Ron Mitchell

Quick story A new wave of AI agent platforms and “copilot” features is connecting large language models to real business systems — CRMs, ticketing tools, data warehouses and reporting stacks. Instead of just answering questions, these agents can take actions: update records, compile reports from live data, create follow-up emails, and triage leads across tools. […]

Quick summary AI agents — smart software that can read your data, talk to systems (CRM, ERP, chat), and take actions — are now being used in real business workflows, not just research demos. Companies are using agents to qualify leads, generate and send proposals, automate routine reporting, and trigger downstream processes when conditions change.

What’s changing In 2025 we’ve moved past “wow” demos to real-world deployments of autonomous AI agents — systems that can read your CRM, pull numbers from spreadsheets, draft outreach, and even trigger actions in other apps. These agents combine large language models with connectors, retrieval (RAG), and basic automation so they don’t just answer questions

Quick summary AI agents — autonomous, task-focused AI that can read your data, take actions, and talk to users or systems — are no longer experimental. Over the past 18 months we’ve seen vendors and enterprises build agents that handle end-to-end workflows: qualifying leads, scheduling meetings, updating CRMs, generating reports, and routing exceptions to humans.

Quick summary AI “agents” — autonomous, task-focused AI that can read your systems, take actions, and loop in people — moved from experiments into practical tools in 2023–24. Frameworks (LangChain-style agents), vendor copilots, and easier connectors to CRMs, BI tools, and calendars let these agents do work like lead qualification, routine reporting, pipeline follow-ups, and

Story summary AI agents — autonomous software that can read your systems, take actions, and generate human-ready outputs — have moved from demos to daily business use. Companies are combining agent frameworks with retrieval-augmented generation (RAG) and private knowledge bases so agents can query CRMs, ERPs, and document stores, then produce sales outreach, pipeline reports,

Summary — the story in plain terms AI “agents” — autonomous, tool-enabled AI assistants that can read your CRM, send emails, book meetings, and generate reports — are moving from demos into real business use. Advances in retrieval-augmented generation (RAG), vector search, and secure API integrations mean these agents can use company data safely and

Quick summary AI “agents” — autonomous, multi-step workflows powered by large language models plus data connectors — moved from research demos to practical business tools in 2023–24. With better models, retrieval-augmented generation (RAG), and connectors to CRMs, ERPs and cloud storage, agents can now fetch data, make decisions, take actions, and hand off to humans

Quick summary AI agents — small, autonomous workflows built from large language models plus connectors (CRMs, calendars, ticketing, RPA) — are moving fast from lab demos into real business use. Today they can qualify leads, schedule demos, generate quotes, triage support tickets, and assemble recurring management reports by pulling data from multiple systems and taking

Big idea Large vendors and fast-moving companies are rolling generative AI into everyday sales and operations — think AI copilots inside CRMs, autonomous agents that gather and summarize customer info, and automated reporting that updates itself from live data. These tools are moving from demos into payment-ready features that teams actually use. Why this matters