Ron Mitchell

The story in plain terms AI agents — autonomous, task-oriented systems built on large language models plus tools (search, CRM connectors, databases) — have crossed from lab demos into real business workflows. Big vendors (Microsoft Copilot in 365/Dynamics, Google Duet/Workspace) and an ecosystem of agent frameworks and vector databases have made it realistic to automate […]

Quick summary AI “agents” — systems that can act on your behalf across apps and data — moved from lab experiments into real business pilots in 2024–25. Companies are using agents to do things like qualify leads, generate weekly sales reports, manage scheduling, and run multi-step workflows without constant human hand-holding. Why this matters for

Quick summary Autonomous AI agents — software that can carry out multi-step tasks with little human direction — moved from research labs into real business tools over the last 18 months. Platform vendors rolled out agent builders (custom GPTs, Copilot studios, and similar tools), and companies started using agents for things like automated lead qualification,

Quick summary Companies are rapidly adopting AI agents — small, goal-driven systems that use large language models plus internal data to perform tasks (think: summarize calls, qualify leads, draft personalized outreach, or auto-build weekly reports). In 2023–24 we saw these agents move from experiments into practical pilots because two things got better: retrieval-augmented knowledge (so

Quick summary AI “agents” — task-oriented AI that can act, plan, and follow workflows — are moving out of labs and into real-world business use. Major cloud vendors and AI platform providers have released agent frameworks and integrations that let companies automate multi-step tasks: qualify leads, generate quotes, summarize meetings, and produce recurring sales and

The story (short summary) AI “agents” — autonomous, task-focused AI assistants built on models like GPT — have moved beyond demos. Over the last year we’ve seen a wave of tools and platforms that let teams create low-code agents to handle specific workflows: qualifying leads, updating CRMs, summarizing meetings, and generating regular sales and financial

Quick story summary Major cloud and AI vendors are rolling out enterprise-ready AI agent platforms — low-code/no-code tools that let businesses build autonomous agents to do things like generate reports, automate follow-ups, and connect to CRMs and company data. These agents combine large language models with connectors, retrieval systems, and basic governance so they can

What’s happening AI “agents” — autonomous assistants that can access systems, run multi-step workflows, and generate reports — have moved from experiments to real business use. Improvements in models, tool integrations, and secure retrieval (RAG) mean these agents can now pull from your internal data, interact with CRMs, trigger actions, and produce AI-powered reporting with

Story summary AI agents — autonomous systems built on large language models that can connect to tools, fetch data, and act on behalf of users — have moved out of demos and into real enterprise pilots. Over the last 18–24 months, toolkits and “agent” frameworks made it far easier to glue models to CRMs, data

Short summary AI “agents” — software that can act on your behalf across apps — plus natural‑language reporting tools are moving from labs into real business use. Over the past year vendors and startups have shipped agent features that can draft emails, run CRM outreach, pull data from BI systems and produce narrative reports automatically.