Ron Mitchell

Quick summary AI agents — not just chatbots, but automated systems that combine reasoning, tools, and data connectors — have moved from lab demos to real business use. Improvements in large models, faster inference, vector search (RAG), orchestration frameworks, and enterprise connectors mean agents can now handle multi-step tasks like lead qualification, scheduling, and report […]

Quick summary AI agents — software that can act autonomously to complete multi-step tasks — have moved from demos into real business use. Today’s agents can research prospects, draft and send personalized outreach, book meetings, update CRMs, and generate sales reports without constant human prompts. Companies that integrate agents with their CRM, calendar, and internal

Quick summary AI “agents” — AI systems that can act on your behalf by connecting to calendars, CRMs, email, and internal data — have moved out of labs and into real enterprise pilots. Over the last 12–18 months major cloud vendors, enterprise software providers, and open-source toolkits have released agent platforms and connectors that let

Quick story AI agents — autonomous, conversational programs that can use tools, pull data, and take actions — are moving from labs into real business workflows. Major vendors and startups have rolled out agent frameworks and low-code builder tools that let teams create “copilots” for sales, operations, and finance. The result: companies can automate multi-step

Quick summary AI “agents” — software that can act on your behalf across apps, data, and people — are no longer just R&D demos. Over the last 18–24 months businesses have started running agents in production to do tasks like lead qualification, automated reporting, meeting follow-ups, and routine order handling. The technology combines language models,

Quick summary AI “agents” — autonomous, task-focused AI that can plan, fetch data, and act across apps — have moved from experiments into real business use. Over the past 12–18 months major platforms and vendors released enterprise-grade agent tools and frameworks that connect language models to company data, calendars, CRMs, and automation systems. That shift

Why this story matters AI “agents” — autonomous, goal-oriented systems that can use tools, access your data, and act on behalf of users — have moved well past experiments. Advances in large models, tool integrations, memory, and low-code orchestration platforms mean businesses can now deploy agents that do real operational work: qualify leads, update CRMs,

Quick summary There’s a clear, recent surge in production-ready “autonomous AI agents” — software that uses large language models to plan, act, and coordinate tasks across apps and systems. Unlike one-off chatbots, these agents can run multi-step workflows: triage emails, update CRMs, pull data from your BI tools, generate a weekly sales report, and even

Quick summary AI agents — software that can act on your behalf to complete tasks and make decisions — are no longer just R&D toys. Over the past year we’ve seen more companies deploy agents to automate repeatable workflows: generating sales reports, routing leads, managing scheduling, and triaging customer requests. These systems combine language models,

Quick story AI agents — autonomous, task-focused systems that combine large language models with real data connectors — have moved from proof-of-concept to practical use in sales and operations. Companies are using them to pull data from CRMs, generate weekly pipeline reports, summarize meetings, and even triage inbound leads without manual work. Why it matters