Ron Mitchell

Quick summary AI agents — software that can act on your behalf, connect to tools, and complete multi-step tasks — moved from research demos into practical business tools over the last 18–24 months. Companies are using agents to automate sales outreach, generate recurring sales and ops reports, triage customer requests, and run parts of finance […]

Quick story Over the last year we’ve seen a surge of low-code “AI agent” builders and marketplaces that let non‑engineers create task-specific bots: lead screeners that qualify prospects, calendar assistants that book demos, and agents that pull CRM data and produce automated sales reports. Those tools are moving from experiments to practical, production-ready uses for

Quick summary AI agents — autonomous systems that combine large language models, tools, and company data to perform multi-step tasks — are no longer just research demos. Over the last couple of years we’ve seen a steady shift: teams are deploying agents to qualify leads, summarize customer interactions, generate recurring reports, and automate routine back‑office

The story (short) Over the last 18 months we’ve seen a clear shift: AI agents are moving out of labs and into everyday business systems. Instead of standalone chatbots, modern agents can connect directly to CRMs, calendars, databases and reporting tools. They can draft personalized outreach, update opportunity stages, generate weekly pipeline reports, and kick

Quick summary Autonomous AI agents — multi-step, goal-oriented systems that can access your apps and data — have moved beyond lab demos. Over the past year we’ve seen low-code agent builders, better agent orchestration, and stronger security features that make agents practical for real business work: lead qualification, automated reporting, exception handling, and 24/7 customer

Short summary AI agents — autonomous, conversational systems that can access your apps, fetch data, and complete tasks — have moved from experiments into real business use. Instead of only answering questions, today’s agents can pull CRM records, generate quotes, update pipelines, and create tailored reports by combining large language models with retrieval systems and

Quick summary AI “agents” — self-directed AI workflows that can plan, act, and follow up — have moved from experiments into real business use. Instead of one-off prompts, agents string together steps (gather data, run analysis, send an email, update a CRM) and keep working until a goal is met. Companies using them report faster

Quick summary AI “agents” — small, goal-directed systems that connect language models to your apps and data — have moved fast from research demos to practical tools. Companies are now using agents to automate tasks like sales outreach, meeting scheduling, pipeline updates, and routine reporting. That makes parts of work faster and cheaper, but it

Quick summary – In the past year we’ve seen a surge in AI agents — autonomous, task-focused software that can research, draft, follow up, and automate workflows across systems. – Companies are using agents for sales outreach, lead qualification, invoice processing, and real-time business reporting — not just prototyping but production deployments. – The result:

Summary AI agents and AI-powered reporting tools are moving out of pilots and into day-to-day operations. More companies are using conversational, task-focused agents to automate routine sales tasks (lead research, follow-ups, CRM updates) and to generate faster, clearer business reports from multiple data sources. The result: fewer manual handoffs, faster decisions, and routine work shifted