Ron Mitchell

Short summary AI agents — systems that combine language models, retrieval (RAG), and tool access — are moving from labs into real business workflows. Over the last year we’ve seen these agents reliably perform end-to-end tasks: research leads, assemble and update CRM records, generate executive reports, and even trigger follow-up actions across apps. That shift […]

Quick summary Autonomous AI agents — systems that can take multi-step actions (qualify leads, send personalized outreach, update CRMs, generate reports) — have moved from experiments to real business pilots. Better integrations (CRM connectors, vector search, scheduled workflows) and cheaper compute mean these agents can run routine sales and operations tasks end-to-end. At the same

Quick summary AI agents — autonomous, goal-driven software that can research, act, and report — are moving from proof-of-concept demos into real business systems. Over the past year we’ve seen CRMs, marketing platforms, and analytics tools add agent-style features: automated lead outreach, dynamic campaign optimization, and AI-powered reporting that summarizes performance and suggests next steps.

The story (short) Autonomous AI agents — not just chatbots — are becoming a practical tool for frontline teams. Instead of only answering questions, these agents can perform multi-step work: pull data from your CRM, run rules, create reports, update systems, and trigger human approvals. Over the last couple of years the combination of more

Big picture (the story) Over the past year the market has shifted: major vendors and startups alike are making it practical for companies to deploy autonomous AI agents that do real work — not just generate text. These agents can prospect and qualify leads, run personalized outreach, update CRMs, automate routine workflows, and produce near-real-time

Quick summary – Over the past year businesses have moved from experimenting with chatbots to deploying autonomous AI agents — small, goal-oriented systems that can run sales tasks, qualify leads, generate reports, and trigger workflows without constant human prompting. – These agents combine large language models, retrieval (RAG), and integrations (APIs/CRMs) so they can read

Quick summary Major AI platforms have made it much easier for companies to build and deploy custom AI agents — little autonomous assistants that can read your CRM, pull numbers from spreadsheets, summarize meetings, draft outreach, and even trigger routine actions. These tools are moving from tech demos to real business apps because you no

Short summary AI agents — autonomous, tool-using systems built on large language models — are no longer just a research demo. Companies are using them to draft and personalize sales outreach, triage customer requests, generate routine reports by pulling data from multiple systems, and automate repetitive back-office steps. These agents combine AI reasoning with direct

Short summary AI agents — software that can carry out multi-step tasks (like qualify a lead, draft a proposal, update a CRM, or compile a monthly report) — moved quickly from demos to real business use in 2023–24. Companies are now connecting these agents to CRMs, data warehouses, and workflows so the systems can act

Quick summary A growing number of companies are embedding AI agents directly into sales and operations workflows — not just chatbots, but semi-autonomous agents that research leads, draft outreach, update CRMs, and generate repeatable reports. These agents combine large language models, retrieval-augmented generation (RAG), and automation tools to complete multi-step tasks with minimal human supervision.