Ron Mitchell

Short summary Autonomous AI agents — tools that carry out multi-step tasks with little human hand-holding — are moving from labs into real business use. Today’s agents can gather data, run analysis, draft emails, update CRMs, and even generate monthly reports without manual stitching. That shift makes AI more than a productivity booster: it becomes […]

Quick summary AI “agents” — autonomous systems that use large language models (LLMs) plus tools and data connectors to complete tasks — have moved out of demos and into real business workflows. Instead of just answering questions, these agents can pull CRM data, run queries against your finance system, draft proposals, schedule follow-ups, and produce

Quick summary AI “agents” — autonomous, task-focused AI that can act across apps and data — have moved from lab experiments to real business tools. New agent platforms and better connectors to CRMs, calendars, and data warehouses let teams automate end-to-end tasks like lead qualification, follow-ups, and report generation without rebuilding core systems. Why this

Short summary AI “agents” — autonomous, multi-step AI routines that can plan, fetch data, take actions, and loop until a goal is reached — have moved from experiment to business-ready tools. Instead of single-answer chatbots, modern agents can handle workflows like qualifying leads, generating personalized proposals, routing support tickets, or compiling monthly performance reports by

Short summary Over the past year we’ve seen a rapid shift from “AI that advises” to “AI that acts.” New AI agents — tools that can access your systems, run multi-step workflows, and take actions without constant human direction — are being embedded into CRMs, calendars, and reporting stacks. That means tasks like lead qualification,

Quick summary This year we’ve moved from clever demos to real-world AI agents that run multi-step tasks without constant human prompting. Big vendors and startups are shipping agents that can pull data from your CRM, draft emails, run analytics, update records, and even trigger downstream systems — all with safeguards and audit trails. That shift

Quick summary AI “agents” — software that can take multi-step actions across apps to complete goals — are moving from demos into real business use. Instead of just answering questions, these agents can pull CRM data, draft and send personalized outreach, compile financial reports, and trigger follow-up tasks. That shift turns AI from an assistant

Summary AI “agents” — lightweight, goal-driven systems that can take actions (send emails, update CRMs, fetch reports, schedule meetings) — are no longer just research demos. Over the past year many vendors and in-house teams have moved from proofs-of-concept to live pilots that automate parts of sales, customer service, and operational reporting. Why this matters

Short summary AI agents — small, goal-oriented programs that take actions for you (write emails, update CRMs, run reports) — have moved from demos to real business use. Major AI platforms and developer tools now make it easy to build agents that connect to your systems, act autonomously, and hand off to humans when needed.

What happened AI agents — autonomous software that uses large language models to run multi-step tasks, call APIs, and update systems — have moved from pilot projects into real business use. Newer models and easier integrations mean agents can do more than draft emails: they can qualify leads, book meetings, update CRMs, and generate near-real-time