Ron Mitchell

Quick summary A growing wave of AI agents—small, task-focused bots that can read your systems, draft messages, and take actions—has moved from experiments into real business use. Companies now use agents to qualify leads, automate routine customer replies, generate weekly sales reports, and trigger follow-up workflows. These agents combine large language models with data connectors, […]

Quick summary AI agents—autonomous, LLM-driven programs that can use tools, search data, and complete multi-step tasks—are moving from labs into real business workflows. Companies are starting to connect these agents to CRMs, ERPs, help desks and reporting systems so the agents can act on data, run processes, and deliver near-real-time answers instead of just static

Quick summary AI agents — models that act across apps, pull live data, and take multi-step actions — have moved from lab demos into practical business tools. Major vendors and startups now offer low-code agent builders and connectors to CRMs, email, calendars, and BI systems. That means companies can automate tasks that used to need

Short summary AI “agents” — autonomous, task-focused assistants built on large language models — are moving from demos into real business use. Instead of one-off chat answers, agents can monitor data, run workflows, update CRMs, generate reports, and take repetitive actions across tools. That means fewer manual steps, faster decisions, and cleaner data for teams

Quick summary AI “agents” — software that autonomously performs multi-step tasks (think: qualify a lead, update your CRM, create follow-up tasks, and send a summary email) — have moved from experiments to practical tools for business teams. Companies are using agents today to handle lead triage, meeting summaries, cross-system reporting, renewal workflows, and routine customer

Big picture: AI “agents” — autonomous workflows that combine LLMs, tools, and data — are moving from demos into real business use. Vendors and open-source projects now make it easier to build agents that handle multi-step tasks: qualify leads, update CRMs, generate weekly performance reports, and route exceptions to humans. That shift turns AI from

Short summary Autonomous AI agents — software that can carry out multi-step tasks with little human direction — are moving from demos into real business use. Companies are using agents to qualify leads, draft proposals, update CRMs, run recurring reports, and automate follow-ups. The result: faster response times, cleaner data, and more time for teams

Quick summary AI agents — autonomous, task-focused AI programs that can take actions, query systems, and follow multi-step workflows — moved from experiments to business-ready tools in 2024–2025. Major platforms made it easier to build, secure, and monitor agents that handle things like outbound outreach, lead qualification, report generation, and repetitive back-office work. That shift

Short summary AI agents — persistent, tool-using AI that can act on your behalf — have moved from experiments to practical business tools. Advances in multimodal models, tool integration, and RAG-style access to company data mean these agents can qualify leads, draft proposals, summarize calls, update CRMs, and trigger downstream automations with minimal human input.

What’s happening AI “agents” — small, purpose-built AI systems that can act, fetch data, and take multi-step actions — are moving out of labs and into real business workflows. Improvements in retrieval (RAG), vector databases, orchestration tools, and safety guardrails mean companies can now automate end-to-end tasks rather than only run one-off experiments. Why this