Ron Mitchell

The story in brief AI agents — autonomous, task-focused systems powered by large language models — have moved from labs into real business workflows. Companies are using them to automate lead qualification, draft personalized outreach, generate sales reports, summarize meetings, and triage customer requests. The result: faster decisions, fewer manual steps, and better use of […]

Quick takeaway AI agents — autonomous software that can read, act, and follow up — have moved from lab demos to real business use. Companies are already using them to automate outreach, qualify leads, update CRMs, and generate timely business reports. That means faster pipelines, fewer manual tasks, and clearer insight for decision-makers. Why this

The story in one line Major CRM and productivity vendors — and a fast-growing group of startups — are embedding AI agents that can draft outreach, qualify leads, update records, summarize meetings, and generate reports automatically. These agents are no longer experiments; they’re becoming part of everyday workflows. Why this matters for your business –

Quick summary – Autonomous AI agents — tools that can carry out multi-step tasks (like drafting outreach, updating CRMs, and generating weekly reports) without constant human prompts — are no longer just experiments. – New agent builders and integrations make it faster to connect agents to calendars, email, CRMs, and analytics systems. That means real

Story summary AI agents — autonomous, API-driven systems that can research, act, and report on behalf of users — have moved from experimental demos to real business deployments. Over the last year more companies have started using agents to handle sales follow-up, triage customer support tickets, generate monthly performance reports, and automate repetitive finance tasks.

Quick summary AI agents — autonomous, task-focused systems that connect to your CRM, email, calendar, and BI tools — are no longer experimental. More companies are using them to automate routine sales work, generate on-demand reports in plain language, and orchestrate cross-team processes without heavy developer effort. Why this matters for business – Faster sales

Summary Over the last year, major AI vendors have shifted from one-size-fits-all large language models to practical, customizable AI agents: purpose-built assistants that can access your data, run workflows, and take repeatable actions. Think of an agent that reads your CRM, drafts personalized outreach, schedules follow-ups, and updates your pipeline — without a developer rebuilding

Quick summary AI agents — autonomous, multi-step AI programs that can act on your behalf (book meetings, summarize deals, enrich records, run reports) — have moved from experiments to real business tools. Over the last year we’ve seen more no-code agent builders, tighter integrations with CRMs and data warehouses, and enterprise vendors adding “agent studios”

Quick summary AI “agents” — autonomous, multi-step AI assistants that can call APIs, pull data, and act on behalf of users — are no longer just demos. Over the last year major platforms have made it easier to build and deploy agents that handle tasks end-to-end: from triaging customer messages and updating CRMs to generating

Quick summary – What’s happening: Over the past year we’ve seen AI agents and agent frameworks move out of research demos and into enterprise tooling. Cloud vendors and startups now offer ways to chain LLMs, connect them to your systems, and orchestrate multi-step workflows — from lead qualification to automated reporting. – Why it matters