In this tutorial, we explore the advanced capabilities of Google’s Agent Development Kit (ADK) by building a multi-agent system equipped with specialized roles and tools. We guide you through creating agents tailored for tasks such as web research, mathematical computation, data analysis, and content creation. By integrating Google Search, asynchronous execution, and modular architecture, we…
In this tutorial, we walk through the creation of an advanced AI evaluation framework designed to assess the performance, safety, and reliability of AI agents. We begin by implementing a comprehensive AdvancedAIEvaluator class that leverages multiple evaluation metrics, such as semantic similarity, hallucination detection, factual accuracy, toxicity, and bias analysis. Using Python’s object-oriented programming, multithreading…
As large language models (LLMs) evolve from simple text generators to agentic systems —able to plan, reason, and autonomously act—there is a significant increase in both their capabilities and associated risks. Enterprises are rapidly adopting agentic AI for automation, but this trend exposes organizations to new challenges: goal misalignment, prompt injection, unintended behaviors, data leakage,…
The demand for AI-powered coding tools has exploded—with open-source alternatives now rivaling commercial solutions like Cursor in features, flexibility, and privacy. If you’re seeking a powerful, cost-effective, and open-source code assistant, consider these top picks for 2025: 1. Zed Zed is a high-performance, open-source code editor designed for both humans and AI collaboration. Built by the…
Microsoft has taken a major leap into the future of web browsing with the launch of Copilot Mode in Edge, positioning it as the company’s first real step toward an AI-native browser. This marks a pivotal moment not just for Edge, but for the entire concept of what a browser can be in the era…
The White House just released the U.S. AI Playbook—formally titled “America’s AI Action Plan”—a sweeping, high-impact federal strategy that clarifies one thing: the United States is going all in on artificial intelligence. Whether you’re in Silicon Valley, leading a Fortune 500, or managing a critical government agency, the message is unambiguous: scale AI fast, dismantle…
In this tutorial, we walk through the complete implementation of an advanced AI agent system powered by Nomic Embeddings and Google’s Gemini. We design the architecture from the ground up, integrating semantic memory, contextual reasoning, and multi-agent orchestration into a single intelligent framework. Using LangChain, Faiss, and LangChain-Nomic, we equip our agents with the ability…
In this tutorial, we guide you through the development of an advanced Graph Agent framework, powered by the Google Gemini API. Our goal is to build intelligent, multi-step agents that execute tasks through a well-defined graph structure of interconnected nodes. Each node represents a specific function, ranging from taking input, performing logical processing, making decisions,…
The importance of memory in AI agents cannot be overstated. As artificial intelligence matures from simple statistical models to autonomous agents, the ability to remember, learn, and adapt becomes a foundational capability. Memory distinguishes basic reactive bots from truly interactive, context-aware digital entities capable of supporting nuanced, humanlike interactions and decision-making. Why Is Memory Vital in AI…
In this tutorial, we build a GPU‑capable local LLM stack that unifies Ollama and LangChain. We install the required libraries, launch the Ollama server, pull a model, and wrap it in a custom LangChain LLM, allowing us to control temperature, token limits, and context. We add a Retrieval-Augmented Generation layer that ingests PDFs or text,…