Ron Mitchell

Quick summary AI agents — autonomous, goal-directed AI “helpers” that can act across apps, fetch data, and complete tasks — have moved from labs into everyday business tools. Major cloud vendors and open-source frameworks now make it practical to deploy agents that do real work: generate sales outreach, consolidate reporting, handle routine customer interactions, and […]

Quick summary AI agents—autonomous workflows that combine language models, data connectors, and action tools—are moving from experiments into real business use. Instead of a person running multiple apps and copy/paste, an agent can pull CRM data, qualify leads, send personalized outreach, and update your pipeline — or generate weekly performance reports with narrative insights and

Quick story AI “agents” have moved from lab experiments to real business tools. Modern agents can connect to CRMs, calendars, email, knowledge bases and data warehouses to complete multi-step tasks — for example, qualify a lead, create a personalized proposal, update the CRM, and schedule a follow-up — with minimal human handoff. Large vendors and

Recent story (short summary) AI “agents” — software that can take multi-step actions across apps and systems on its own — have moved from demos into real business use. Instead of just answering questions, these agents can pull CRM data, draft personalized outreach, schedule meetings, and generate up-to-date sales and operations reports. Companies are starting

Quick summary AI agents — autonomous, task-focused systems powered by large language models — are moving from experiments to real business tools. When connected to company data (sales CRM, knowledge bases, ERP), these agents can draft proposals, generate up-to-date reports, triage customer requests, and automate repetitive sales and operations tasks with minimal human oversight. Why

Quick summary AI is shifting from chat-based assistants to autonomous, task-focused AI agents. These agents can take multi-step actions — e.g., research leads, draft personalized outreach, update CRM records, generate weekly sales reports, and even trigger follow-up workflows — without a user typing every step. That capability is rapidly being adopted by teams in sales,

Short summary AI “agents” — autonomous, task-focused AI assistants that read your documents, run workflows, and answer questions — are no longer a novelty. Companies are using retrieval-augmented generation (RAG) and small chains of agents to automate routine sales tasks, generate timely reports, and speed up contract and onboarding workflows. The result: faster responses, fewer

Quick summary – What’s happening: Over the last year we’ve seen autonomous AI agents — systems that can execute multi-step tasks, call APIs, and coordinate other tools — move out of labs and into real business workflows. Companies are using agents for things like lead qualification, automated outreach, customer triage, invoice processing, and recurring reporting.

Quick summary AI agents — autonomous, LLM-driven tools that can read your systems, take actions, and follow multi-step workflows — are moving from lab experiments into real business use. New low-code agent builders, improved large models, and better ways to connect agents to company data (secure APIs, retrieval-augmented generation) mean teams can now automate tasks

Quick summary AI “agents” — autonomous workflows that use large language models to run tasks, call APIs, and interact with apps — have moved from demos into real business use. Instead of just answering questions, modern agents can research prospects, enrich CRM records, draft outreach, schedule meetings, and auto-generate reports across systems. That shift means