Ron Mitchell

Quick summary Major vendors and toolmakers have moved from “AI that writes” to “AI that acts.” Modern AI agents can read your CRM, pull data from finance systems, draft communications, and trigger workflows across apps — often with a single prompt. That shift is showing up in products like Copilot-style assistants, platform connectors (e.g., Zapier/Workato […]

Quick summary Major vendors and startups are shipping “AI agents” — autonomous, multi-step assistants that can fetch data, run workflows, and produce human-ready outputs. Instead of one-off chat replies, these agents coordinate tasks (like enriching leads, generating reports, or triaging customer requests) across systems and act with minimal human direction. Why this matters for business

Quick summary AI agents — autonomous workflows built on large language models with retrieval and task orchestration — moved this year from demos into real business use. Companies are now using agents to draft and personalize outreach, update CRMs, automate recurring reporting, and run low-risk decision loops (e.g., triaging leads or support tickets). The result:

Quick summary AI “agents” — autonomous multi-step AI assistants that can read systems, act on data, and coordinate tasks — are no longer just demos. Over the past year we’ve seen frameworks (LangChain, AutoGen), major vendors (Microsoft Copilot for Dynamics, Google/Vertex integrations, Salesforce Einstein GPT) and startup tools make agents practical for real workflows: lead

Quick summary Over the past 12–18 months the AI story many businesses are watching is the rise of autonomous, connected AI agents — small systems that can take actions for users by calling APIs, reading internal data, and managing multi-step workflows. Vendors and cloud providers added built-in connectors, low‑code agent builders, and stronger security controls,

Quick summary AI agents — small, task-focused systems powered by large language models and connected data (often called RAG — retrieval-augmented generation) — are moving from labs into real business workflows. They can pull CRM and ERP data, generate sales and financial reports, qualify leads, and trigger follow-up actions across tools without waiting on a

Summary AI “agents” — small, goal-oriented AI programs that can read your data, take actions across apps, and follow multi-step workflows — have moved from experimental demos to practical business tools. Today’s agents can qualify leads, update CRMs, draft and send follow-ups, pull figures for weekly reports, and trigger downstream processes with little human supervision.

Big picture (the story) AI “agents” — models that can call APIs, browse, query databases, and take multi-step actions — have moved from research demos into real business tools. Major platforms now let companies create agentic workflows that can read CRM records, enrich leads, run reports, open tickets, and even trigger downstream processes without constant

Quick story AI agents — autonomous, task-focused AI helpers that can read systems, take actions, and hold multi-step conversations — are moving from experiments into real business use. Over the last year we’ve seen more companies deploy agents for sales outreach, customer support triage, procurement approvals, and automated reporting. These agents are now easier to

Quick summary AI “agents” — autonomous systems that can read company data, take actions, and keep doing follow-ups — are moving from labs into real business use. Companies are using agents to qualify leads, update CRMs, generate sales reports, and trigger workflows across tools (calendar, email, ERP) with minimal human handoffs. The result: faster responses,