Ron Mitchell

Quick summary AI “agents” — autonomous, task-focused AI assistants that can read your data, draft messages, schedule follow-ups, and generate reports — are no longer an experiment. Vendors and platforms have made it practical to embed these agents into CRM, sales engagement, and reporting workflows so they do routine work for reps and ops teams. […]

Quick summary AI “agents” — autonomous assistants that can take actions across apps, run workflows, and generate reports — moved from labs into real business pilots in 2023–2024. Major vendors (e.g., OpenAI, Microsoft, Anthropic) added agent features and integrations, and companies in sales, finance, and operations began using them to automate routine work, speed reporting,

Quick summary AI agents — autonomous, connected AI tools that can read your systems, take actions, and deliver results — are no longer experimental. Companies are using them for lead qualification, scheduling, customer triage, automated financial and sales reporting, and routine operational work. Modern agents combine large language models (LLMs), retrieval-augmented generation (RAG), and secure

Quick summary AI agents — autonomous or semi-autonomous software that can act on your behalf — have moved from research demos into real business use. Companies are now using agent frameworks and low‑code builders to automate sales outreach, generate real‑time reports, and handle routine operational tasks with minimal human oversight. That shift is making workflows

Summary Autonomous AI agents — software that can take multi-step actions on your behalf (think: qualify leads, update CRMs, generate reports, and trigger workflows) — are shifting from experiments into day-to-day operations. Instead of just suggesting text or insights, these agents can complete tasks end-to-end: fetch data, run analysis, create a deliverable, and notify people

Summary AI-driven agents — software that can act autonomously across apps (CRM, email, chat, analytics) — are moving from experiments to everyday business tools. These agents can qualify leads, draft personalized outreach, update records, and generate automated reports with narrative summaries. That means faster response times, fewer manual handoffs, and near-real-time insights for decision-makers. Why

Summary AI “agents” — autonomous assistants that can read your systems, take actions, and follow multi-step workflows — have moved quickly from experiments to real deployments. Today many vendors and platforms let these agents connect to CRMs, calendars, email, and internal documents so they can qualify leads, draft proposals, update pipelines, and generate recurring reports

Quick summary AI “agents” are software bots that can act autonomously across tools: they read documents, pull data from your CRM, run queries, write emails, and even schedule meetings. Over the last year we’ve seen more low-code agent builders, marketplaces, and plug-and-play connectors that let businesses automate multi-step tasks without heavy engineering. Why this matters

Summary AI “agents” — small, goal-directed systems that use large language models to take multiple steps, call APIs, and act on data — have moved fast from research demos into real business tools. Over the last year we’ve seen more off-the-shelf and low-code agent solutions that connect to CRMs, calendars, ticket systems, and data warehouses

Quick summary AI “agents” — autonomous workflows built from large language models plus connectors to apps and data — have moved beyond demos. Teams are now using agents to qualify leads, draft personalized outreach, automate recurring reports, and run routine approval workflows. Tools and frameworks such as LangChain-like agents, platform “GPTs,” and low-code connectors make