Ron Mitchell

Summary AI “agents” — small, goal-oriented systems that use language models to act across apps and data — have moved from experiment to practical tool. Improvements in large language models, ready-made connectors (to CRMs, calendars, BI tools), and agent frameworks like LangChain and Auto-GPT make it easier to automate multi-step work that used to need […]

Quick summary Major vendors and startups are packing AI agents into everyday business tools — think generative assistants that draft outreach, pull together sales reports, and trigger follow-up tasks automatically. You’re seeing this in CRM add-ons, collaboration suites, and low-code automation platforms. The shift isn’t “more chatbots” — it’s practical automation that does parts of

Story pick (what’s happening) AI “agents” — autonomous, workflow-driving AI that can use tools, access data, and act without constant human prompts — went from research curiosity to practical business technology in 2024. Major cloud providers and platforms released agent frameworks and plug‑and‑play integrations with CRMs, calendars, and BI tools. That made it realistic for

Summary of the story – Over the past year businesses have moved beyond one-off chatbots to “AI agents” — systems that can carry out multi-step workflows, interact with tools (CRMs, calendars, email), and make decisions with less human prompting. – Low-code agent builders and integrations into major business apps mean teams can automate complex tasks:

Quick summary Major AI platforms and enterprise vendors have recently pushed tools that make building “AI agents” much easier. These agents are automated digital workers you can customize to handle tasks—like qualifying leads, answering customer questions, scheduling meetings, or compiling weekly performance reports—by connecting to your CRM, calendar, databases, and APIs. Why this matters for

Quick summary AI “agents” — autonomous, multi-step assistants that can read your systems, talk to people, and take actions across apps — are no longer just experiments. Over the last year we’ve seen more firms move agents into production for tasks like sales follow-up, cross-system reporting, and finance reconciliation. Those projects reduce repetitive work, speed

What’s happening – AI agents — software that can carry out tasks end-to-end (research, draft, act, follow up) — are no longer just lab demos. Over the last year we’ve seen a wave of low‑code agent platforms, better integrations with CRMs and data warehouses, and more plug‑and‑play tools for automated reporting and workflows. – These

Summary AI “agents” — small, task-focused systems that combine large language models with workflows, APIs, and data retrieval — are moving from experiments to real business value. Instead of asking an LLM a question, agents can take multi-step actions: pull CRM records, draft personalized outreach, update a pipeline, generate a sales report, and alert a

Quick hook AI agents—small, goal-driven AI programs that can act across apps, fetch data, and complete multi-step tasks—have moved from demos into real business pilots. That shift matters for leaders who want faster decisions, lower costs, and better sales and reporting without adding headcount. What’s happening (short summary) – Modern AI models can now use

Story summary AI “agents” — multi-step, goal-driven systems built on large language models — are moving from labs into real business use. Instead of answering single questions, these agents can run multi-step workflows: pull CRM data, draft outreach, schedule follow-ups, and generate consolidated sales reports. At the same time, AI-powered reporting tools now turn raw