Ron Mitchell

Short summary AI “agents” — software that can fetch data, talk to apps, and take multi-step actions on your behalf — have moved from labs into business tools. Over the last year, vendors and open-source projects made it much easier to build agents that connect to CRMs, calendars, reporting systems, and chat channels. That means […]

Quick summary AI agents — autonomous, task-focused AI programs that can read, act, and chain actions across apps — have moved from research demos to real business tools. Low-code agent builders and agent frameworks make it far easier to create assistants that handle end-to-end work: from prospect outreach and scheduling to compiling monthly reports and

Quick summary – AI agents — autonomous, task-focused tools powered by large language models — are being embedded into CRMs, email, and reporting tools. – That means agents can draft personalized outreach, summarize calls, update opportunities, and generate sales and finance reports automatically. – For businesses this is not just a tech trend: it can

Quick summary AI “agents” — software that can act autonomously to complete tasks — are no longer just research demos. Over the last year we’ve seen them move into real business use: scheduling meetings, qualifying leads, drafting personalized outreach, and running routine data checks. Paired with retrieval-augmented generation (RAG) and modern reporting tools, these agents

Quick summary Autonomous AI agents — tools that can plan and carry out multi-step tasks by connecting to your apps and data — have moved from proof-of-concept experiments into real-world pilots. Businesses are using agents to qualify leads, run recurring sales reports, monitor inventory and pricing, route customer issues, and even orchestrate multi-system workflows that

Summary Autonomous AI agents — software that can plan, act, and complete tasks with little human direction — have moved from lab demos into real business use. Teams are using agents to qualify leads, draft personalized outreach, automate reporting, reconcile invoices, and monitor campaigns. The result: faster decision cycles, fewer manual errors, and more time

Quick summary AI agents — autonomous software that can read data, take actions, and talk to tools — are moving from experiments into real business use. Instead of just generating text, modern agents can check your CRM, pull reports, create follow-up emails, schedule calls, and update pipelines automatically. That shift is making AI a practical

Big picture Autonomous AI agents — software that can plan, act, and complete multi-step work with little human intervention — have moved from experiments into real business use. Companies are using agents to draft outreach, qualify leads, update CRMs, run routine reporting, route support tickets, and trigger follow-up actions across systems. The result: faster responses,

Short summary AI “agents” — autonomous, workflow-aware AI that can read your systems, take actions, and summarize results — are no longer just a research demo. Over the last year we’ve seen platforms make it easier to build low-code/custom agents that connect to CRMs, calendars, databases and reporting stacks. That means businesses can automate tasks

Summary AI “agents” — software that can plan, act, and chain tasks across systems on its own — have moved out of labs and into real business workflows. Companies are using agents to qualify leads, generate and distribute reports, handle routine customer messages, and automate multi-step processes that used to require human coordination. Why this