Ron Mitchell

Summary AI agents — autonomous, task-focused AI that can read your systems, take actions, and produce outputs without constant human prompts — are moving from lab demos into real business use. You’re seeing them schedule follow-ups, enrich CRM records, generate weekly sales reports, and trigger workflows when opportunities heat up. Companies that combine these agents […]

Quick summary AI agents — purpose-built, semi-autonomous AI “workers” that can read data, take actions, and follow simple rules — moved from proof-of-concept into real business use in the past year. Cloud vendors and frameworks (think custom GPTs, LangChain-style agents, and vendor “agent” features) have made it easier to build agents that handle tasks like

Quick summary AI “agents” — software that can act autonomously to complete tasks (draft emails, run reports, book meetings, triage customer requests) — have moved out of research demos and into real business pilots. Vendors and open-source projects now make it easier to chain LLMs with tools, calendars, CRMs and databases so agents can do

What’s new AI “agents” — models that can take multi-step actions across apps (email, calendars, CRMs, BI tools) — moved from proofs-of-concept to reliable business tools in the past year. They now combine better integrations, memory, and guardrails so they can run repeatable workflows (like lead follow-up or report generation) with less human supervision. Why

The story in short AI agents — software that can plan, act, and talk to other apps — moved from research demos into real business use in 2024–25. Open-source frameworks and low-code platforms made it much easier to build agents that can draft outreach, pull live data, run analyses, and automate follow-up tasks across CRM,

Quick story AI “agents” — autonomous software that can read your CRM, schedule meetings, pull numbers, write emails, and trigger actions across apps — have moved from lab experiments into real business workflows this year. More companies are deploying these agents to handle recurring tasks like lead qualification, sales outreach, expense reporting, and executive briefs.

Quick summary Major vendors and startups are embedding autonomous AI agents into sales and operations workflows. These agents can qualify leads, draft personalized outreach, update CRMs, and generate routine reports — often by combining an LLM with company data (retrieval-augmented generation). The result: repetitive work drops, pipeline visibility improves, and teams spend more time on

Quick summary A new wave of enterprise-ready AI agents and orchestration tools has moved beyond research demos into real business pilots. These agents can execute multi-step workflows (think: pull CRM data, draft outreach, schedule follow-ups, and update reports) with far less human hand-holding than previous generation chatbots. Vendors and open-source frameworks have focused on retrieval-augmented

Summary AI agents — software that uses large language models plus APIs to act autonomously across apps — are no longer just a research demo. Businesses are now connecting these agents to CRMs, calendars, email systems, and reporting tools so they can qualify leads, schedule demos, update pipelines, and generate sales reports without constant human

Summary — what’s happening now – Lately, a new class of AI tools called “agents” has moved from demos into real business pilots. These agents can connect to your apps (CRM, calendar, email, spreadsheets, dashboards) and perform multi-step work on their own: qualify leads, enrich records, draft outreach, update pipelines, and generate executive reports. –