AI has traditionally been used to answer questions, generate content, classify information, or make predictions. AI agents are different. An AI agent is designed to take a goal and work toward completing it. It can decide which information it needs, choose a tool, perform an action, inspect the result, change its approach when something fails, and continue until the task is complete or until it needs a human to take over. That distinction is becoming especially visible in sof

Remember when moving everything to "the cloud" was the ultimate tech upgrade? You finally packed up all those clunky on-site servers, sent your data off to a neat, invisible digital storage unit, and breathed a sigh of relief. But then, Artificial Intelligence exploded onto the scene, and suddenly, the old cloud rules went out the window. AI doesn't just passively store data; it actively devours it, requiring massive computing power and handling highly sensitive, proprietary

Building enterprise AI applications has historically suffered from the "N × M integration problem." If you have M large language models (LLMs) and N internal tools (databases, APIs, file systems), developers had to build custom connectors for every possible combination. In late 2024, Anthropic solved this by introducing the Model Context Protocol (MCP). Think of MCP as the "USB port for AI" an open, standard protocol that allows any AI model to securely connect to any externa

I was exploring a core concept about AI and AI Agents and frequently came across ReAct. As I kept seeing it, I thought to take a deeper dive and below is my understanding." Today’s AI models can search the web, use tools, solve multi-step problems, write code, and even interact with software environments. One of the most important concepts behind this evolution is ReAct. ReAct combines reasoning and action into a single framework, enabling AI systems to think step by step whi

You can have the most capable model, a well-engineered harness, and a solid product vision - and still have no idea if your agent is actually working. That's the problem evals solve. An evaluation ("eval") is a test for an AI system: give an AI an input, then apply grading logic to its output to measure success. Good evaluations help teams ship AI agents more confidently. Without them, it's easy to get stuck in reactive loops - catching issues only in production, where fixing

Every AI coding tool faces the same fundamental problem: your codebase is too large to fit in a context window, but the model needs to understand it to help you. How a tool solves that problem shapes everything about what it can and can't do. GitHub Copilot and Claude Code solve it in opposite ways - and the gap between them isn't a matter of polish or features. It's a genuine architectural disagreement. How Copilot Works: Index First, Retrieve Later If your repository is hos

Most AI demos look impressive. Most AI agents in production quietly fail. The difference is rarely the model — it's everything built around it. Agentic AI refers to systems where a model takes autonomous, multi-step actions to complete a goal — browsing the web, writing and running code, calling APIs — acting, observing the result, and acting again, often over hours. Harness engineering is the discipline of building the system that makes those actions reliable. As LangChain p

Google Launched its most capable model starting in December 2023. On 13 December Gemini API was made accessible to developers and businesses to build on top of Gemini. Today, we will see how we can get access to the free Gemini Pro API Key and also will make a curl request to API using an API key. To get the Gemini Pro API key go to https://aistudio.google.com/ this will take you to Google AI Studio. In Google AI Studio, on the side, you will see something similar like below