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Tag: AI Agent

  • Mastering LangChain and LangGraph: From Novice to Expert

    Mastering LangChain and LangGraph: From Novice to Expert You’re about to become an expert in building powerful AI applications using LangChain and LangGraph. These two frameworks are essential tools for anyone looking to go beyond simple prompts and create sophisticated, intelligent systems powered by Large Language Models (LLMs). We’ll start with the fundamentals of LangChain,… Read more

  • Mastering Mosaic AI Vector Search: From Novice to Expert

    Mastering Mosaic AI Vector Search: From Novice to Expert You’re about to embark on a journey from understanding the basics of vector search to becoming an expert in leveraging Databricks’ powerful Mosaic AI Vector Search. This technology is at the heart of making AI truly intelligent, enabling Large Language Models (LLMs) and other AI systems… Read more

  • Mosaic AI Agent Framework vs. LangGraph: A Detailed Comparison

    Mosaic AI Agent Framework vs. LangGraph: A Detailed Comparison When building sophisticated AI agents, developers often face a choice between general-purpose frameworks and platform-specific solutions. This comparison will delve into two prominent options: Databricks‘ Mosaic AI Agent Framework and LangGraph (a module of LangChain), highlighting their strengths, weaknesses, and ideal use cases. Both frameworks aim… Read more

  • Detailed Guide to Using Databricks with Agentic AI

    Detailed Guide to Using Databricks with Agentic AI Databricks, with its unified Lakehouse Platform, offers a robust environment for developing, deploying, and managing Agentic AI systems. Agentic AI involves AI models (often Large Language Models – LLMs) that can reason, plan, use tools, and take autonomous actions. This guide will detail how to leverage Databricks… Read more

  • Top 25 Use Cases for Agentic AI in Retail Banking

    Top 25 Use Cases for Agentic AI in Retail Banking Agentic AI, with its capacity for autonomous reasoning, learning, decision-making, and action, is set to redefine the retail banking landscape. It promises not only to streamline operations and bolster security but also to deliver profoundly personalized customer experiences. I. Enhanced Customer Experience & Personalization 1.… Read more

  • Comprehensive List of Best Practices for Agentic AI

    Agentic AI Best Practices Agentic AI represents a significant leap from traditional generative AI, as it imbues models with the ability to act autonomously, make decisions, and pursue goals. This increased agency introduces a new layer of complexity and risk, necessitating a distinct and comprehensive set of best practices. These practices are designed to ensure… Read more

  • Microsoft AI-Powered Coding Tools

    Microsoft AI Coding Tools Microsoft offers a comprehensive ecosystem of AI-powered coding tools and services, deeply integrated across its developer platforms like Azure and GitHub, and productivity suites like Microsoft 365. These tools leverage advanced AI models, including OpenAI’s GPT series, to enhance productivity, improve code quality, and automate development workflows. 1. GitHub Copilot GitHub… Read more

  • Google’s AI-Powered Coding Tools

    Google AI Coding Tools Google provides a powerful suite of AI-driven coding tools, primarily leveraging its advanced AI models like Gemini, to assist developers throughout the software development lifecycle. These tools are designed to boost productivity, improve code quality, and automate routine tasks, making coding more efficient and accessible. 1. Jules: Your Asynchronous AI Coding… Read more

  • BPM AI Agents Explained

    BPM AI Agents Explained for Novices (Detailed) Imagine the inner workings of a company as a network of interconnected pathways – these pathways represent the various business processes that drive operations, from fulfilling customer orders to managing supply chains and handling internal approvals. Business Process Management (BPM) is the discipline of understanding, designing, executing, documenting,… Read more

  • Agentic AI: The Critical Role of Explainable AI (XAI)

    Agentic AI: The Critical Role of Explainable AI (XAI) Agentic AI promises a significant evolution in how artificial intelligence systems operate, enabling autonomous, intelligent, and adaptive behavior. However, the full potential and responsible deployment of these powerful systems hinge on our ability to understand their decision-making processes. This is where Explainable AI (XAI) becomes not… Read more

