Here is short info about post: Decision Support Systems (DSS) have long been built on the premise that a human is the ultimate decision-maker. These systems collect, process, and present data in a way that aligns with human cognitive processes—visual dashboards, scenario simulations, what-if analyses, and interactive reports. However, in the era of AI agents, large language models, and autonomous systems, an important shift is emerging: what if the final consumer of the DSS is no longer human, but an AI agent? In this article, we’ll ... Rethinking DSS Systems: From Human Decision-Making to AI as the Final Consumer

Here is short info about post: Artificial Intelligence agents are rapidly evolving from simple task executors to autonomous systems capable of intelligent decision-making and dynamic adaptation. However, for AI agents to truly reason and act in a human-like manner, they require more than access to isolated APIs—they need contextual awareness, tool access, memory, and the ability to understand intent across multiple turns. This is where the Model Context Protocol (MCP) introduces a paradigm shift. MCP adds a dynamic, intelligent layer over traditional APIs, enabling AI agents ... How MCP Adds A Dynamic Layer Over Traditional APIs, Enabling AI Agents To Access Tools, Context, And Real-Time Data For Smarter, More Adaptive Behavior

Here is short info about post: Speech recognition systems have undergone a dramatic transformation in the past decade, moving from brittle command-and-control interfaces to intelligent, human-like assistants. At the heart of this revolution is Natural Language Processing (NLP)—a field of artificial intelligence that enables machines to understand, interpret, and generate human language. When integrated with speech recognition pipelines, NLP substantially boosts system accuracy, contextual awareness, and multilingual capability. In this article, we’ll explore how NLP empowers speech recognition systems, with practical insights and code examples using ... How Natural Language Processing Enhances Speech Recognition Systems for Improved Accuracy, Context Understanding, and Multilingual Support

Here is short info about post: The software testing landscape is rapidly evolving. While traditional approaches like on-premise virtual machines and static Tosca DEX (Distributed Execution) Agents have served their purpose, they now often fall short in agility, scalability, and cost-efficiency. Enter Tricentis’ Elastic Execution Grid (E2E) and Flexible Execution Agents — a modern alternative that replaces manual VM upkeep with cloud-native orchestration. This article outlines a step-by-step migration strategy, shares the benefits of E2E, and includes practical implementation examples to guide you in transitioning your ... Migrating From Local Tosca DEX Test Agents To The Elastic Execution Grid (E2E) With Flexible Cloud Agents And Replacing Manual VM Upkeep

Here is short info about post: In an era of exponential technological growth, the fusion of natural language processing and software engineering has unlocked new paradigms in development methodology. One of the most transformative of these is Vibe Coding—a fluid, AI-driven software development style that empowers developers and non-technical users alike to build, automate, and solve complex problems simply by expressing their intent in natural language. Vibe Coding transcends conventional paradigms like low-code or no-code. It’s not just a new tool—it’s a mindset: leveraging AI copilots, ... Vibe Coding: An AI-Driven, Natural Language Approach To Rapid Software Development, Enabling Fast Prototyping, Automation, And Creative Problem-Solving

Here is short info about post: Modern identity systems require intelligent, context-aware, and secure communication between authentication components. Model Context Protocol (MCP) enables AI agents and identity servers like Keycloak to share dynamic session context and decision boundaries. In this guide, you’ll learn how to build an MCP server for Keycloak using Quarkus, a fast Java framework for microservices, and the Goose CLI, a lightweight tool for managing MCP configurations and state. We’ll cover: What MCP is and why it matters The architecture of an MCP-enabled ... How To Create A Model Context Protocol (MCP) Server For Keycloak Using Quarkus And Goose CLI

Here is short info about post: Progressive delivery has become an essential deployment strategy for modern Kubernetes environments. It enables engineering teams to incrementally release new application versions, monitor their performance, and roll back quickly if something goes wrong. This approach reduces risk, enhances control, and supports continuous delivery pipelines. In this article, we’ll explore how Argo Rollouts, a Kubernetes controller for progressive delivery, can be effectively combined with Datadog, a leading observability platform, to enhance deployment safety and visibility. We’ll cover core concepts, integration steps, ... How Progressive Delivery In Kubernetes Environments Can Be Enhanced Using Argo Rollouts In Combination With Datadog Metrics

Here is short info about post: Automating ETL (Extract, Transform, Load) processes for settlement files—such as bank statements, payment reconciliations, and transaction logs—is crucial for timely and error-free financial operations. Traditional manual data processing is time-consuming, error-prone, and lacks scalability. AWS offers a powerful combination of services like Amazon S3, AWS Glue, AWS Lambda, and AWS Step Functions to build scalable, serverless data pipelines that automate these ETL workflows end-to-end. This article delves into how you can use these AWS services to automate the processing of ... How AWS Data Pipelines Automate ETL for Settlement Files Using S3, Glue, Lambda, and Step Functions

Here is short info about post: In an era where digital transformation is reshaping industries, AI Twins are emerging as powerful tools for personal productivity, automation, and simulation. An AI Twin—or digital twin powered by artificial intelligence—is a virtual replica of a human or a system that can autonomously make decisions, learn behaviors, and interact with the environment in meaningful ways. This article provides a practical and conceptual roadmap to build your own AI Twin, complete with implementation details using Python, vector databases, and open-source language ... Building an AI Twin: A Comprehensive Guide with Coding Examples

Here is short info about post: Salesforce is one of the world’s most popular Customer Relationship Management (CRM) platforms, powering everything from sales and service to marketing and community experiences. However, its true power is often unlocked through API integration, enabling organizations to create seamless workflows, synchronize data, and automate tasks across disparate systems. This article explores key use cases, best APIs (REST, SOAP, Bulk), and implementation tips, with code examples to guide developers and architects through successful integration strategies. Why Salesforce API Integration Matters Modern ... Salesforce API Integration: Use Cases, API Types, and Implementation Tips with Code Examples

Here is short info about post: Artificial Intelligence (AI) is transforming how systems adapt, learn, and respond to changing business conditions. However, to deploy AI effectively at scale, especially in real-time or near-real-time systems, traditional monolithic architectures often fall short. Instead, event-driven systems provide the reactive backbone that can handle distributed, scalable workflows. Combining this with the Model Context Protocol (MCP) enables AI models to manage conversational or task-specific memory across decentralized events — delivering contextually aware and intelligent automation at scale. In this article, we’ll ... How To Build Scalable And Reliable AI-Driven Workflows Using Event-Driven Systems, Combined With The Model Context Protocol

Here is short info about post: Machine learning (ML) models and IoT (Internet of Things) devices are a natural match. IoT devices collect a tremendous amount of data from the physical world, while ML models can analyze this data to derive actionable insights. However, deploying ML models on IoT devices in environments where cloud infrastructure is restricted or not desired introduces a unique set of challenges. In this article, we explore how to deploy ML models directly to IoT devices using DevOps principles without managing traditional ... Deploying Machine Learning Models on IoT Devices Using DevOps Practices Without Managing Cloud Infrastructure