Ron Mitchell

The story (short summary) – AI “agents” — autonomous, goal-driven software that can read systems, take actions, and interact with people — moved from labs into real business use in the last 12–18 months. Major vendors and developer toolkits now make it much easier to build agents that connect to CRMs, ticketing systems, BI tools, […]

Quick summary AI agents — autonomous, multi-step AI assistants that can read documents, pull data, draft messages, and take actions across apps — are moving fast from lab demos to real business use. Companies are already using agents to qualify leads, automate follow-ups, generate weekly reports, and triage customer requests. This trend pairs large language

The story in one line Across industries, AI agents — autonomous, task-focused AI that can access tools, data, and workflows — are moving out of the lab and into day‑to‑day business systems. Low‑code agent builders, plug‑ins for CRMs and reporting tools, and improved retrieval‑augmented generation (RAG) make it easier for non‑technical teams to create agents

Quick summary AI agents — autonomous software that can read, reason, and take actions across apps — are moving from experiments into real business use. Teams are already using agents to automate routine sales tasks, generate reusable reports, and run cross‑system workflows without constant human hand-holding. That shift is making processes faster, cutting repetitive work,

Quick summary AI agents — autonomous software assistants that can act across apps, pull data, and complete multi-step tasks — are moving from experiments to everyday business tools. Companies are using them to qualify leads, draft proposals, run recurring reports, and trigger follow-ups without constant human intervention. The result: faster cycles, fewer manual handoffs, and

Quick summary Major vendors and startups are rolling out enterprise-grade AI agents — autonomous, workflow-capable systems that connect to CRMs, databases, email, calendars, and BI tools. Unlike early chatbots, these agents can take multi-step actions: qualify leads, generate follow-up sequences, update records, pull and summarize sales performance, and escalate issues to humans. New builds emphasize

What’s happening Over the last year the conversation moved from “what if” to “how do we use it.” AI agents — software that can act on your behalf, connect to apps, and complete multi-step tasks — are being built into cloud platforms and enterprise tools. These agents can draft outreach, pull and summarize data for

Quick summary AI agents — autonomous, multi-step AI tools that can plan and execute tasks — have moved from experiments into real business use. Frameworks (like LangChain and vendor-built “GPTs”) plus integrations to CRMs, data warehouses, and BI tools make it practical to automate workflows that used to need human attention: personalized outreach, meeting triage,

Big picture AI “agents” — models that can act, decide, and connect to apps and data — are no longer just research demos. Over the past 12–18 months we’ve seen platform builders and enterprises stitch agents into CRMs, calendars, data warehouses and reporting tools so these agents can do real work: triage leads, draft personalized

Quick summary OpenAI’s rollout of user-customizable “GPTs” made headlines in 2024 because it let non‑developers build tailored AI agents for specific tasks — from drafting emails to answering product questions. That shift pushed AI agents out of the lab and into everyday business workflows: sales assistants, customer-service helpers, and automated reporting bots that can access