Ron Mitchell

Quick summary AI agents — autonomous software that can read, decide, and act across systems — have moved fast from demos to real business pilots. Instead of only helping people draft emails or analyze data, modern agents can qualify leads, schedule meetings, update CRM records, generate pipeline reports, and even trigger follow-up workflows without constant […]

What happened Major cloud and AI vendors have rolled out agent-building tools and frameworks that make it much easier to create autonomous AI agents — systems that can read your data, take actions (send emails, update CRMs, run reports), and loop in humans when needed. That shift is moving AI agents from lab experiments into

Quick summary AI “agents” — autonomous assistants that can read your data, take actions, and run multi-step workflows — are no longer just lab demos. Businesses are increasingly using agents to qualify leads, follow up with prospects, auto-schedule meetings, and generate daily or weekly sales reports. The result: faster pipeline motion, fewer manual tasks for

Summary AI agents — task-focused AI tools that can run workflows, pull data, and act on behalf of users — are no longer experimental. Companies are shipping purpose-built agents for sales, finance, HR, and customer service that connect to CRMs, ERPs, and reporting systems. The result: faster reporting, fewer manual handoffs, and 24/7 task execution

The story (short) Autonomous AI agents — software that can plan, act, and connect to apps on your behalf — moved from research demos into real business pilots in 2024–25. Companies are using agents to draft personalized sales outreach, triage support tickets, prepare recurring reports, and automate routine back‑office tasks. The result: faster cycle times,

Quick summary AI “agents” — autonomous or semi-autonomous software that can research, take actions, and follow up across apps — are no longer just demos. Major platforms and toolkits (agent frameworks, CRM-integrations, and low-code agent builders) are making it simple to automate multi-step tasks: prospect research + outreach, meeting scheduling + prep, or cross-system reporting

Quick summary Large-scale “AI agents” — autonomous systems that can plan, act, and finish multi-step tasks — are no longer research demos. Over the past year, vendors and startups have shipped agent orchestration tools and enterprise connectors that make it practical to run agents on real business workflows: automated lead qualification, personalized outreach, and AI-powered

What’s happening – Over the past year we’ve moved past demos. Agent frameworks (the toolkits that let models take actions, call APIs, and run multi-step workflows) plus retrieval-augmented generation (RAG) and vector databases have made AI agents reliable enough for production use. – Businesses are now using agents to do things like assemble weekly sales

Quick summary A new wave of enterprise-grade AI agents is moving from lab experiments into real business workflows. These agents are more than chatbots — they can connect to your CRM, spreadsheets, reporting tools, and calendars to carry out end-to-end tasks: triage leads, update records, draft proposals, generate weekly revenue reports, and even trigger follow-up

Quick summary Autonomous AI agents — software that uses large language models plus connectors to take actions (create reports, send outreach, update CRMs, trigger workflows) — are moving from experiments into real business use. Advances in LLMs, retrieval-augmented generation (RAG), and low-code agent platforms mean companies can now deploy agents that handle repeatable tasks end-to-end,