Ron Mitchell

Short summary Retrieval-Augmented Generation (RAG) — pairing large language models (LLMs) with vector search and knowledge bases — is now a mainstream way businesses build AI-powered apps. Instead of asking an LLM to invent answers from scratch, RAG pulls relevant documents, product data, and policies from a vector database, then feeds that context to the […]

AI trend summary (what’s happening) AI “agents” — autonomous or semi-autonomous AI programs that can perform multi-step tasks across apps and data sources — are moving from experiments to real business use. From smart sales assistants that qualify leads and update CRMs to finance agents that prepare month-end reports, companies are using agents to automate

Autonomous AI agents — software that can take multi-step actions, learn from feedback, and coordinate across systems — are moving from research demos into real business use. Companies are already using agent frameworks (think LangChain-style agents, RAG-enabled assistants, and workflow orchestration tools) to automate complex tasks like contract review, multi-system reconciliations, proactive customer outreach, and

The trend Retrieval-Augmented Generation (RAG) — combining large language models (LLMs) with fast vector search over your documents — is one of the biggest AI stories for businesses right now. Enterprises are moving beyond one-off chatbots to indexed, secure knowledge layers so AI gives accurate, context-aware answers based on company data. Why it matters to

AI agents — software that can act autonomously to complete tasks, pull data, and interact with systems — are moving from labs into everyday business use. Over the last year this trend has accelerated: companies are combining agent frameworks with RPA, CRM connectors, and LLMs to automate multi-step workflows like lead qualification, invoice reconciliation, and

Big idea in plain terms: Autonomous AI agents — systems that can perform multi-step tasks by themselves — are moving from demos into real business use. Paired with retrieval-augmented generation (RAG) and vector databases (which let models fetch company knowledge safely), these agents can automate complex workflows like invoice processing, contract review, customer triage, and

Short summary Retrieval‑Augmented Generation (RAG) — the technique that combines large language models (LLMs) with fast vector search over company documents — is moving from proof‑of‑concept to production across industries. By storing knowledge as embeddings in a vector database, businesses deliver more accurate, up‑to‑date answers, protect sensitive data, and reduce hallucinations compared with using an

Quick summary Autonomous AI agents — think of them as smart software helpers that can plan, act, and iterate on tasks — are moving from experiments into real business use. When you combine these agents with retrieval-augmented generation (RAG) — a technique that lets models use a company’s own documents, databases, and internal knowledge —

Quick take: Businesses are rapidly building AI “copilots” that use Retrieval-Augmented Generation (RAG) and vector databases to answer questions from company data in real time. Major cloud and AI vendors (and many startups) are packaging tools that make it easier to connect your documents, CRM, and knowledge bases to large language models — so the

Quick summary AI agents — autonomous, task-focused systems built from large language models (LLMs), tool integrations, and retrieval-augmented generation (RAG) — are rapidly moving from experiments into real business use. Over the last year we’ve seen major platform pushes (enterprise copilots, function-calling APIs, and agent frameworks like LangChain and Microsoft/Google copilot toolsets) and growing adoption