Ron Mitchell

Short summary AI agents — self-directed AI assistants that can read systems, take actions, and follow simple rules — have moved from demos to real business use. Over the past year major vendors and startups have released agent-builders and connectors that make it fast to link an agent to your CRM, ticketing system, or data […]

Hook: Autonomous AI agents — not just chatbots — are moving from labs into real business workflows. That shift matters for revenue teams, operations, and leaders who need faster decisions and cleaner reporting. The story in plain terms – What’s changing: New AI “agents” combine language models with tool access (CRMs, calendars, BI tools, email)

Big picture AI agents — task-focused AI that can act autonomously across apps, data, and teams — moved from lab demos into everyday business tools over the past year. Major platforms made it easier to build and customize agents (think: custom GPTs and vendor “Copilot” studio experiences), and companies are starting to deploy them for

AI story summary AI agents — autonomous, goal-driven AI that can take actions across apps and data — have moved beyond research demos into real business use. Major vendor toolkits and open frameworks now make it faster and cheaper to build agents that do things like qualify leads, update CRMs, pull and summarize data, and

Quick summary In the past year the idea of autonomous “AI agents” moved from demos to real business use. These are AI-powered workflows that connect large language models to your apps and data so the agent can take multi-step actions — qualify leads in your CRM, generate and send tailored proposals, run weekly performance reports,

AI story (short summary) AI “agents” — autonomous, multi-step software helpers that can plan, act, and follow up — have moved from demos into real business pilots. Instead of just generating text on command, these agents can pull CRM data, run queries, draft emails, schedule follow-ups, and assemble sales or performance reports with minimal human

Summary AI agents — autonomous software that uses large language models plus connectors to your CRM, calendar, files, and BI tools — are moving from experiments into everyday business use. Instead of a person copying data between systems or manually building reports, agents can gather data, draft emails, run follow-ups, and produce stakeholder-ready reports automatically.

Quick summary AI “agents” — autonomous, task-focused AI that can read your systems, run processes, and produce reports — are no longer just lab experiments. Improved large language models, better retrieval-augmented workflows, and deeper integrations from platforms (think low-code automation + LLMs) mean businesses can now deploy agents for things like sales follow-ups, invoice processing,

Quick summary AI agents — autonomous, workflow-focused AI that can read data, call APIs, and take multi-step actions — are shifting from research demos into real business use. Over the last 18–24 months vendors and in-house teams have matured the tooling: agent frameworks, safer guardrails, integrations with CRMs and BI tools, and monitoring dashboards. That

Summary AI agents—autonomous software that combines large language models with tools, data retrieval, and actions—are moving from engineering demos into real business workflows. Instead of only answering questions, these agents can run a sequence of tasks: pull CRM records, draft outreach, schedule meetings, update records, and generate executive reports. That makes them a practical way