Ron Mitchell

Quick summary There’s been a clear surge in practical AI agents — small, task-focused AI programs that can act across apps: pull data, update CRMs, generate reports, and even initiate outreach. Improved connectors, low-code orchestration tools, and better retrieval methods have made these agents far easier to deploy than a year ago. Why this matters […]

The story Low-code AI agent platforms — tools that let non-developers assemble and deploy autonomous “agents” — have gone from hobby projects to enterprise-ready solutions. These platforms combine large language models, retrieval-augmented generation (RAG), connectors to CRMs and databases, and visual workflow builders. The result: teams can spin up AI agents that answer customer questions,

Short summary AI “agents” — small, goal-oriented AI programs that can act on your data and tools — have gone from experimental demos to real business pilots. Companies are using them to do things like qualify leads, run follow-up email sequences, summarize meetings, and generate recurring sales and finance reports automatically. The result: faster workflows,

Quick summary AI agents — autonomous, goal-driven systems that can act across apps and data — have crossed a turning point. What started as research demos and hobby projects is now showing up in commercial low-code platforms and enterprise toolchains. These agents can connect to CRMs, messaging apps, data warehouses and reporting tools, then take

Summary AI agents — software that can act autonomously across apps, pull data, and complete workflows — are moving from experiments into real business use. Instead of only giving answers, modern agents can run sales outreach sequences, generate monthly reports from multiple systems, and kick off approvals without constant human supervision. They’re becoming faster, cheaper,

AI story summary Autonomous AI agents — software that can act on behalf of people to complete tasks, learn from outcomes, and connect with systems — are no longer just research demos. Over the past year, more companies have moved from small experiments to production pilots that automate parts of the sales process, customer follow-up,

The story in one line AI agents — autonomous, task-focused AI that can read your systems, take actions, and report results — moved from lab demos into real business pilots over the last 18–24 months. Big vendors (Copilots from Microsoft and Salesforce), developer frameworks (LangChain, vector stores), and growing case studies have pushed agents into

Quick summary There’s a clear industry shift: enterprise AI agents — software that can read your inbox, update your CRM, schedule meetings, generate reports and even start workflows across apps — are moving out of R&D and into real business use. Major vendors and startups are packaging these agents for secure, controlled enterprise workflows, not

Quick summary AI agents — autonomous software that can carry out multi-step business tasks (think: qualify leads, create reports, or automate routine approvals) — are no longer just R&D experiments. Over the past year vendors and frameworks (Copilot-style assistants, agent toolkits like LangChain, and enterprise-focused platforms) made it practical for companies to run agents safely

Quick summary AI agents — autonomous programs that can read, act, and communicate across apps — are moving from experiments to everyday business tools. Early adopters use them to research prospects, update CRMs, draft personalized outreach, and generate automated sales reports. The result: faster follow-ups, cleaner data, and near-real-time insights that free teams to focus