Ron Mitchell

Summary AI-powered agents — autonomous, multi-step assistants that can connect to your CRM, calendar, documents, and other systems — are no longer experimental. Businesses are now using them to qualify leads, draft personalized outreach, summarize meetings, and generate automated reports that update in real time. Why this matters for business – Faster revenue motions: Agents […]

Recent trend (short summary) AI agents—software that can plan, act, and carry out multi-step tasks with little human direction—are no longer just research demos. Over the past year we’ve seen more vendors and cloud platforms package agent frameworks that connect LLMs to company systems (CRM, ERP, knowledge bases) and to automation tools (RPA, APIs, workflow

Quick summary Autonomous AI agents — software that can read your company data, make decisions, and take actions (like sending emails, updating CRMs, or generating reports) — moved from experiments to practical tools in 2024–25. Instead of only giving suggestions, modern agents can execute multi-step workflows, coordinate with calendars and apps, and deliver automated status

Short summary AI agents — autonomous software that can follow instructions, access systems, and complete multi-step tasks — are moving out of labs and into real business workflows. Teams are using agents to triage leads, generate and update CRM entries, assemble weekly sales reports, and automate routine customer messages. That shift matters because it turns

Short summary AI agents — autonomous virtual assistants that can run workflows, call APIs, and fetch company data — are no longer just experiments. Over the past year large vendors and startups have made agent frameworks reliable enough for real work: they can qualify leads, book meetings, update CRMs, and generate recurring reports with minimal

The story (short) Autonomous AI agents — systems that combine large language models with tools, data retrieval, and workflow orchestration — are moving from research demos into real business work. Instead of a person prompting an LLM for a single answer, agents can run multi-step tasks: qualify leads, update CRMs, pull and synthesize sales reports,

Short summary A new wave of no‑code and low‑code AI agent platforms has made it practical for businesses to build autonomous assistants that act across apps — scheduling, qualifying leads, updating CRMs, generating reports, and routing exceptions. These “agents” combine large language models with connectors to company data and simple decision rules so they can

Quick summary AI agents — small, goal-focused systems that can chain together steps, access data, and act on behalf of users — have moved from research demos into real business pilots. Companies are now using agents to draft and send personalized sales outreach, update CRMs, generate and distribute weekly performance reports, and automate routine ops

Summary A new wave of AI agents — small, task-focused AI systems that can act autonomously — is moving from labs into real business workflows. Paired with AI-powered reporting that turns raw data into clear narratives and recommendations, these tools let teams do more with less: qualify leads, generate outreach, draft proposals, run recurring analyses,

AI agents move from proof-of-concept to profit — what business leaders should do next Summary of the story Over the last 12–18 months, AI “agents” — software that can take actions, call APIs, and run multi-step workflows — have moved from research demos into real business use. Major cloud providers and open-source toolchains released agent