Ron Mitchell

Short summary There’s a fast-growing wave in AI: practical “AI agents” built on large language models (LLMs) plus tool access, retrieval, and orchestration. Frameworks like LangChain, LlamaIndex and Microsoft’s Semantic Kernel, plus vector databases and RAG (retrieval-augmented generation), let teams create agents that read my files, call your apps, run workflows, and answer questions — […]

Big picture: Businesses are moving beyond generic chatbots to knowledge-powered AI assistants. The recent surge in using Retrieval-Augmented Generation (RAG) — pairing large language models with vector databases that store company documents, CRM records, and product specs — is a practical breakthrough. RAG reduces hallucinations, gives models up-to-date answers, and makes outputs traceable to source

Autonomous AI agents — smart, goal-driven software that can plan, act, and learn — are moving fast from labs into real business use. Companies are now using agents to handle scheduling, customer replies, sales outreach, invoice reconciliation, and routine IT tasks. That shift is lowering costs and freeing teams to focus on higher-value work. Why

Big idea in the news: companies are moving beyond generic chatbots to private LLMs combined with Retrieval-Augmented Generation (RAG). Instead of asking a public model to guess answers, businesses pair a private model with a searchable index of their own documents (vector database). The model pulls verified facts from company data, then generates an answer

AI trend in focus Companies are rapidly adopting Retrieval-Augmented Generation (RAG) to build AI assistants that answer questions from private documents instead of guessing. RAG combines vector search (storing document embeddings) with a large language model that generates answers using only retrieved, relevant content. This approach reduces hallucinations, improves accuracy, and keeps sensitive data inside

Quick take: AI agents that combine large language models with Retrieval-Augmented Generation (RAG) and vector databases are moving from experiments to real business deployments. Companies are using these “knowledge agents” to power faster customer support, smarter sales assistants, and more efficient internal workflows — with measurable time and cost savings. What’s happening (short, clear summary):

Quick summary – Recent months have seen a surge in AI agents and enterprise “copilots” — purpose-built AI tools that can read documents, run workflows, pull data from business systems, and take actions autonomously or semi-autonomously. – These agents pair large language models with retrieval (RAG), connectors to CRMs/ERP/BI, and simple orchestration so teams can

Autonomous AI agents — software that senses, plans, and acts across apps with little human direction — are moving from labs into the boardroom. Over the last year leading cloud and SaaS vendors added agent-style features to their suites, startups launched agent orchestration platforms, and pilots inside sales, support, and ops teams are already showing

Lately, a practical shift is happening in enterprise AI: companies are pairing large language models (LLMs) with retrieval systems and vector databases — a setup known as Retrieval‑Augmented Generation (RAG). Instead of asking a model to memorize everything, RAG pulls the most relevant, up‑to‑date company data into the prompt. The result: more accurate answers, better

Meta: Autonomous AI agents (AutoGPT-style assistants) are rising fast. Business leaders can use them for sales outreach, customer support, and process automation — but need integration, data grounding, and governance. Here’s what to know and how RocketSales helps. The trend in one line Autonomous AI agents — AI “workers” that can plan, fetch data, take