Ron Mitchell

Quick summary There’s been a clear surge in enterprise adoption of AI agents — lightweight, task-focused AI that can act on your behalf (draft emails, qualify leads, pull and summarize data, update CRMs, run reports). Businesses are moving beyond experiments: teams are embedding agents into day-to-day workflows to save time, speed decisions, and make reporting […]

Short summary AI “agents” — software that combines large language models with tools (calendars, CRMs, web search, databases and automation) — have moved from demos into real business pilots. Over the last year many companies have started using agents to do repeatable sales and operations work: qualify leads, draft outreach, update CRMs, generate weekly reports,

Summary Autonomous AI agents—software that can take multi-step actions on behalf of users—have moved from lab experiments into real business tools. Over the last 18 months we’ve seen lightweight agent frameworks, vendor agent APIs, and out-of-the-box integrations that let teams automate outreach, qualify leads, generate tailored proposals, and build live dashboards without heavy development cycles.

Quick summary AI “agents” — autonomous, multi-step AI assistants that can use tools, call systems, and follow workflows — are moving from labs into real business use. Big vendors (Copilot, Einstein GPT, Google/Anthropic integrations) and open-source frameworks (LangChain, Auto-GPT style orchestration) are making it simpler to build agents that do things like qualify leads, run

Quick story summary Major cloud and SaaS vendors have shifted AI from “assistive chat” to action: modern AI agents—LLM-driven services that can access your data, run workflows, and take multi-step actions—are being embedded into CRMs, BI tools, and service platforms. That means software can now draft proposals, run monthly revenue reports, triage support tickets, and

Quick summary AI agents — autonomous software that can read data, take actions, and talk to other apps — moved from research demos to real business tools. Advances in retrieval-augmented generation (RAG), vector databases, and low-code connectors now let companies safely automate workflows that used to need human hand-holding: updating CRM records, generating weekly sales

Quick summary Major vendors and startups are making it easier than ever to build task-focused AI agents that run workflows, query your systems, and produce usable outputs (reports, outreach sequences, meeting summaries). These agents combine large language models with retrieval (your data), connectors (CRM, finance, BI), and simple orchestration so they can act — not

Quick summary AI “agents” — autonomous, workflow-focused AI that can fetch data, draft messages, update systems, and trigger actions — have moved from demos into real business use. Vendors (from low-code platforms to large SaaS providers) are shipping agent templates for sales outreach, automated reporting, customer follow-up, and back-office automation. That means companies can string

Start: The story in plain terms The “agent” wave in AI—models that can take actions, call tools, and run multi-step workflows without constant human prompts—moved from experimental to enterprise-ready this year. Platforms and APIs now make it easier to connect an agent to your CRM, calendar, data warehouse, and reporting tools so it can do

Quick summary In the last year we’ve seen a clear shift: autonomous AI agents — tools that can perform multi-step work across apps (think: read CRM data, draft outreach, update records, and generate a weekly dashboard) — are moving from experiments into day-to-day business use. Improvements in agent orchestration, safer prompt frameworks, and easier API