Ron Mitchell

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

Autonomous AI agents are moving from curiosities to practical business tools. Over the past year, more companies have started piloting agentic systems that can perform multi-step tasks — from drafting customer responses and updating CRMs to coordinating scheduling and running basic analytics — without constant human direction. The result: faster workflows, fewer manual handoffs, and

Big idea: Autonomous AI agents — sometimes called “copilots” — are moving from demos into real business use. Over the last year, major cloud providers and startups released tools that let AI act on behalf of users: connect to calendars, query databases, run reports, triage emails, and trigger systems via APIs. Open-source frameworks like LangChain,

AI is moving beyond chatbots. The latest wave is autonomous AI agents combined with Retrieval‑Augmented Generation (RAG) — systems that pull from your company’s private data (CRM, docs, tickets, ERP) and generate context-aware guidance, reports, or actions. Businesses are using these agents to automate research, draft proposals, summarize customer history, and even trigger specific operational

Big trend right now: companies are moving from public AI APIs to private, retrieval-augmented language models (RAG) paired with vector databases. Why it matters for business leaders: this approach gives better control over sensitive data, lowers long-term costs for heavy usage, improves accuracy on company-specific questions, and makes AI automation easier to audit and govern.

Quick summary Autonomous AI agents — software that can plan, act, and interact with other systems on its own — are moving from research demos into real business use. Over the past year, major cloud vendors and startups released agent-building tools and templates that let companies automate multi-step tasks: customer triage, report generation, sales outreach,