Ron Mitchell

Quick summary AI “agents” — autonomous, workflow-focused AI that can call tools, access data, and act on behalf of users — moved from demos into real business use in 2024. Companies are using agents to draft personalized sales outreach, triage support tickets, automate repetitive reporting, and coordinate cross-team tasks. The result: faster decisions, fewer manual […]

Quick summary AI agents — software that can plan, take multiple steps, call tools, and act on data — are moving from research demos into real business use. Instead of one-off AI answers, companies can now build agents that research leads, update CRMs, generate weekly performance reports, and even trigger follow-up tasks automatically. Why this

Hook: Autonomous AI agents — tools that can act on your behalf, access internal data, and complete multi-step tasks — have moved out of labs and into real business workflows. That shift matters for sales, reporting, and operations. What’s happening (quick summary) – AI agents are increasingly used in production to automate tasks like generating

Short summary AI agents — autonomous assistants that read your systems, take actions, and learn from results — moved from demos to real business use in 2024–25. Companies are using agents to prospect, personalize outreach, update CRMs, run routine reports, and automate follow-ups. The result: faster cycles, fewer manual tasks, and clearer insights for decision-makers.

Quick summary AI “agents” — systems that can take multi-step actions for you (think: qualify leads, book meetings, update CRMs, or generate weekly reports) — moved from hobby projects into enterprise software over the last 18 months. Vendors and open-source projects are shipping agent frameworks and integrations that let these AIs act across apps, trigger

Quick summary – In 2024 we saw a clear shift: AI agents — programmable, autonomous assistants built on LLMs — moved from proofs-of-concept to real production use. Major platforms (Copilot tools, custom GPTs and agent frameworks) made it easier to create agents that can query systems, summarize data, draft messages, and trigger actions. – Businesses

Quick summary AI “agents” — autonomous assistants built from large language models and workflow tools — are moving fast from experiments into real business use. Instead of one-off prompts, these agents can run multi-step tasks: qualify leads, summarize sales calls, update CRMs, generate tailored outreach, and trigger downstream workflows. Vendors and startups are packaging agent

Quick summary AI agents — autonomous systems that plan, act, call APIs, and use tools — are moving from lab experiments into real business use. Vendors now offer agents that integrate with CRMs, ERPs, RPA tools, and BI platforms, and organizations are using them to do things like qualify leads, pull and summarize reports, route

Summary – What’s happening: Autonomous AI agents — systems that connect to your tools, run multi-step workflows, and act with minimal human prompts — are leaving labs and showing up in real business use cases. They can pull CRM data, draft outreach, create and distribute reports, and trigger downstream systems. – Why it matters for

Quick summary AI “agents” — autonomous, task-focused AI assistants — are rapidly shifting from experiments into real business use. Recent months have seen vendors and platforms make agents easier to build, safer to run, and simpler to connect to CRMs, BI tools, and document systems. That means businesses can now automate not just single tasks,