Ron Mitchell

Quick summary A recent wave in business AI is the rise of autonomous AI agents — systems that can carry out multi-step tasks on their own, like qualifying leads, scheduling demos, generating reports, or routing customer issues. Instead of a human typing prompts one-off, these agents act continuously, connect to your CRM, calendar, and data […]

Short summary AI “agents” — systems that autonomously carry out tasks by combining LLMs, retrieval (RAG), and API connections — are no longer an experiment. Businesses are using agents to handle sales follow-ups, automate recurring reports, triage customer requests, and coordinate multi-step workflows across CRM, ERP, and BI tools. Why this matters for business –

Quick summary Autonomous AI agents — software that can plan and carry out multi-step tasks with little human direction — are no longer just demos. Over the last year we’ve seen firms move these agents into sales, reporting, and back‑office automation: automated lead qualification and outreach, meeting follow-ups and summaries, end‑to‑end invoice processing, and scheduled

Summary In the past year we’ve seen a clear shift: AI agents — small, goal-directed systems that can run tasks across apps, learn from feedback, and carry out multi-step workflows — are leaving the lab and showing up in real business systems. Commercial, low-code agent platforms now connect to CRMs, ERPs, and BI tools so

Quick summary AI agents — systems that can read, act, and talk to your apps (think: summarize meetings, update CRM records, run reports, or follow up on leads) — are no longer just lab experiments. Major vendors and open-source tools have made agent-style automation practical for real business workflows. That means businesses can start automating

Quick summary AI “agents” — autonomous, task-focused AI that can call APIs, read files, and act without constant human prompts — have moved from research demos to real business tools. Major platforms and frameworks (custom GPTs, agent toolkits, integrations with CRMs and RPA) make it faster to build agents that do things like generate and

Short summary Companies are increasingly combining retrieval-augmented generation (RAG) with private LLMs and AI agents to get reliable, business-specific answers from their data. Instead of trusting a generic model that hallucinates, teams are connecting internal documents, CRM records, and reporting systems to a vector store so an AI can fetch exact facts before composing responses.

Quick summary Major vendors and startups have shifted from demo-grade chatbots to enterprise-ready AI agents. Platforms from Microsoft, Google, OpenAI and others now let businesses create autonomous agents that connect to CRMs, databases, calendars and BI tools — not just answer questions. That means AI can proactively run workflows (e.g., qualify leads, book demos, prepare

Quick summary In the last year we’ve seen a clear shift: AI agents — software that can act, decide, and connect to tools — are moving out of labs and into real business systems. Vendors are shipping enterprise-ready agent platforms with connectors to CRMs, databases, and reporting tools, plus controls for security, cost, and auditability.

Summary AI agents — autonomous, task-focused AI that can read, act, and follow up across apps — are no longer just a research curiosity. Over the last year we’ve seen more companies move these agents from pilots into real business workflows: automating sales outreach, enriching leads, running regular reports, and even handling cross-system tasks like