Ron Mitchell

Summary AI agents — software that acts on your behalf (drafting emails, updating CRMs, generating reports, scheduling meetings) — have moved from experiments to practical tools you can deploy today. Advances in retrieval-augmented generation (RAG), secure connectors, and low-code agent orchestration mean these systems can safely use your internal data and automate end-to-end tasks. Why […]

Quick summary AI agents — goal-driven systems that can take multi-step actions across apps and data — are moving from labs into everyday business use. Instead of just answering questions, these agents can research leads, update CRMs, generate and deliver reports, triage support tickets, and trigger workflows across tools. That means businesses can automate repetitive

The story (quick summary) – Autonomous AI agents — think LLMs that use tools, databases, and APIs to complete multi-step tasks — have moved from experiments into real business use. Major platforms and low-code vendors now let teams build agents that qualify leads, run follow-ups, compile reports, and automate back‑office workflows. – These agents combine

Quick summary – Over the past 18 months we’ve seen autonomous AI agents — systems that can plan, act, and follow up without constant human prompts — move from demos into real business tools. – These agents are being embedded into CRMs, help desks, and reporting stacks to automate outreach, qualify leads, create summaries, and

Quick story Over the past year major vendors and startups pushed “AI agents” — autonomous, task-focused assistants that can read systems, act on rules, and complete multi-step work. Microsoft’s Copilot Studio, Google’s agent features, and a wave of specialized agent tools made it easy for businesses to stitch LLMs into real workflows (CRM, ERP, helpdesk,

Quick summary In the past year we’ve moved past demos and academic proofs-of-concept: AI agents — autonomous workflows that can read data, take actions, and coordinate across apps — are being adopted by real teams to automate tasks, generate reports, and speed decision-making. These agents can run routine processes end-to-end (for example: gather sales data,

Quick summary AI agents — autonomous, task-focused AI tools that can research, draft outreach, update systems, and trigger actions — are moving from experiments into day-to-day business use. Companies are using them to automate repetitive sales tasks, generate near-real-time reports, and run small decision loops without constant human supervision. That shift is lowering costs, speeding

Quick summary Autonomous AI agents — configurable AI “workers” that can read data, take actions, and coordinate across apps — are no longer just research demos. Major platforms have made it dramatically easier to build custom agents that plug into CRMs, reporting tools, calendars, and customer platforms. That means businesses can now automate complex sales

The story (short) AI “agents” and copilots — systems that act on your behalf across apps and data — are moving fast from tech demos into real business use. Companies are using them to draft emails, fetch and combine data for reports, triage customer requests, and automate repetitive workflows. The difference now is these agents

Quick summary AI “agents” — autonomous, goal-driven tools that can read, write, call APIs, and act across apps — have stopped being just a developer curiosity. Over the last year organizations have moved from experiments to real production use: agents are scheduling follow-ups, drafting outreach, triaging support tickets, and generating executive-ready reports automatically. The result: