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Showing posts with the label AI agents

What an AI Code Assistant Is & How It Works (2025 Guide)

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  Discover what an AI code assistant is, how it works, and why it’s transforming the future of software development. Learn about LLMs, machine learning , code generation, debugging, automation, and real-world use cases for developers in 2025.  The Rise of AI Coding Assistants The world of software development has been transformed by AI-powered code assistants . These tools—such as GitHub Copilot , ChatGPT , Codeium, AWS CodeWhisperer , Tabnine, and many others—have become essential helpers for developers, engineers, data scientists, and even beginners who are just learning to code. In 2025, AI code assistants are no longer “optional productivity boosters.” They have evolved into smart collaborators , capable of: Writing code from natural language Suggesting solutions instantly Fixing bugs Generating documentation Reviewing pull requests Recommending best practices based on code context Acting as full-fledged pair programmers To understand the power o...
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Training Agentic AI — How to Build Intelligent Agents That Think, Act & Learn

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“ Training Agentic AI : Learn how to build, train and deploy autonomous AI agents — from design patterns , data pipelines, reinforcement learning , tool-use , multi-agent systems and real-world best-practices.” Artificial Intelligence has reached a new frontier: not just systems that respond and generate content, but systems that plan, execute, adapt and learn autonomously — what we’ve called in the previous post “Agentic AI”. In this article we’ll go deeper into how to train such systems: from architecture, data, algorithms, tools, deployment, monitoring to ethics, so you (as a technologist, researcher, developer or business leader) can understand how to build or oversee an agentic AI workflow.  Why “Training” Matters for Agentic AI When we talk about “training” in the context of traditional machine learning , we often mean fitting a model to labelled data, tuning hyper-parameters, then deploying. But for agentic AI the training (and the ongoing learning) becomes far more comp...
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Agentic AI in 2025

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  Introduction “ Agentic AI : Explore how this next-generation form of artificial intelligence empowers systems to set goals, plan, act and learn — beyond simple prompt-response. Learn how it works, real-world applications, benefits, risks and what it means for business and society.” Artificial Intelligence has evolved rapidly in recent years — from rule-based automation, to machine learning , to generative AI that can write text, generate images, code snippets, and more. But a new frontier is emerging: what’s often called Agentic AI . Unlike traditional AI or even most generative models, agentic AI systems do not simply wait for a prompt and respond; they set goals, plan multi-step actions, act on their own (or with limited supervision), adapt and learn . In this blog post we will unpack what Agentic AI is, how it differs from other AI paradigms, how it works under the hood, where it’s being applied today, what benefits it promises, what risks and challenges it brings, and how...
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What is an AI Agent? A Complete Guide with Concepts, Examples, and Implementation

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  Artificial Intelligence (AI) is no longer a futuristic concept; it’s already embedded in the tools and services we use daily. From virtual assistants like Siri and Alexa to customer service chatbots and autonomous vehicles, AI has transformed how humans interact with technology. At the heart of these intelligent systems lies a powerful concept: the AI Agent . In this article, we’ll explore what an AI agent is, how it works, real-world applications, types of AI agents, and how you can build your own AI agent . Whether you are a student, researcher, developer, or simply curious about AI, this guide will give you a complete overview. 1. What is an AI Agent? In simple terms, an AI agent is a system that perceives its environment, processes information, and takes actions to achieve specific goals . Think of it as a digital entity that senses, thinks, and acts. Perception: The agent gathers data from its environment using sensors (e.g., camera, microphone, or data APIs). R...
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