Ron Mitchell

Quick summary Autonomous AI agents — tools that can act on your behalf (send outreach, book meetings, update records, generate reports) — moved from experiments to practical tools in 2024–25. They’re being built into CRMs, scheduling apps, and reporting systems so teams can automate repetitive work end-to-end instead of only getting “suggestions” from a copilot. […]

What’s happening There’s been a rapid shift from “AI as a helper” to “AI as an autonomous worker.” Production-ready AI agents and orchestration platforms now let models take multi-step actions across apps (CRM, ERP, helpdesk, spreadsheets) — not just answer questions. In practice that means AI agents can open tickets, enrich records, run reconciliation checks,

Quick summary AI agents — autonomous, task-focused AI that can read your data, take actions, and loop in people when needed — have moved from research demos into real business use. Over the last 18–24 months we’ve seen tooling, integrations, and safety features mature (think agent orchestration libraries, retrieval-augmented workflows, and enterprise connectors to CRMs

Quick summary AI “agents” — models that can act on your behalf to complete multi-step tasks (think: qualifying leads, triaging support tickets, or assembling weekly sales reports) — moved from experiments to real-world use in 2024–25. Businesses are combining large language models with retrieval systems, connectors to CRMs and analytics, and simple automation so these

Quick summary AI agents — software that combines generative AI, connectors to your systems, and simple decision rules — are no longer just experiments. Over the past year we’ve seen more businesses put autonomous agents into production to handle repeatable sales and operations work: lead qualification, personalized outreach, order follow-up, and live reporting dashboards. Better

The story AI agents—software that can carry out multi-step tasks, make decisions, and interact with systems—have moved from labs into everyday business use. Over the last year we’ve seen more enterprise tooling that lets these agents connect to CRMs, finance systems, and reporting platforms. That means companies can automate complex workflows (lead follow-up, deal qualification,

Quick summary AI “agents” — systems that plan and carry out multi-step tasks (think: research, decide, act) using large language models and connected tools — are no longer just experiments. Over the past year, more companies have put these agents into production to automate sales outreach, triage customer support, and generate business reports that once

A quick update AI agents — autonomous, task-focused AI that can read your CRM, send emails, book meetings, and generate reports — are no longer just R&D experiments. Over the past year, more companies have moved these agents into everyday sales and operations workflows. The result: faster follow-up, fewer admin hours, and more timely, actionable

Summary AI “agents” — autonomous, multi-step systems built on large language models that can use tools, access data, and take actions — are no longer just demo tech. Companies are starting to embed agents into everyday processes: drafting and sending outreach, triaging support tickets, generating monthly reports, and running exception-driven workflows. These agents can chain

Quick summary AI agents — autonomous software that can use APIs, calendars, email, CRMs and web data to complete tasks — are no longer a research novelty. Over the past year major platforms and frameworks (enterprise copilots, agent toolkits, and plug‑in ecosystems) have made it much easier to build agentic workflows that qualify leads, book