Ron Mitchell

Quick summary In the past year we’ve seen a clear shift: AI agents — software that can act autonomously on behalf of users — are moving from experiments into everyday business tools. Vendors and developer frameworks now let companies deploy agents that qualify leads, draft outreach, summarize meetings, and auto-generate reports from live data. That […]

Short take: Autonomous AI agents — small, goal-directed AI programs that can act, fetch data, and complete tasks — have moved beyond lab demos into real business use. Over the past year, more organizations have started connecting agents to CRMs, data warehouses, and automation tools to handle lead research, follow-ups, and routine reporting. That shift

Quick summary AI agents — software that can act on behalf of users by reading documents, querying systems, and taking actions — are moving from experiments into real business workflows. Today’s agent platforms combine large language models with connectors to CRMs, ERPs, email, and dashboards so a single “agent” can qualify leads, draft outreach, update

Short summary AI agents — autonomous, multi-step tools that act across apps and data — are no longer just demos. Over the past year we’ve seen companies move from one-off pilots to production deployments that automate tasks like lead qualification, personalized outreach, monthly reporting, and expense reconciliation. The big win is speed: agents connect to

Short summary AI agents — autonomous, task-focused AI assistants that combine large language models, retrieval-augmented generation (RAG), and connectors to your apps — are moving from experiments into production. Instead of giving employees a single answer, these agents complete multi-step processes: pull CRM data, draft and send personalized outreach, update records, and generate a weekly

Why this matters now Big cloud and SaaS vendors — and a growing ecosystem of startups — have moved AI agents out of labs and into real business apps. These “autonomous” agents can perform multi-step tasks (fetch data, run calculations, update systems, send messages) instead of just answering one prompt. Improvements in retrieval (RAG), tool

Quick summary AI agents — autonomous, multi-step AI assistants that can act on data, call APIs, and manage workflows — are no longer experimental. Over the last 18 months the ecosystem (agent frameworks, tool integrations, and vendor copilots) has matured enough for real business use: qualifying leads, running daily sales reporting, triaging customer issues, and

Quick story summary AI “agents” — autonomous workflows built on large language models that can read, act, and connect to tools — have moved from research demos into real business use. Over the last year we’ve seen platforms and toolkits (agent frameworks, integrations into Copilot-style products, and connector libraries) make it practical to have AIs

Summary In the past year, AI agents — autonomous software that can run tasks, fetch data, and take actions across apps — have moved from experiments to actual business use. Major platforms now offer agent frameworks and marketplaces, and more companies are connecting agents to CRMs, ERPs, and reporting tools. The result: routine work gets

Why AI agents are the next productivity leap for business teams Step 1 — The story in one line Autonomous AI agents — systems that can carry out multi-step tasks with minimal human direction — have moved from lab demos to real business pilots, and companies are starting to deploy them for sales outreach, automated