Ron Mitchell

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

AI story in brief Autonomous AI agents — software that can plan, act, and connect to company systems without constant human prompting — are moving from labs into real business use. Over the last year, more organizations have piloted agents that handle tasks like customer triage, sales research, routine reporting, and scheduling. These agents combine

Short summary of the trend Autonomous AI agents and retrieval-augmented generation (RAG) are moving from R&D experiments into real business workflows. Companies are combining LLMs with knowledge retrieval, process automation tools, and event triggers to let “AI agents” handle tasks like customer triage, order processing, contract review, and routine finance reconciliations. The result: faster response

Quick summary AI “agents” — software that can take multiple steps, access company systems, and make decisions on behalf of users — have moved from research demos to real business pilots. Tools and platforms (think plugin-enabled assistants, AutoGPT-style workflows, and enterprise agent builders) let AI complete tasks like handling customer follow-ups, compiling weekly reports, or

AI agents — autonomous systems that can read, plan, act, and learn — are moving from tech demos into real business use. Over the past year, more companies have started using AI agents to automate routine workflows, accelerate decision-making, and reduce manual handoffs. These agents can do things like update CRM records, draft tailored customer

Short summary: Autonomous AI agents — software that can plan, act, and follow up across multiple systems — are moving from labs into real business use. Companies are using agents to run end-to-end workflows like sales outreach, invoice processing, IT ticket triage, and competitive research. The combination of large multimodal models, retrieval-augmented generation (RAG), and

Short summary Autonomous AI agents — small, goal-driven systems built on large language models (LLMs) and tool integrations — are moving quickly from tech demos into real business use. These agents can read documents, call APIs, coordinate apps (CRMs, ERPs, ticketing systems), and complete multi-step tasks with minimal human prompts. Companies are using them for

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,