Ron Mitchell

AI agents — autonomous, tool‑using AI that can read, act, and follow multi‑step tasks — are moving from labs into real business use. From intelligent sales assistants that draft outreach and update CRMs, to reporting agents that pull live data and deliver board‑ready summaries, companies are using agents to speed up routine work and scale […]

Trending topic summary AI is moving from experiments to practical business systems because companies are combining large language models with their own data using Retrieval-Augmented Generation (RAG). RAG + vector databases (Pinecone, Weaviate, Milvus and others) lets LLMs search secure company knowledge — docs, CRM records, SOPs — and generate accurate, context-aware answers. That shift

AI agents — autonomous, multi-step AI systems that can act on your behalf — are no longer experimental. Over the last 12–18 months, companies and platform vendors have moved from demos to real deployments: agents that pull information from your knowledge base, run approval workflows, draft emails, schedule follow-ups, and trigger downstream systems. This trend

AI trend in focus: Retrieval-Augmented Generation (RAG) paired with vector databases is fast becoming the go-to approach for building private, accurate “AI copilots” for business. Instead of relying only on large general models (which can hallucinate or leak sensitive data), companies are combining domain-specific content stored as vectors with LLMs that fetch and use that

AI trend snapshot AI agents — software that plans, acts, and coordinates tasks on behalf of a user — are moving from labs into the enterprise. Over the last year, checklist-style assistants and more autonomous multi-step agents have started handling real business work: drafting personalized sales outreach, triaging customer support tickets, generating monthly KPI reports,

Summary Autonomous AI agents — software that can plan, act, and complete multi-step tasks with little or no human hand-holding — are moving from labs into real business use. Over the last year, platforms and open-source toolkits have made it easier to create agents that can read documents, call APIs, update CRMs, run reports, and

Summary Autonomous AI agents — small AI programs that act on behalf of users to complete tasks — are moving from labs into real business use. Major cloud and AI vendors have released agent-building frameworks, and companies are piloting agents for tasks like lead qualification, customer support triage, procurement checks, and basic finance reconciliations. Why

The trend: Autonomous AI agents — software that can plan, act across apps, and complete tasks with little human input — are no longer hype. Big tech and startups are rolling out agent frameworks and plug-and-play tools that connect to CRMs, calendars, email, ERPs, and reporting systems. For business leaders, this means new ways to

AI trend in focus AI “agents” — autonomous, task-focused AI that can read, act, and learn — are moving from experiments into real business use. Paired with Retrieval-Augmented Generation (RAG) and vector databases, agents can pull company documents, CRM records, and real-time data to complete multi-step tasks: draft personalized proposals, reconcile invoices, run root-cause analysis,

Big picture: Major AI models and tools are making it easy for businesses to build private “AI copilots” that read documents, summarize conversations, automate routine tasks, and produce polished reports — all powered by LLMs plus retrieval-augmented generation (RAG) and vector databases. Why this matters now – Vendors from OpenAI to Microsoft and Google are