  • A2A (Agent-to-Agent) vs. MCP (Model Context Protocol)

    A2A (Agent-to-Agent) vs. MCP (Model Context Protocol) A2A (Agent-to-Agent) vs. MCP (Model Context Protocol) Here’s a comparison between A2A (Agent-to-Agent Protocol) and MCP (Model Context Protocol) in the context of AI agents: A2A (Agent-to-Agent Protocol): Primary Focus: Standardizing communication and interoperability between different AI agents, regardless of their origin or framework. Aims to give AI… Read more

  • How SAP and Oracle Can Use Agentic AI

    How SAP and Oracle Can Use Agentic AI SAP and Oracle, as leading enterprise software providers, are actively integrating Agentic AI capabilities into their platforms to enhance organizational productivity across various business functions. Here’s how they can leverage this transformative technology: SAP’s Use of Agentic AI: SAP is embedding “Business AI” across its portfolio, which… Read more

  • Various flavors of Retrieval Augmented Generation (RAG)

    Various Types of RAG The field of Retrieval-Augmented Generation (RAG) is rapidly evolving, with several variations and advanced techniques emerging beyond the basic “naive” RAG. I. Based on the Core RAG Pipeline 1. Naive/Standard RAG The user’s query is directly used to retrieve relevant documents, and these are passed to the LLM for generation. Use… Read more

  • Exploring LangSmith Observability in Detail

    LangSmith Observability in Detail LangSmith provides comprehensive observability for your LLM applications, offering detailed insights into the execution flow, performance, and outputs of your chains, agents, and tools. It helps you understand what’s happening inside your LLM application, making it easier to debug, evaluate, and improve its reliability and quality. 1. Tracing: End-to-End Visibility Detailed… Read more

  • Salesforce Agentic AI: A Comprehensive Overview

    Salesforce Agentic AI: A Comprehensive Overview Salesforce Agentic AI represents a significant evolution in how artificial intelligence is integrated into the Salesforce platform. Moving beyond simple automation and predictive analytics, Agentic AI aims to create intelligent, autonomous agents capable of understanding complex goals, planning multi-step actions, and executing tasks on behalf of users. This detailed… Read more

  • Sample Agentic AI Orchestrating Complex Cybersecurity Workflow in GCP

    Agentic AI Orchestrating Complex Workflow in GCP (Sample) Agentic AI Orchestrating Complex Workflow in GCP (Sample) This sample outlines a conceptual implementation of an agentic AI system orchestrating a complex cybersecurity workflow in Google Cloud Platform (GCP), focusing on automatically investigating and responding to potential phishing incidents reported by employees. Conceptual Architecture +———————+ +———————+ +——————–+… Read more

  • Platforms for Integrating Blockchain and AI

    Blockchain and AI Platforms Several platforms are emerging that facilitate the integration of blockchain and artificial intelligence, enabling the development of novel and powerful applications. Here are a few notable examples with their key features: 1. Oraichain (ORAI) Oraichain is a Layer 1 blockchain focused on AI and oracles. It aims to be the foundational… Read more

  • Real-time Recommendation Engine AI Agent on AWS

    Real-time Recommendation Engine AI Agent on AWS Implementing a real-time recommendation engine AI agent on AWS requires a robust and scalable architecture. Here are implementation examples for key services in the tech stack: 1. Real-time Data Ingestion (Amazon Kinesis Data Streams): You would use the AWS SDK (Boto3 in Python) in your application backend to… Read more

  • AI Agent with Short-Term Memory on Google Cloud

    AI Agent with Short-Term Memory on Google Cloud Creating AI agents capable of handling complex tasks and maintaining context requires implementing short-term memory, often referred to as “scratchpad” or working memory. This allows agents to temporarily store and process information relevant to their immediate goals. Google Cloud Platform (GCP) offers a range of services that… Read more

