Ron Mitchell

Short summary (news + trend) – What’s happening: In 2024 we’re seeing a clear surge in autonomous AI agents — software that can plan, act, and complete multi-step tasks without constant human direction. Frameworks like LangChain and Auto-GPT, plus major vendor tools and “copilot” offerings from big cloud providers, have made it easier to build […]

Short summary Businesses are increasingly pairing large language models with Retrieval-Augmented Generation (RAG) and vector databases to get accurate, up-to-date answers from their own data. Instead of asking a model to rely solely on pre-trained knowledge — which can be out-of-date or off-target — RAG retrieves relevant documents, passages, or data points, then uses the

Quick update for business leaders: Autonomous AI agents—software that uses large language models to plan and run multi-step tasks across apps—are moving from demos into real business use. New agent frameworks, plugin ecosystems, and tighter integrations with CRMs, ERPs, and BI tools are letting companies automate complex work like lead qualification, compliance checks, proposal drafting,

Quick summary Enterprises are rapidly adopting Retrieval-Augmented Generation (RAG) and vector databases to build private, accurate AI assistants. Instead of relying only on general-purpose LLM outputs (which can hallucinate or expose sensitive info), companies are combining local knowledge stores, embeddings, and secure retrieval to give models real-time access to verified company data. This trend is

Quick summary AI agents and enterprise “copilots” are moving from demos into real business use. Companies are deploying autonomous or semi-autonomous AI agents that handle multi-step tasks — from triaging support tickets and drafting sales outreach, to assembling financial reports and automating back-office work. These systems pair large language models with retrieval-augmented generation (RAG), vector

Why this matters now AI agents—autonomous workflows powered by large language models—are moving from demos into real business use. Companies are combining agents with Retrieval-Augmented Generation (RAG) and vector databases to give LLMs access to company knowledge (policies, contracts, CRM records, product specs). The result: faster reporting, smarter customer responses, and automated routine processes that

Autonomous AI agents—software that plans, decides, and acts across apps—are no longer sci‑fi. Modern agents combine large language models with retrieval‑augmented generation (RAG), vector databases, and API connectors to complete multi‑step tasks across CRM, ERP, email, and reporting systems. That makes them a powerful new tool for automating customer service, order‑to‑cash, sales outreach, and routine

Big update: The European Union’s AI rules have moved from proposal to enforcement-ready reality. The new law creates strict rules for “high‑risk” AI systems, bans certain harmful practices, and requires transparency for AI that interacts with people or generates content. That means companies using AI — from customer service bots to hiring tools and automated

Big idea — Retrieval-Augmented Generation (RAG) and vector databases are changing how businesses use large language models (LLMs). Instead of asking an LLM to “remember” everything, RAG pulls relevant, company-specific documents from a searchable vector database and feeds that context into the model. The result: faster answers, fewer hallucinations, and AI that can safely work

Headline summary Autonomous AI agents — software that can act on behalf of people to research, decide, and execute tasks — moved from experiments into real business pilots over the last year. Cloud vendors and startups released agent frameworks that connect language models to calendars, CRMs, knowledge bases, and automation tools. Companies are already using