Ron Mitchell

Quick summary AI agents — autonomous, task-focused assistants built on large language models (LLMs) — are moving from demos into real enterprise use. Paired with Retrieval-Augmented Generation (RAG) and vector databases, these agents can pull company data, run workflows, and produce accurate, context-aware outputs without constant human prompting. That combo is making end-to-end automation, faster […]

Short version: Retrieval-Augmented Generation (RAG) — combining your company data with large language models via vector databases — is rapidly moving from tech pilots into real business use. Companies are launching AI copilots, on-demand reporting, and automated support that use RAG to deliver accurate, context-rich answers while keeping sensitive data private. What happened (clear, business-friendly

Trend summary (what’s happening) Enterprises are moving fast from experimenting with chatbots to deploying Retrieval-Augmented Generation (RAG) backed by vector databases. RAG pairs large language models (LLMs) with searchable embeddings of your internal documents, product data, contracts, and knowledge bases. That mix gives AI answers grounded in company data — reducing hallucinations and making AI

Trending topic: Autonomous AI agents — lightweight AI programs that can plan, take actions, and connect to tools — are moving quickly from research demos to real business use. Platforms and vendors (from workflow tools to major cloud players) are shipping agent features that let AI handle tasks end-to-end: qualify leads, update CRMs, create proposals,

Short summary: Generative AI is moving beyond demos and chat windows into full‑blown work automation. Leading cloud and software vendors have embedded “copilot” features into everyday apps, and custom AI agents (built with tools like LangChain, RAG, and vector databases) are being used to handle real tasks — from drafting sales outreach and automating invoice

Quick summary Autonomous AI agents — tools that can plan, act, and use other software or data sources without constant human prompting — are moving from labs into real business use. Companies are already experimenting with agents for tasks like customer follow-ups, sales outreach, routine IT fixes, and report generation. These agents can speed work,

Quick take: The big trend right now is the rise of AI “agents” — conversational, task-oriented AI that can act on behalf of users to run workflows, pull data, draft responses, and even trigger systems. Tools and model updates from vendors like OpenAI (GPT‑4o), Microsoft (Copilot), and the growing ecosystem of agent frameworks (e.g., LangChain-style

Short summary: Companies are increasingly using Retrieval-Augmented Generation (RAG) — combining large language models (LLMs) with vector databases — to turn scattered documents, chat logs, and manuals into accurate, searchable knowledge. Instead of asking a model to “remember” everything, RAG pulls the most relevant, up-to-date documents into the model’s context before it answers. The result:

AI agents — autonomous software that uses large language models (LLMs) to complete multi-step tasks across apps — are moving from labs into real company workflows. These agents can schedule meetings, summarize customer threads, update CRMs, and even draft parts of reports without constant human prompts. Recent advances in context length, tool integration, and retrieval-augmented

Short summary AI “copilots” powered by private large language models (LLMs) and retrieval-augmented generation (RAG) are moving from experiments to production across enterprises. Companies are combining private LLMs (hosted on cloud or on-premises) with secure knowledge bases, RPA, and analytics to create apps that answer questions from internal data, draft documents, automate approvals, and speed