Ron Mitchell

Quick summary AI agents — autonomous, task-focused AI programs that can read your files, talk to apps, and take action — have moved from lab experiments to real business tools. Over the past year we’ve seen major platform vendors and enterprise tools add agent capabilities, better data connectors, and tighter security controls. That combination makes […]

Quick summary AI “agents” — small, goal-oriented AI programs that can read, act, and talk to other systems — are moving from lab experiments into real business use. Instead of just answering questions, agents can qualify leads, update your CRM, generate weekly sales reports, and trigger downstream automations. Companies are no longer asking whether agents

Summary AI “agents” — chatbots that can plan, act, and use external tools or data — have moved from demos into real business use. Over the last year we’ve seen startups and major cloud vendors ship agent orchestration platforms that let an AI coordinate multiple systems (CRM, calendar, email, BI) to complete multi-step tasks without

Quick summary AI agents — autonomous software that can research, act, and follow up on tasks — are moving out of tech demos and into real business workflows. Companies are using agents to qualify leads, draft and send personalized outreach, generate and refresh sales reports, schedule meetings, and automate repetitive back-office tasks. The payoff: faster

What happened Over the last year, “AI agents” — models that can act on their own, chain tasks together, and interact with tools — moved from research demos into real business pilots. Major cloud providers and open-source toolkits made it much easier to build agents that can, for example, gather sales intelligence, update CRMs, generate

Quick summary AI agents — autonomous, multi-step AI assistants that can act on behalf of users — are shifting from experiments to real production work in sales, operations, and reporting. Instead of a single prompt -> answer flow, these agents combine data access, memory, business rules, and automated actions (like updating a CRM, generating a

What’s happening (quick summary) – Over the last year, more companies have started using autonomous AI agents and AI-powered reporting to automate routine work and generate real-time, narrative-driven insights from their data. – These agents can pull from CRMs, ERPs, and BI systems to draft sales outreach, summarize customer health, and update dashboards — often

Quick summary AI agents — autonomous software that can read, decide, and act across apps — moved from labs into real business tools this year. No‑code builders and connectors now let non‑technical teams create agents that fetch data, run approval workflows, generate reports, or start sales outreach without constant human prompting. Why this matters for

Quick take Over the past year, more businesses are piloting autonomous AI agents — small software “helpers” that combine large language models with connectors to CRMs, email, calendars, and RPA tools. These agents can qualify leads, draft outreach, update pipelines, summarize meetings, and auto-generate reports. That makes routine sales and operations work faster and less

Quick summary – AI agents — autonomous software that connects language models to your data, apps, and workflows — are no longer just demos. Companies are using them to draft personalized outreach, triage customer requests, automate repetitive tasks, and generate near-real-time business reports. – The ingredients making this practical now: more capable language models, easy