Ron Mitchell

Quick summary AI agents — models that can act, fetch data, and call tools on their own — are no longer a lab novelty. Companies are combining large language models with retrieval (RAG), API connectors, and low-code automation to build agents that handle tasks like lead triage, invoice processing, and automated reporting. The result: faster […]

Short summary AI agents — autonomous, task-oriented tools powered by large language models and connected to your apps and data — moved from lab demos to real business use in the last 18–24 months. Today’s agents can read dashboards, draft and send emails, update CRMs, and trigger workflows across systems without constant human hand-holding. That

Quick summary Over the last 12–18 months we’ve moved from talking about chatbots and single-model prompts to deploying AI agents — autonomous software that can access your systems, pull data, take actions, and coordinate across tools. Businesses are using agents to generate sales outreach, assemble weekly reports, handle routine customer requests, and automate multi-step processes

Quick summary AI agents — autonomous, task-focused systems that can read your CRM, run queries, draft outreach, and trigger actions across apps — are no longer experimental. Over the last 12–18 months we’ve seen a wave of enterprise-ready agent platforms and orchestration tools that make it practical to automate end-to-end sales and operations workflows. That

Quick summary In the past year, autonomous AI agents—software that can carry out multi-step tasks on behalf of people—have moved from labs to everyday business tools. You’re already seeing them inside CRMs, email apps, cloud suites, and purpose-built “agent as a service” offerings. These agents can research prospects, draft and send personalized outreach, update records,

Quick summary AI agents — autonomous, task-focused systems that can write, fetch, and act across apps — are shifting from experiments to real-world business use. Companies are now using agents to draft proposals, update CRM records, automate recurring reports, triage leads, and run process checks without constant human supervision. Why this matters for business –

Quick summary Autonomous AI agents — software that can plan and run multi-step tasks with little human help — are finally moving out of experimentation and into everyday business use. Low-code agent builders, better integrations with CRMs and ERP systems, and clearer governance patterns mean companies are deploying agents for lead qualification, meeting follow-ups, quoting,

Quick summary AI agents — small, goal-focused AI programs that can act across tools (email, calendar, CRM, chat) — moved from experiments to practical business tools. Today they can draft outreach, follow up on leads, book meetings, and push updates into CRMs automatically. That means teams can move faster, reduce repetitive work, and get near-real-time

What happened Leading cloud and AI vendors have rolled out low-code “agent builders” that let businesses create AI agents connected to internal systems (CRM, BI, calendars, ticketing). These agents can read your data, generate reports, follow up on leads, and trigger workflows — without months of custom engineering. Why this matters for businesses – Faster,

Quick summary There’s a clear shift happening: companies can now build lightweight, task-focused AI agents (think custom “GPTs” or vendor equivalents) that connect to your apps and data. These agents can do things like qualify leads, draft sales outreach, generate weekly reports from CRM data, or triage support tickets — with far less engineering time