  • AI Agent with Long-Term Memory on Google Cloud

    AI Agent with Long-Term Memory on Google Cloud Building truly intelligent AI agents requires not only short-term “scratchpad” memory but also robust long-term memory capabilities. Long-term memory allows agents to retain and recall information over extended periods, learn from past experiences, build knowledge, and personalize interactions based on accumulated history. Google Cloud Platform (GCP) offers… Read more

  • AI Agent with Long-Term Memory on Azure

    AI Agent with Long-Term Memory on Azure Building truly intelligent AI agents requires not only short-term “scratchpad” memory but also robust long-term memory capabilities. Long-term memory allows agents to retain and recall information over extended periods, learn from past experiences, build knowledge, and personalize interactions based on accumulated history. Microsoft Azure offers a comprehensive suite… Read more

  • AI Agent with Short-Term Memory on Azure

    AI Agent with Short-Term Memory on Azure Creating AI agents capable of handling complex tasks and maintaining context requires implementing short-term memory, often referred to as “scratchpad” or working memory. This allows agents to temporarily store and process information relevant to their immediate goals. Microsoft Azure offers a range of services that can be utilized… Read more

  • AI Agent with Long-Term Memory on AWS

    AI Agent with Long-Term Memory on AWS Building truly intelligent AI agents requires not only short-term “scratchpad” memory but also robust long-term memory capabilities. Long-term memory allows agents to retain and recall information over extended periods, learn from past experiences, build knowledge, and personalize interactions based on accumulated history. Amazon Web Services (AWS) offers a… Read more

  • AI Agent with Short-Term Memory on AWS

    AI Agent with Short-Term Memory on AWS In the realm of Artificial Intelligence, creating agents that can effectively interact with their environment and solve complex tasks often requires equipping them with a form of short-term memory, also known as “scratchpad” or working memory. This allows the agent to temporarily store and process information relevant to… Read more

  • AI Agent with Scratchpad Memory on AWS

    AI Agents with Scratchpad Memory on AWS AI agents equipped with “scratchpad” memory, or short-term working memory, significantly enhance their capabilities by allowing them to temporarily store and process information relevant to their current tasks. This enables them to handle complex scenarios, maintain context across interactions, and reason more effectively. This article explores the use… Read more

  • Processing Data Lakehouse Data for Agentic AI

    Processing Data Lakehouse Data for Agentic AI Processing Data Lakehouse Data for Agentic AI Agentic AI, characterized by its autonomy, goal-directed behavior, and ability to interact with its environment, relies heavily on data for learning, reasoning, and decision-making. Processing data from a data lakehouse for such AI agents requires careful consideration of data quality, relevance,… Read more

  • GCP AI Offerings – Details & Use Cases

    GCP AI Offerings – Details and Use Cases GCP AI Offerings – Details and Use Cases Google Cloud Platform (GCP) offers a comprehensive suite of AI and Machine Learning services, ranging from pre-trained APIs to platforms for building and deploying custom models, including cutting-edge Generative AI capabilities. Generative AI Services: Vertex AI Gemini Models Access… Read more

  • GraphQL vs RESTful for Agentic AI

    GraphQL vs RESTful for Agentic AI Both RESTful and GraphQL APIs can be used to build agentic AI systems, and the choice between them depends on the specific requirements and characteristics of the AI agent and the systems it interacts with. Here’s a comparison of their suitability: RESTful APIs for Agentic AI: Pros: Simplicity and… Read more

  • Leveraging Kafka for Agentic AI Systems

    Apache Kafka, a distributed streaming platform, offers significant advantages for building and deploying agentic AI systems. Its core strength lies in its ability to handle high-throughput, real-time data streams reliably, making it an excellent choice for managing the dynamic interactions and data flow inherent in intelligent agents. Key Use Cases of Kafka in Agentic AI:… Read more

  • Leveraging Redis for Agentic AI

    Redis, a fast, in-memory data structure store, offers significant advantages when building and deploying agentic AI systems. Its speed and versatility make it ideal for managing the memory and state necessary for intelligent and context-aware agents. Key Use Cases of Redis in Agentic AI: Memory Management Semantic Caching Cache embeddings of user queries and corresponding… Read more