Ron Mitchell

AI agents—autonomous software that uses large language models (LLMs), retrieval systems, and task orchestration—are moving from labs into everyday business workflows. Companies are using agent-based automation to handle tasks like invoice processing, complex customer support routing, procurement triage, and cross-system reporting. The result: faster cycle times, fewer manual handoffs, and more consistent outcomes when built […]

Big picture (what’s happening) – Over the past year, major AI vendors have pushed powerful multimodal LLMs and easy-to-build AI agents into the market. These systems can read text, analyze images, pull from databases, and trigger actions across apps — then orchestrate multi-step workflows with less human hand-holding. – The result: companies can now automate

Why this matters now AI agents—software that reasons, plans, and acts across apps and data—are moving from labs into real business workflows. Instead of just suggesting text or insights, these agents can complete tasks end-to-end: query a CRM, update records, generate quotes, schedule meetings, and trigger downstream systems. When paired with retrieval-augmented generation (RAG), vector

Short headline: Retrieval-Augmented Generation (RAG) and vector databases are moving from experiments into production — and they’re changing how companies use AI for search, support, and decision-making. What’s happening now – Organizations are combining large language models (LLMs) with vector databases to build Retrieval-Augmented Generation (RAG) systems. – Instead of asking a model to “know

Short summary: Autonomous AI agents — software that can plan, act, and interact across apps — are moving from labs into real business use. Over the last 18–24 months organizations have started using agents to qualify leads, draft contracts, automate monthly reports, and run routine ops tasks. These agents combine large language models, connectors (CRMs,

Short summary AI agents — autonomous, conversational systems that combine large language models (LLMs) with tools, APIs, and company data — are moving from demos into real business use. Companies are using agents plus Retrieval-Augmented Generation (RAG) to let AI safely access internal documents, CRM records, and operational systems so the AI can draft client

Short summary Over the past year, many companies have moved from experimenting with public chatbots to building private LLM systems that connect to their own data via vector databases (retrieval-augmented generation, or RAG). The result: AI that answers questions using your internal knowledge, reduces hallucinations, and keeps sensitive data in-house. This trend is driven by

Quick take: AI “copilots” and autonomous agents that connect to a company’s apps and data are moving from demo stage into real business use. Companies are using RAG (retrieval-augmented generation), vector databases, and workflow orchestration to build assistants that draft emails, summarize meetings, surface customer insights, and automate repeat tasks — while leaders focus on

AI agents — autonomous, multi-step workflows powered by large language models — are moving from labs into the enterprise. Over the past year we’ve seen more platforms ship agent frameworks, more vendor integrations (CRM, ticketing, BI), and more pilots that replace repetitive human tasks like research, ticket triage, and report drafting. Why this matters for

Recent trend snapshot – Autonomous AI agents — software that plans, executes, and adapts multi-step tasks with minimal human direction — have moved from research demos into real business pilots. – Frameworks and toolkits (think LangChain-style agent patterns, vendors adding agent features to their stacks) are making it easier for teams to build agents that