Ron Mitchell

Quick summary In the past year we’ve moved from experiments to real-world deployments of AI agents — software that can act, decide, and complete multi-step tasks on your behalf. Rather than just answering questions, agents can run sales outreach, update CRMs, generate and distribute reports, and trigger downstream processes across tools. Leading cloud and platform […]

Quick summary Over the last 12–18 months we’ve seen a big jump in practical AI agents — not just chatbots, but autonomous assistants that connect to your CRM, BI tools, calendars, and email to complete multi-step work (e.g., prioritize leads, draft outreach, generate weekly reports, and update records). Advances in retrieval-augmented generation (RAG), secure connectors,

Quick summary AI agents — autonomous, multi-step AI assistants that can access your systems, pull data, and take actions — have moved from labs into real business use. Over the last year, major vendors and startups have rolled out agent frameworks and orchestration tools that let models complete tasks end-to-end: triaging leads, enriching CRM records,

Summary AI agents — software that can act autonomously, interact with systems, and chain tasks — are no longer just research demos. Over the past year major platforms have made it easier to build agents without heavy engineering: low‑code/no‑code builders, prebuilt connectors to CRMs and data warehouses, and templates for common workflows (sales outreach, customer

Quick summary – Over the past 12–18 months, autonomous AI agents and agent frameworks have gone from research demos to practical business tools. Companies are using them to automate repeatable workflows: lead qualification, CRM updates, meeting follow-ups, invoice triage, and routine reporting. – Vendors and open-source projects now offer easier ways to connect agents to

Quick summary AI agents — autonomous, goal-directed software that can read systems, take actions, and learn from outcomes — are moving out of labs and into everyday business workflows. Recent advances in agent frameworks, cheaper model access, and better connectors to CRMs, calendars, and reporting tools make it realistic for teams to automate tasks like

Summary AI agents — software that combines large language models with tools like your calendar, CRM, and data connectors — have moved from demos to real business pilots. Instead of asking a model a single question, agents can run multi-step tasks: find the right customer data, draft outreach, schedule meetings, and update records automatically. Companies

Lead: Autonomous AI agents — LLM-powered tools that act on your behalf to research, draft, automate, and report — are moving from demos to day-to-day use across sales, ops, and finance. That shift matters for any business that wants to cut repetitive labor, speed decisions, and scale personalized customer outreach. The story in plain terms

Quick summary AI “agents” — autonomous AI tools that can read your data, talk to apps, and carry out multi-step tasks — are moving from experiments into real business use. Frameworks and enterprise tools (think agent platforms and built-in copilots) now make it practical to automate workflows like lead qualification, report generation, contract triage, and

Quick summary Autonomous AI agents — software that can act, fetch data, and make decisions across tools with little human prompting — have moved from lab experiments into real business use. Companies are using agents to qualify leads, generate recurring reports, reconcile invoices, route customer issues, and automate repetitive workflows that used to take hours