Ron Mitchell

Big picture (the story) AI “agents” — autonomous software that uses large language models (LLMs) to perform multi-step tasks — have moved from labs into real business use. Over the last 12–18 months, companies have started deploying agents to qualify leads, draft and personalize outreach, run recurring data checks, and produce narrative reports from dashboards. […]

Quick summary AI “agents” — autonomous or semi-autonomous software that can read, plan, act, and report — moved from proof-of-concept to real business pilots over the last 18–24 months. Companies are combining large language models with connectors to CRMs, calendars, and data warehouses so agents can qualify leads, prepare meeting briefs, run follow-ups, and generate

Quick take Major AI vendors and platform teams are moving beyond chatbots toward customizable, agent-style tools that can run workflows, pull data from your systems, and complete multi-step tasks with little human oversight. For businesses, that means faster reporting, cleaner sales pipelines, and real automation — if you adopt thoughtfully. What’s happening (short summary) –

AI is no longer just a buzzword on slide decks. Autonomous AI agents — think software that plans, executes, and follows up on tasks with little human hand-holding — are maturing fast. Combined with retrieval-augmented generation (RAG) and better data integrations, these tools are unlocking real business outcomes: faster reporting, smarter lead qualification, and automated

Story summary There’s been a clear surge in “AI agents” — autonomous, task-focused AI that can take actions (search, summarize, email, run reports, update CRMs) on behalf of people. These agents range from simple scripts that pull and email a weekly sales summary to more advanced systems that handle multi-step workflows across tools. Vendors and

Short summary AI “agents” — autonomous workflows powered by large language models and connected to your systems — have moved from experiments into real business results. Major cloud and app vendors now offer agent frameworks and low-code tools that let teams automate end-to-end tasks: personalized sales outreach, customer-triage, automated meeting notes pushed to CRM, and

Quick summary AI “agents” — autonomous assistants that can run tasks end-to-end (think: qualify leads, draft outreach, assemble reports, trigger follow-ups) — moved from demos into mainstream vendor stacks over the last 18 months. Major platforms and tools (CRM copilots, workflow automation + agent frameworks) now let businesses attach agents to live data and processes.

Quick summary AI agents — autonomous, chainable AI “workers” that can read your systems, take actions, and return results — moved from labs into real business use over the last year. Vendors now offer agent toolkits and connectors (to CRMs, email, docs, BI tools), which means teams can automate complex workflows end-to-end: prospect research +

The story (short) There’s been a clear shift in how companies use AI: instead of just using models for one-off tasks, businesses are now deploying AI agents — autonomous or semi-autonomous software “workers” that can research, take action, update systems, and report results. Sales teams use agents to qualify leads, schedule meetings, and update CRMs.

Quick summary AI agents — autonomous software that completes tasks by reading systems, writing messages, and calling APIs — have moved from proofs-of-concept into real business use. Companies are using agents to draft personalized sales outreach, assemble monthly reports from multiple systems, triage support tickets, and automate repetitive back-office work. The payoff: faster workflows, fewer