Ron Mitchell

Summary AI “agents” — autonomous software that can read data, call APIs, and take multi-step actions — have moved from lab demos to real workplace wins. Companies are using agents to automate sales outreach, generate recurring reports, manage meeting scheduling, and orchestrate cross‑system workflows. Instead of one-off scripts, today’s agents can chain tools (CRM, calendar, […]

Short summary AI agents — autonomous assistants that can run tasks, pull data, and act across apps — have shifted from lab demos to real business use. Teams are using agents to generate weekly sales reports, run lead outreach, triage customer tickets, and automate repetitive back‑office work. The result: faster reporting, fewer manual errors, and

Quick summary AI agents — autonomous, task-focused systems that can access your tools, run workflows, and generate human-like outputs — are no longer just experiments. Companies are using them to draft sales outreach, update CRMs, automate routine reporting, and summarize meetings. That shift speeds work, cuts manual errors, and frees teams to focus on higher-value

Short summary AI “agents” — autonomous, task-focused models that can act on your behalf — went from research demos to real business tools in 2024–25. Platforms like LangChain, Copilot Studio, and PaLM agents have made it easier to chain steps (fetch data, take action, report results). Companies are now using agents to triage leads, draft

Quick summary AI agents — software that can take multi-step actions (like read your CRM, draft emails, schedule calls, and update reports) — have moved from research demos into real business tools. Major vendors and open-source frameworks now make it practical to build agents that connect to your systems, run workflows, and surface insights automatically.

The story AI is shifting from “assistants” that follow prompts to autonomous agents that can perform end-to-end tasks — for example, qualify leads, draft personalized outreach, update CRM records, and generate weekly performance reports with little human hand-holding. Large vendors and startups are embedding these agents into CRM, helpdesk, and analytics tools so teams can

Quick summary AI “agents” — autonomous, goal-driven AI that can run tasks, follow-up with customers, update systems, and generate reports — are no longer just research demos. Platforms and frameworks (think custom GPTs, LangChain/AutoGen-style agent toolkits, and vendor copilots) make it much easier to chain LLMs with data connectors, automation, and business rules. The result:

Quick summary AI “agents” — small, goal‑driven AI programs that can act on your behalf — have moved out of research labs and into real business tools. New low‑code platforms, built‑in memory, and ready connectors to CRMs, calendars, and data warehouses make it easier to give these agents real tasks: draft and send personalized outreach,

Summary AI agents — autonomous assistants that can read, write, act across apps, and follow up without a human at every step — have moved from experiments to real business pilots. Companies are now using agents to draft outreach, qualify leads, update CRMs, summarize meetings, and generate near-real-time reports. The result: faster response times, fewer

Quick summary AI agents — autonomous, task-focused AI that can act on your behalf (draft emails, qualify leads, update CRM, generate reports) — are no longer just experiments. Vendors and integrators are packaging agents into sales, customer service, and operations workflows so teams can automate end‑to‑to‑end tasks, not just generate text. Why this matters for