Pritesh Sonu

Two years ago, "agentic AI" was a phrase you'd mostly hear at a conference. In 2026, it is how a growing number of US startups actually run their day-to-day work. Support tickets get triaged before a human even opens the inbox. Sales leads get researched, scored, and queued for outreach overnight. Code gets written, tested, and flagged for review while the engineering team sleeps. This isn't a future scenario. It's happening right now, and it's happening faster inside startups than almost...

Executive Summary: MLOps (Machine Learning Operations) is the operational discipline of automating the lifecycle of machine learning and AI models—from data ingestion and CI/CD deployment to continuous monitoring and retraining. For mid-market enterprises, right-sized MLOps bridges the gap between experimental AI prototypes and production-grade revenue engines, preventing model drift and costly silent failures without requiring dedicated enterprise infrastructure teams. Picture this. A...

Quick answer: Traditional automation follows fixed rules and breaks on any change. RPA mimics human clicks on a screen and breaks when the screen changes. Agentic AI, powered by Large Language Models (LLMs), pursues a goal, reasons through obstacles, and adapts when things change — without needing to be reprogrammed. The era of enterprise automation is moving at supersonic speed. Not long ago, the phrase "business automation" simply meant writing a script or setting up a software bot to clear...