Ron Mitchell

Short summary There’s a clear, accelerating trend: AI agents — autonomous, task-focused AI that can read your data, take actions, and coordinate tools — are moving out of labs and into real business workflows. Big vendors and startups alike are packaging agent frameworks and integrations that let teams automate email outreach, compile reports, summarize meetings, […]

Quick summary AI agents — small, goal-driven software that can read your inbox, update CRM records, schedule meetings, pull data, and even draft proposals — are no longer an experiment. Over the past year the technology has matured: better integrations, safer access controls, and more accurate retrieval from internal data make agents practical for everyday

Quick summary AI agents — software that can act on your behalf (access systems, send emails, schedule meetings, update records) — have moved from prototypes to practical tools. New low-code platforms and better connectors let these agents plug into CRMs, calendars, inboxes, and reporting systems so they can complete multi-step tasks without a human at

Quick summary AI agents — task-focused, customizable “digital assistants” that can read your documents, query systems, and take actions — have moved from research demos into real business tools. Companies are now using agents to automate sales follow-ups, generate executive reports, and run routine operational checks. These agents combine large language models with data connectors,

Quick summary AI agents—small, purpose-built systems powered by large language models—are moving from experiments into real business use. Instead of one-off chatbots, companies are building agents that connect to CRMs, calendars, analytics, and email to complete tasks end-to-end: qualify leads, draft outreach, schedule meetings, reconcile reports, or generate weekly dashboards automatically. Why this matters for

Summary Autonomous AI agents — software that can carry out multi-step tasks on behalf of a user — are no longer just research demos. Over the last year we’ve seen a wave of enterprise tools that let agents pull from company data, complete workflows (like drafting responses, updating CRMs, and generating reports), and hand off

Short summary AI agents — software that plans and executes multi-step tasks with little human prompting — have moved from experiments to practical business tools. Instead of a single prompt, modern agents can research prospects, draft personalized outreach, update CRMs, and generate summary reports automatically. That means faster lead response, cleaner pipeline data, and near-real-time

Quick summary Recent months have shown a clear shift: AI agents — models that can use tools, call systems, and run multi-step workflows — are no longer just research demos. Businesses are wiring agents into CRMs, calendars, ERPs, and document stores so the AI can complete end-to-end tasks like preparing sales reports, drafting personalized outreach,

Short summary AI “agents” — models that act on your behalf across apps and data — have moved from experiments to real business tools. In practice that means autonomous workflows that can qualify leads, update CRMs, generate reports, and trigger follow-up actions without constant human hand-holding. Why this matters for business – Faster execution: routine

Quick summary AI “agents” — autonomous, multi-step assistants that can research, take actions, and integrate with other software — are no longer just demo fodder. Over the past year these systems have matured enough to reliably handle sales tasks, routine customer support, and automated reporting when given proper guardrails and data access. That shift is