Ron Mitchell

Summary In the last year we’ve moved beyond single-chat LLMs to practical AI agents that can run multi-step tasks: pull CRM data, draft outreach, book meetings, assemble quarterly reports, and push updates to dashboards. These agents combine language models with retrieval (RAG), connectors to apps (CRM, calendar, BI), and simple automation rules. The result: routine […]

Quick story Over the last 18–24 months we’ve seen a clear shift: AI agents are no longer just research demos or lab pilots. Companies are connecting LLM-based agents to CRMs, ERPs, ticketing systems and BI platforms to handle real work — generating quotes, triaging support tickets, drafting outreach, and producing near-real-time sales and performance reports.

Quick summary AI “agents” — autonomous workflows powered by large language models that can use tools, access systems, and act on behalf of users — are moving from experimental projects into everyday business use. Vendors and platforms now offer agent templates that connect to CRMs, calendars, analytics, and messaging tools so an agent can enrich

Quick summary Major platforms (Microsoft, Google, Salesforce and others) are embedding AI assistants and agent-style automation into everyday business tools — and companies are moving from experiments to production. These AI agents can draft outreach, triage customer requests, automate repetitive workflows, and generate natural-language reports from live data. That’s lowering the barrier to AI-led productivity

Short summary AI agents — autonomous tools that can connect to your systems, take actions, and carry out multi-step tasks — are no longer just experiments. Vendors and startups are shipping purpose-built agents and low-code platforms that plug into CRMs, calendars, ERPs, and reporting tools. Companies are using them today for lead qualification, automated follow-ups,

Quick story There’s been a clear surge in business interest around AI agents — autonomous or semi-autonomous software that can carry out multi-step tasks (think: qualify leads, update your CRM, compile weekly sales reports, or trigger fulfillment actions). From no-code agent builders to enterprise-grade “copilot” features inside CRMs and BI tools, organizations are moving from

Quick summary – Autonomous AI agents—software that can act on behalf of users, connect to systems, and complete multi-step tasks—are moving from labs into real business workflows. – Companies are already using agents to qualify leads, book meetings, auto-update CRMs, produce monthly sales reports, and handle routine finance reconciliations. – The result: faster cycle times,

Short summary Over the past year the conversation in enterprise tech has shifted from “AI tools” to “AI agents” — software that can act on your behalf across systems: draft emails, update CRMs, pull and explain data, route support tickets, and even trigger downstream automations. These agents are being embedded in CRMs, reporting tools, and

Quick summary AI “agents” — autonomous AI assistants that can access your apps, run tasks, and make decisions — have moved from concept to practical tools for businesses. In the last year we’ve seen a wave of platforms that make it easier to build agents that connect to CRMs, calendars, databases, and messaging tools without

The story — short version Autonomous AI agents — software that can act across apps, take multi-step actions, and learn from outcomes — are moving from demos into real business pilots. Instead of just suggesting email text or summarizing meetings, these agents can qualify leads in your CRM, draft and send follow-ups, update records, and