Ron Mitchell

AI agents — small, goal-directed AI programs that can research, draft, act, and follow up — are moving fast from proof-of-concept to real business use. Over the last year we’ve seen major platforms (from cloud providers to open-source frameworks) make agents easier to build, connect, and manage. That means teams can automate routine sales and

Quick summary Generative AI (large language models and multimodal agents) is being embedded into robotic process automation (RPA) and workflow platforms. Instead of rigid, rule-based bots, organizations can now build flexible “AI-driven” automations that understand unstructured text, extract data from emails and documents, summarize exceptions, and follow natural-language instructions. The result: faster automation development, broader

Big news: enterprise AI agents — sometimes called copilots — are moving fast from lab demos into real business use. Major cloud vendors and platforms now let companies build custom AI agents that connect to internal data, CRMs, and workflows. That means generative AI can do more than write text: it can pull live data,

AI news snapshot Autonomous AI agents—software that can plan, act, and follow up with little human input—have moved from experiments to real business pilots. Over the past year, more companies have tested agents that handle tasks like sales outreach, customer triage, invoice processing, and monitoring systems. Frameworks such as LangChain, agent templates from major cloud

Short summary Autonomous AI agents and retrieval-augmented generation (RAG) have moved from labs into real business use. Companies now combine large language models with fast access to their own documents and systems so AI can answer questions, draft reports, automate routine tasks, and even trigger workflows — all with more accuracy and business context than

Quick summary Enterprises are increasingly using Retrieval‑Augmented Generation (RAG) paired with vector databases to create private, accurate AI copilots that can answer questions from company documents, CRM data, and operational systems. Instead of asking a large language model (LLM) to rely only on its training, RAG finds the most relevant internal documents, sends them to