Ron Mitchell

What’s happening (short take) Autonomous AI agents — small, purpose-built AI programs that can take actions across apps and systems — are no longer just an R&D curiosity. Over the last year we’ve seen a surge in companies using AI agents for lead qualification, customer triage, recurring reporting, and routine back-office work. Improvements in large […]

Quick summary AI “agents” are autonomous software tools that can read your data, take actions across apps (CRM, email, calendar, ERP), and complete multi-step tasks with little human prompting. Over the past year more companies have moved from one-off pilots to production agents that qualify leads, update CRMs, generate reports, and automate routine sales and

Quick summary Over the past year, major AI platforms have made it easy for businesses to build custom AI agents — think trained assistants that can access your data, run tools, and follow business rules. These “GPTs” or agent builders let non‑engineers create assistants for tasks like drafting sales outreach, compiling reports, or automating routine

Quick summary AI agents — autonomous workflows that combine large language models with data access, tools, and automation — have moved from labs into real business projects. Companies are now using agents to research leads, draft personalized outreach, run routine reporting, and automate multi-step back-office tasks. That means faster decisions, fewer manual steps, and more

The story in one line Over the last 12–18 months we’ve seen a big shift: AI agents — autonomous, task-focused AI that can use tools, access systems, and follow multi-step processes — are moving out of labs and into real business workflows. Vendors from cloud giants to open-source projects have made agents easier to deploy,

AI story (short summary) Autonomous AI agents — multi‑step virtual assistants that can act on behalf of users — have moved from demos to real business pilots. Instead of answering single questions, these agents chain tasks: they pull data, summarize reports, update CRMs, send follow-ups, and trigger workflows. Early adopters in sales, customer success, and

Quick summary AI agents — software that can carry out multi-step tasks on your behalf by connecting to apps, data, and APIs — have moved from lab experiments into real business use. Sales teams use agents to draft outreach, update CRMs, and qualify leads. Operations teams use them to automate approvals, reconcile data, and generate

Quick summary AI agents — autonomous, goal-driven workflows built on large language models — are moving fast from experiments to real business results. Companies are using them to qualify leads, draft personalized outreach, summarize meetings, and generate live operational reports. That means less manual drudgery, faster decision cycles, and clearer sales pipelines without hiring more

Quick summary Autonomous AI agents—small programs that can follow multi-step instructions, pull from your data, and take actions—are moving from experiments into real business use. When combined with retrieval-augmented generation (RAG)—a way to feed relevant company documents into large language models—these agents can automate complex tasks like sales follow-ups, executive reporting, and cross-system workflows. Why

What happened In the last year, AI agents — task-focused bots that can read your systems, take actions, and coordinate multi-step work — went from lab demos to real business pilots. Platforms now make it easier to build custom agents that connect to CRMs, databases, and BI tools. Companies are using them for things like