Ron Mitchell

Summary AI “agents” — autonomous, task-focused systems built on large language models and automation tools — are becoming practical for businesses. Instead of a person running every step, an agent can draft outreach, score leads, update your CRM, and assemble weekly sales reports by orchestrating data, APIs, and LLM reasoning. Why this matters for businesses […]

Why this matters now AI “agents” — autonomous, task-focused AI assistants built from models like custom GPTs and connected to your data — moved from demos into real business use this year. Companies are using them to qualify leads, draft tailored outreach, summarize calls, refresh dashboards, and trigger automations across CRM and workflow tools. For

Quick summary A growing wave of companies are deploying task-oriented AI agents — lightweight, configurable bots that handle specific workflows like lead qualification, follow-up outreach, customer support triage, and automated reporting. Instead of replacing people, these agents take repetitive tasks off your team’s plate, feed cleaned data into your CRM, and produce ready-to-use analytics and

Summary AI “agents” — autonomous, multi-step AI workflows that can research, decide, and act across apps — have moved well beyond lab demos. Tools like agent frameworks and orchestration platforms let businesses combine large language models with your data, CRM, and automation tools so agents can complete tasks end-to-end: update records, generate and send proposals,

Short summary Autonomous AI agents — software that can act on your behalf across apps, run workflows, and even make decisions within pre-set limits — are moving from research demos into real business use. Companies are using agents to qualify leads, update CRMs, generate regular reports, triage support tickets, and automate repetitive approvals. The result:

Quick summary Over the last year we’ve seen a clear shift: AI agents — autonomous systems that combine language models, retrieval (RAG), connectors, and automation — are moving out of research demos and into real business pilots. Companies are using agents to generate automated reports, triage customer inquiries, run sales outreach sequences, and orchestrate multi-step

Summary Major business tools are embedding AI agents into reporting and analytics. Examples include generative-AI copilots in BI platforms (think Power BI and similar tools) that let users ask questions in plain English, generate dashboards, and automate follow-ups. These AI agents can pull from CRM and ERP systems, run analyses, and produce narrative summaries —

Big picture Major AI platforms and toolmakers are shipping low-code/no-code “agent builders” that let non-technical teams create autonomous AI agents that connect to CRMs, calendars, email, and internal databases. Instead of only suggesting text, these agents can take actions — qualify leads, create deals, update records, run reports, and even trigger downstream workflows. Why this

Short summary AI has moved beyond chat windows. Over the last year major vendors and startups have pushed autonomous AI agents — tools that can run tasks, pull data, and act across systems with little human prompting. Businesses are already using these agents for sales research, automated reporting, customer triage, and back-office automation. Why this

What’s happening – AI agents — autonomous, task-focused AI that can read your systems, take actions, and report back — are no longer a novelty. Large vendors and niche startups are embedding agents into CRMs, ticketing systems, and analytics tools so teams can automate outreach, reporting, and routine decisions. – This shift makes it practical