Ron Mitchell

Summary AI agents — small, goal-oriented programs that can read your CRM, draft emails, book meetings, or generate reports — have moved from proofs-of-concept into real business tools. Advances in large language models, connector ecosystems, and low-code orchestration mean agents can now safely and reliably act across sales, support, and operations systems. Why this matters […]

Quick summary AI agents — software that can act on your behalf across apps, make decisions, and complete multi-step workflows — moved from research demos to real business tools in the last 18 months. Vendors (from cloud giants to niche startups) are shipping agent templates that can do lead follow-up, automate invoicing checks, generate weekly

What’s happening AI “agents” — autonomous, multi-step assistants that can access your calendar, CRM, documents, and APIs — are no longer an academic demo. Major platforms and enterprise tools now let businesses deploy role-specific agents (sales copilots, customer-service bots, executive assistants) that complete tasks end-to-end: research leads, draft and send outreach, pull numbers from finance

Quick summary Major vendors and open-source frameworks have made building autonomous AI agents much easier. Instead of one-off chatbots, companies are deploying agents that can qualify leads, run recurring reports, update CRMs, triage support tickets, and even coordinate multi-step sales or procurement workflows across apps. These agents connect to your data, act on rules you

Story summary – The big trend right now: AI agents — autonomous, multi-step AI “workers” that can access your data, run processes, and take actions — have moved from lab experiments to enterprise-ready tools. – Improvements in secure connectors, orchestration platforms, and human-in-the-loop controls mean agents can safely pull CRM and ERP data, generate reports,

Quick story summary Autonomous AI agents — software that can plan, act, and use tools with little human direction — have moved from demos to real business pilots. Companies are using agents to qualify leads, triage support tickets, update CRMs, and produce recurring reports. These agents combine large language models with retrieval (RAG), workflow tools,

Quick summary AI agents — autonomous, task-focused AI that can read your systems, take actions, and produce reports — have moved from experiments to real business tools. Companies are now using them to qualify leads, generate personalized outreach, run recurring analytics, and automate routine workflows. The result: faster decisions, fewer manual errors, and lower operating

Quick summary AI “agents” — autonomous systems that can read, decide, and act across apps — are moving from labs into real business use. Instead of only generating text, today’s agents can pull CRM data, draft outreach, update records, run reports, and trigger approvals across tools. That means faster sales cycles, fewer manual handoffs, and

The story (short) – Over the past year, a wave of turnkey “AI agents” and embedded copilots has moved from labs into business apps. These agents combine large language models with task orchestration and connectors (CRMs, email, calendars, data warehouses) so they can act—draft outreach, update records, generate reports, and trigger workflows. – What’s new:

Quick summary AI is moving from “helpful chat” to “do-it-for-you” agents that can run tasks end-to-end: gather data, generate a report, update your CRM, or follow up with prospects. Over the past 18–24 months vendors and enterprises have combined large language models with retrieval, connectors, and orchestration tools so agents can safely take actions across