Ron Mitchell

Quick summary – Over the last year we’ve seen a clear shift: autonomous AI agents — systems that can take multiple steps, interact with apps, and make decisions — are moving out of labs and into real business processes. – Companies are using agents to qualify leads, update CRMs, generate sales reports, triage customer inquiries, […]

Big picture Over the last year we’ve seen AI agents — models that can take actions, call other systems, and follow multi-step workflows — move out of labs and into real business use. Instead of just answering questions, they can schedule meetings, update CRMs, generate reports, run routine analyses, and trigger downstream automations. That makes

Quick summary AI “agents” — autonomous, goal-driven AI programs — are moving from experiments to real business use. Teams are using agents to qualify leads, update CRMs, summarize meetings, and generate regular reports without constant manual work. Instead of replacing people, these agents do repetitive tasks and surface insights faster so teams can focus on

Story summary A clear trend is emerging: autonomous AI agents — systems that can carry out multi-step tasks with little human direction — are no longer just research demos. Companies are piloting agents to handle routine sales work (lead qualification, follow-ups), operations (order status checks, vendor coordination), and reporting (automatic dashboards and narrative summaries). These

The story (short) AI agents — software that can plan, act, and carry out multi-step tasks on behalf of users — are moving out of labs and into business workflows. Large vendors (think copilots from major cloud providers) plus a wave of startups have made it easier to connect agents to CRMs, analytics, and back-office

Quick summary AI agents — autonomous assistants that act on your behalf (think: qualify leads, update CRMs, run reports, or triage support tickets) — are suddenly practical for businesses. Advances in models, APIs, and low‑code tools have lowered the technical barrier, so teams can deploy agents that connect to calendars, CRMs, chat systems, and databases

Summary AI “agents” — software that can plan and carry out multi-step tasks across apps — are no longer just demos. Over the past year we’ve seen toolkits, connectors, and enterprise integrations that let agents read your CRM, send emails, update records, and compile reports automatically. That shift means businesses can move from point tools

Quick summary AI “agents” — systems that can plan, act across tools, and complete multi-step tasks — have moved from experiments into real business use. Improvements in model cost, orchestration libraries, and safer deployment practices mean companies can now automate end-to-end workflows: auto-qualifying leads, scheduling and running follow-ups, generating recurring reports, or handling routine customer

What happened (short summary) AI “agents” — systems that act autonomously across apps (qualifying leads, drafting proposals, updating CRM, generating reports) — are no longer just experiments. Companies are embedding agent layers into sales, ops, and reporting workflows so the AI can take multi-step actions (research a lead, draft outreach, log activity) instead of just

Hook: AI agents — software that plans and executes multi-step tasks across apps and data — have moved from experiments into real business work. Companies are already using them to automate sales outreach, generate reports, and handle routine operations. What’s happening (quick summary) – Modern AI agent platforms can connect to your CRM, ERP, calendar,