Ron Mitchell

Quick summary AI is moving from chat and content generation to autonomous agents — software that uses large language models (LLMs), retrieval-augmented generation (RAG), and APIs to plan, act, and complete multi-step business tasks with less human supervision. Examples include agents that draft and send outreach, run complex data pulls and reports, troubleshoot IT incidents, […]

Summary: Autonomous AI agents — sometimes called “AI agents” or “autonomous copilots” — are moving fast from research demos into real business use. These systems combine large language models, tools (calendars, email, CRMs), and business rules to carry out multi-step tasks with little human supervision. Recent pilots at enterprises show agents scheduling meetings, drafting outreach,

Short summary (LinkedIn-ready) The European Union’s AI Act is poised to reshape how companies buy, build, and run AI. It groups AI systems by risk (unacceptable, high, limited, minimal) and sets clear rules for high‑risk uses such as hiring, credit decisions, critical infrastructure, and biometric ID. That means stronger requirements for documentation, data quality, transparency,

Quick overview AI “agents” — autonomous systems that can plan, act, and execute multi-step tasks across apps and data sources — are moving from demos into real business use. Built with large language models, connectors, and orchestration layers (e.g., LangChain-style frameworks and commercial agent platforms), these agents can handle things like research and summarization, ticket

Quick news snapshot: There’s been a clear shift in 2024–2025: companies are moving from public, one-size-fits-all AI services to private, fine-tuned large language models (LLMs) hosted on secure cloud or on-premises environments. This trend is driven by concerns about data privacy, cost predictability, and the need for specialized behavior from AI (industry vocabulary, SOPs, compliance

AI agents — autonomous, goal-directed AI programs that can take actions across apps and data — are moving fast from pilots into real business processes. Companies are already using agents to handle tasks like customer triage, invoice processing, lead qualification, and automated reporting. The result: faster cycle times, fewer repetitive tasks, and teams focused on

Meta description (SEO-friendly): Autonomous AI agents — powered by large language models, retrieval-augmented generation (RAG), and agent frameworks — are enabling businesses to automate end-to-end workflows, boost productivity, and reduce costs. Learn how to pilot, integrate, and govern AI agents safely with RocketSales. Short summary (for LinkedIn and business audiences) AI “agents” — software that

AI trend summary (short and clear) AI “agents” — autonomous, goal-driven AI tools that can plan, act, and interact with systems — are moving from labs into real business use. Instead of one-off chat answers, modern agents can run multi-step tasks: schedule meetings, summarize research, update CRMs, or run complex customer workflows across apps. Tools

The trend Open‑source large language models (LLMs) like Llama 2 and Mistral have moved from research curiosities to practical tools for businesses. Companies are adopting these models because they offer lower per‑query cost, direct control over data and weights, and easier on‑prem or private‑cloud deployment. At the same time, tooling (Hugging Face, LangChain, Ollama, etc.)

Quick summary – Trend: Businesses are rapidly adopting Retrieval-Augmented Generation (RAG) — using vector databases to feed large language models with company-specific knowledge — to build smarter, context-aware AI assistants. – Why it matters: RAG reduces hallucinations, improves answer accuracy, and lets LLMs use your documents, CRM data, and SOPs to produce up-to-date, auditable responses.