Ron Mitchell

Quick summary AI agents — automated software that can carry out multi-step tasks, make decisions, and talk to other systems — have moved from proof-of-concept demos into real business workflows. New orchestration tools, better connectors to CRMs and ERPs, and easier low-code builders mean teams can now automate complex processes end-to-end: think proactive lead follow-up, […]

What’s happening Autonomous AI agents — tools that can carry out multi-step tasks, connect to apps, pull data, and take actions — have matured from demos into production pilots. Large platform vendors and niche startups now let agents access CRMs, ERPs, databases, and reporting systems. Companies are using them to draft sales outreach, triage support

Why this story matters now AI agents — autonomous, task-focused AI that can read your systems, call APIs, draft messages, and take next steps — have moved from experiments to real business pilots. Advances in large language models, agent orchestration tools, and ready-made connectors mean companies can automate multi-step work (lead enrichment, follow-ups, recurring reports)

Short summary There’s a clear momentum: autonomous AI agents — tools that can plan, act across apps, and complete multi-step tasks — are moving from demos into real business use. Companies are using them to triage customer issues, generate and deliver sales outreach, run recurring reports, and glue together workflows across CRM, spreadsheets, and internal

Quick summary Autonomous AI agents — tools that can plan, act, and use business apps without constant human prompts — have moved from experiments into real-world use. Improvements in large language models, safer tool access, and low-code orchestration platforms mean teams can now build agents to qualify leads, run reports, triage support tickets, and automate

Short story summary AI agents — autonomous, goal‑driven AI that can carry out multi-step tasks — have moved from research demos into real business use. Over the last year vendors and startups have shipped agent frameworks and plug‑and‑play connectors that let AI interact with calendars, CRMs, databases, and other apps. That means businesses can now

Quick summary Over the past year businesses have moved from experimenting with chat-based AI to deploying AI agents — software that can autonomously execute tasks across apps (CRM, email, calendars, databases) and return results. These agents can do things like qualify leads, summarize customer interactions, generate regular reports, and trigger follow-up workflows without a human

Quick summary Major AI platforms have shifted from chat-only models to practical AI agents that can connect to tools (CRMs, spreadsheets, BI dashboards), run workflows, and generate ready-to-use outputs like sales follow-ups and executive reports. That means LLMs are no longer just for brainstorming — they can act, fetch, calculate, and update systems automatically. Why

Summary In the past year, AI agents — software that can take multi-step actions on your behalf — have moved from demos to real business use. Major cloud providers and startups released agent-building tools and prebuilt connectors, making it easier to automate tasks that used to need lots of human coordination: lead qualification, multi-channel follow-ups,

What’s new A recent wave of enterprise-focused “AI agents” is moving beyond research demos into real business use. These are systems that can autonomously run multi-step tasks — for example: read your CRM, draft outreach, book meetings, update records, and generate follow-up reports — with fewer human prompts. Improvements in model reliability, retrieval (RAG/semantic search),