Ron Mitchell

Short summary Major business software vendors and startups are embedding AI agents that can act — not just advise. These agents can read emails, update CRMs, generate pipeline reports, book meetings, and even trigger follow-up tasks across tools. That shift from “assistive” AI to “action-taking” AI is closing the gap between pilots and measurable business […]

Quick summary AI agents — software that can plan, act, and carry out multi-step tasks — are shifting from experiments to real-world work. Companies are using them to draft outreach, triage customer tickets, extract insights from data, and run recurring reports without constant human supervision. That means faster responses, fewer manual steps, and clearer, real-time

What’s happening Autonomous AI agents — software that can plan, act, and carry out multi-step tasks without constant human prompting — have moved from demos to real business pilots. Over the last year we’ve seen toolkits, connectors, and enterprise-grade agent platforms mature. That means teams can now combine language models, company data, and workflow automations

Quick summary AI agents — autonomous, task-focused assistants that can read systems, take actions, and generate reports — are no longer just prototypes. Major vendors and niche startups have been embedding agents into CRMs, email, calendar apps, and BI tools so they can qualify leads, schedule meetings, update records, and produce automated sales and performance

Short summary Autonomous AI agents — software that can plan, act, and follow up on tasks with little human prompting — moved from research demos into real business pilots in 2023–24. Companies are using these agents to run routine workflows (e.g., lead qualification, expense review, data reconciliation), generate and update reports, and automate repetitive decision

Quick summary AI agents — task-focused AI that can read systems, take actions, and follow business rules — are no longer just experiments. Companies are using agents to qualify leads, draft proposals, automate reporting, and keep repetitive workflows running 24/7. When combined with automation platforms and retrieval-augmented generation (RAG) for accurate context, agents can cut

Summary AI “agents” — goal-driven software that plans and executes tasks with little human prompting — are moving out of labs and into real business workflows. Low-code agent builders and orchestration platforms now let teams create customized agents that handle things like lead qualification, follow-up outreach, recurring reporting, and routine process automation. Why this matters

Short summary AI agents — software that autonomously completes tasks by combining language, actions, and data — have moved from demos into real business pilots. Over the past year more teams have started using agents to draft sales proposals, triage customer requests, run scheduled reports, and automate routine operational workflows. The result: faster turnaround, fewer

Quick summary AI “agents” — LLM-driven assistants that can read your data, talk to apps, and act on your behalf — have moved fast from research demos to practical business tools. Today many organizations are using agents to automate cross-app workflows (CRM → email → calendar → reporting), generate real-time sales and finance reports, and

Quick summary AI agents — autonomous or semi-autonomous software that can read, act, and coordinate across apps — are no longer just developer experiments. Over the last year we’ve seen major platforms and toolmakers add agent frameworks, low‑code builders, and connectors that let these agents interact with CRM, calendars, email, and reporting tools. That makes