent Frameworks: Over-Complexity: These frameworks often introduce unnecessary complexity for tasks that require straightforward automation. They're built around the idea of AI agents that can reason and choose actions dynamically, which might be overkill for defined business processes. Lack of Robustness: Due to their complexity, these systems can become brittle. When every run can produce different outcomes, reliability becomes an issue, which is critical in business applications. Abstraction Overload: Building on top of someone else's abstraction in a still-maturing field like AI can obscure what's happening behind the scenes, making debugging and optimization challenging. The Pipeline Advantage: Instead of agent frameworks, I advocate for a data pipeline approach: Simplicity: A pipeline model follows a straightforward input-process-output flow, akin to traditional ETL (Extract, Transform, Load) processes. This simplicity aids in understanding and maintenance. Reliability: By designing your workflow as a directed acyclic graph (DAG), where data flows in one direction, you ensure that each step's output is predictable and usable by the next, enhancing system reliability. Flexibility and Scalability: Pipelines can be easily modified or extended. You can add or remove steps without disrupting the entire system, which is perfect for evolving business needs. Example in Action: Consider an email classification and response system. Using a pipeline: Input: Receive an email. Processing: Classify the email (using an LLM or simpler models). Generate a response based on classification. Output: Send the response or store results. This can be coded with simple, sequential steps, leveraging design patterns like the Chain of Responsibility, making the system both robust and understandable. While agent frameworks are innovative, for most business applications where processes are well-defined or need to be, a pipeline model offers clarity, control, and scalability. Before adopting a new framework, consider if your problem can be solved more effectively with a straightforward, pipeline-based solution. https://lnkd.in/gjPQV6qN Let's discuss: Have you encountered challenges with agent frameworks, or do you find them fitting for your use cases? Share your experiences below! #AI #DataEngineering #Automation #TechTrends #BusinessSolutions #pipeline #ML #EMIDS #AIEngineering
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Over the past few weeks, I’ve been exploring the world of Generative AI, specifically looking into how it integrates with MuleSoft. During this journey, I came across the MuleSoft AI Chain (MAC) Project, which sparked my curiosity to build some use cases. After digging deeper into MAC and comparing it with other AI orchestration tools, here are my observations. 𝗠𝗔𝗖 (𝗠𝘂𝗹𝗲𝗦𝗼𝗳𝘁 𝗔𝗜 𝗖𝗵𝗮𝗶𝗻) 𝘃𝘀. 𝗢𝘁𝗵𝗲𝗿 𝗔𝗜 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗧𝗼𝗼𝗹𝘀 𝗘𝗮𝘀𝗲 𝗼𝗳 𝗨𝘀𝗲: 𝙈𝘼𝘾 𝙋𝙧𝙤𝙟𝙚𝙘𝙩: Designed for MuleSoft developers, the MAC Project offers minimal coding requirements, allowing users to configure and manage AI agents directly within the Anypoint Platform. This makes it accessible for those already familiar with MuleSoft's ecosystem. 𝙊𝙩𝙝𝙚𝙧 𝙏𝙤𝙤𝙡𝙨: Some AI orchestration tools may require more extensive coding and technical expertise, which can increase the complexity of setup and management. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀: 𝙈𝘼𝘾 𝙋𝙧𝙤𝙟𝙚𝙘𝙩: Provides seamless interaction with Large Language Models (LLMs) and vector stores, simplifying the integration of language models and vector databases. It connects AI services with MuleSoft components, enabling functionalities like sentiment analysis, chat prompts, and image generation. 𝙊𝙩𝙝𝙚𝙧 𝙏𝙤𝙤𝙡𝙨:While other orchestration tools may offer similar capabilities, they may not be as tightly integrated with enterprise-grade API management platforms like MuleSoft, which excels in this area. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: 𝙈𝘼𝘾 𝙋𝙧𝙤𝙟𝙚𝙘𝙩: Offers comprehensive security, governance, and monitoring features, empowering developers to innovate confidently and securely. 𝙊𝙩𝙝𝙚𝙧 𝙏𝙤𝙤𝙡𝙨: Security and governance features can vary widely among other AI orchestration tools, with some focusing more on flexibility and others on robust security measures. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆: 𝙈𝘼𝘾 𝙋𝙧𝙤𝙟𝙚𝙘𝙩: Built on the open-source framework LangChain4j, the MAC Project enhances the development of LLM-driven applications, offering scalability and flexibility in AI orchestration. 𝙊𝙩𝙝𝙚𝙧 𝙏𝙤𝙤𝙡𝙨:Many other tools also support scalability and flexibility, but the level of integration with existing enterprise systems like MuleSoft might differ. 𝗧𝗮𝗿𝗴𝗲𝘁 𝗔𝘂𝗱𝗶𝗲𝗻𝗰𝗲: 𝙈𝘼𝘾 𝙋𝙧𝙤𝙟𝙚𝙘𝙩: Specifically targets MuleSoft developers and enterprises already using MuleSoft's Anypoint Platform, making it a natural extension for those ecosystems. 𝙊𝙩𝙝𝙚𝙧 𝙏𝙤𝙤𝙡𝙨:May target a broader audience across different platforms and industries, not necessarily focused on MuleSoft users If you have any additional observations, I’d love to hear your thoughts. MuleSoft
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Among AI use cases, chatbots, security/privacy, and document analysis are getting real traction. Sentiment analysis, not so much. These findings come from a new survey data about AI adoption from Shawn Rogers of BARC and Merv Adrian of IT Market Strategy. Data/AI experts, what do you think? Are you surprised (like us) that recommendation engines are not more popular? Check out the full report, "Optimizing Your Architecture for AI Innovation," here, with excerpts below. https://lnkd.in/gNjuS2M3 "When it comes to leveraging AI and GenAI, many users ask, "Where should we use AI first?" "Companies feel pressure to rush because of a fear of missing out (FOMO), so they often begin with general AI use cases that appear likely to help them expedite the project and experience an immediate return on investment (ROI) without taking on a lot of risks. "This doesn't mean that these use cases lack value, but they do provide a faster, less complex on-ramp to integrating AI into a company's culture. "The most popular general use cases are AI chatbots and intelligent assistants, with 28% of respondents deploying them and another 24% in proof-of-concept (POC) testing. "Use cases for AI-infused business intelligence and analytics, as well as AI-driven data management tasks and integration, are becoming critical for companies early on. When combining deployed, POC and planned projects in the next 12 months, the data shows that both are top-tier initiatives. "Data management use cases fit this group with 69%, and business intelligence and analytics at 72%. "AI has been used to improve user experience via recommendation engines, automated interactions and personalized data. It's surprising that this use case came in lower, as early adopters have found positive ROI by infusing AI in it. 23% of respondents surveyed plan to adopt this use case in the next 12 months, and the same number are actively testing it. "Across all general use cases, mass personalization is the least popular, with 31% of respondents indicating that it is not part of their AI projects, and only 14% indicating that they have deployed that type of use case." Timm Grosser Wayne Eckerson Eckerson Group Randolf Reiss Paul Sergeant Paul Baier Jacqueline Bloemen Alexander Seeliger Thank you to report sponsors Aerospike, Boomi, Domino Data Lab, InterSystems, and Qlik. #ai #artificialintelligence #genai
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Automation and Integration Specialist | Principal Solution Engineer
2moLiterally watching you break this down further on Prompt Templates as I type this: https://www.youtube.com/watch?v=x5WoSf4EhKY Keep making this stuff digestible! 🤙