Ron Mitchell

Short summary AI agents — autonomous software that can read, act, and follow up across systems — have moved from experiments into real business use. Frameworks and platforms (think AutoGPT-style agents, LangChain integrations, and custom copilots from major cloud providers) are now being used to automate repetitive work: route leads, draft outreach, reconcile data, and […]

What’s happening This year, major AI vendors have made it dramatically easier to build and deploy AI agents — customizable assistants that can complete tasks, run workflows, and interact with systems without constant human direction. Low‑code agent builders, marketplaces for prebuilt agents, and tighter integrations with CRMs, data lakes, and reporting tools mean businesses can

The story (short): Over the past year, AI agents — software that can act on your behalf (draft emails, update CRMs, pull reports, schedule meetings, troubleshoot systems) — have shifted from experimental demos to real, repeatable business workflows. Advances in retrieval-augmented generation (RAG), secure vector databases, and better integrations with enterprise apps mean these agents

Quick summary Companies are increasingly moving from public chatbots to private, enterprise-grade AI agents that run on company data (either on-premise or in a private cloud). This shift is driven by data privacy concerns, new regulation (like the EU AI Act), better open-source models, and clear business wins: faster reporting, automated workflows, and improved sales

Quick summary – What’s new: Autonomous AI agents — small, goal-driven systems built on large language models — are being embedded into real business workflows. Instead of one-off chat answers, agents can run tasks end-to-end: qualify leads, gather and summarize data, update CRMs, or build recurring reports. – Why it’s trending: Advances in multimodal models

Quick summary AI agents — small, purpose-built AI “workers” that can follow instructions, connect to systems, and act on behalf of users — have crossed from lab experiments into real business use. Low-code builders and agent orchestration platforms mean non‑engineers can now create agents to qualify leads, update CRMs, generate weekly sales reports, and handle

Short summary AI agents — autonomous assistants built with large language models — are no longer just experiments. Teams are now using them to draft personalized sales outreach, run routine customer support triage, generate management reports, and automate multi-step workflows across CRM, email, and analytics tools. That shift turns AI from a point tool into

Summary AI agents — autonomous, task-focused AI that can act across apps and systems — moved from lab demos into real business use in 2024. New tools and connectors let these agents read your CRM, generate outreach, update tickets, run reports, and even trigger workflows without a human clicking every step. Why this matters for

Quick summary AI “agents” — autonomous workflows that combine large language models with tools, data, and step-by-step logic — are moving from demos into real business use. Recent months have seen a wave of low‑code builders, better CRM and calendar integrations, and prebuilt agent templates for tasks like lead qualification, meeting scheduling, and automated reporting.

Quick summary Autonomous AI agents — systems that can plan, act, and follow up on tasks without constant human prompting — are no longer just research demos. Over the past year businesses have started embedding agents into sales workflows, customer support, and reporting pipelines. These agents combine large language models, retrieval from company data, and