Ron Mitchell

The story (short) – Advances in large language models plus “tool use” and easy APIs have turned AI agents from demos into real business tools. Today’s agents can read documents, query CRMs, send emails, and run multi-step workflows — often with low latency and reasonable cost. – That matters because these agents don’t just generate […]

Quick summary AI agents — small, autonomous systems that can read, decide, and act across tools — are moving from pilots into real workstreams. Instead of only generating text, modern agents can pull customer data, draft outreach, update CRMs, run follow-up tasks, and produce routine reports. Vendors and early adopters are now focusing on makes-it-reliable:

Short summary Autonomous AI agents — software that can take multi-step action (research, write, schedule, update systems, and follow up) — are moving from lab experiments into real business use. Instead of giving a single answer, these agents carry out workflows across apps and data sources. That means faster reporting, smarter lead qualification, and automated

Summary There’s a clear shift right now: AI agents — software that can act autonomously, talk to systems, and complete multi-step tasks — are moving out of labs and into everyday business workflows. Companies are using agents to handle sales follow-ups, generate real-time reports, triage support tickets, and automate repetitive admin tasks. At the same

Short summary AI “agents” — autonomous, multi-step AI assistants that can act on your data, apps, and workflows — are moving from demos into real business use. Major cloud and AI vendors and a wave of startups now offer agent frameworks that can schedule follow-ups, triage support tickets, assemble reports, and even draft personalized sales

Quick summary Over the last year we’ve seen a clear shift: AI agents — autonomous software that combines large language models, data connectors, and workflow logic — are moving out of pilots and into production. Companies are using agents to qualify leads, auto-fill quotes and orders, generate sales reports, and triage support requests with little

Quick summary of the story – Across industries, companies are moving beyond one-off AI chatbots to deploy autonomous AI agents that can complete end-to-end tasks — for example, run prospecting, draft and send personalized outreach, update CRMs, and generate performance reports. – These agents combine large language models, retrieval-augmented generation (RAG), API integrations and simple

Quick summary AI agents — systems that combine large language models with connectors to apps, data, and automation tools — are no longer just research demos. Over the last year we’ve seen a wave of practical deployments: agents that can read your CRM, draft outreach, update records, run analytics and generate human-friendly reports, then trigger

Summary AI “agents” — small, goal-oriented systems that use large language models to act on behalf of users — are rapidly becoming practical for real business work. Recent improvements in model capability, agent frameworks, and integrations with CRMs, calendars, and BI tools mean companies can automate follow-ups, produce on-demand reports, and run routine sales tasks

Quick summary AI agents — autonomous software that reads your data, takes multi-step actions, and talks to systems via APIs — have moved out of the lab and into real business workflows. Over the past year we’ve seen more off-the-shelf agent frameworks and pre-built connectors for CRMs, ticketing, and reporting tools. That makes it easier