Ron Mitchell

Quick summary AI agents — software that can act autonomously across apps and data — are moving from experiments into everyday business use. Modern agents can read your CRM, draft personalized outreach, update records, trigger follow-up tasks, and create live reports without constant human direction. Combined with secure data connectors and retrieval-augmented generation (RAG — […]

Quick summary AI agents — autonomous, task-focused AI that can read systems, take multi-step actions, and learn from outcomes — are moving from labs into the day-to-day of sales, operations, and reporting. Instead of a person copy-pasting between screens, these agents can qualify leads, generate tailored proposals, update CRMs, and assemble executive reports automatically. Why

Quick summary Big software vendors and startups are no longer just talking about “AI agents” as experiments. Over the past year we’ve seen AI agents rolled into productivity suites, CRMs, and reporting tools so they can complete end-to-end tasks — for example: qualify leads, summarize meetings, generate and distribute sales reports, and trigger downstream automations.

Summary AI agents — autonomous software that can plan, act, and follow up on tasks using LLMs, retrieval systems, and automation — are moving from lab experiments into real business workflows. Frameworks and tools like LangChain, AutoGen, and commercial offerings (e.g., Copilot-style agents) make it faster to build agents that handle things like customer follow-ups,

Quick summary AI “agents” — autonomous assistants that connect to your systems, take actions, and run multi-step workflows — have moved from labs into real business use. Instead of only answering questions, agents now book meetings, create and send personalized sales outreach, assemble monthly reports, and trigger approvals across CRM, ERP, and ticketing systems. Why

Quick summary AI agents — autonomous, multi-step AI programs that can access apps, APIs, and data — moved fast from demos into real business use in 2024. Major cloud providers and startups released agent platforms and frameworks that make it easier to build assistants that research prospects, draft outreach, update CRMs, and generate BI-ready reports

Summary AI agents — small, goal-oriented AI programs that act on your behalf across apps (email, CRM, calendar, chat) — are moving from experiments into everyday use. Instead of just generating text, modern agents can qualify leads, book demos, follow up on proposals, update records in your CRM, and summarize customer interactions. That means faster

Summary – Over the past year, AI “agents” — small, goal-directed systems that can run multi-step tasks, call APIs, and coordinate data — have moved from demos to real business pilots. Tools and frameworks like LangChain and Auto-GPT (and enterprise platforms that build on them) make it easier to automate workflows that used to need

Quick summary There’s been a clear surge in enterprise interest around autonomous “AI agents” — software that can run multi-step tasks on its own, connect to your apps, and make decisions or draft outputs without constant human prompts. At the same time, companies are deploying AI-powered reporting and automation to turn data into readable, actionable

Quick summary AI agents—software that can act autonomously across apps, pull data, and complete multi-step tasks—are moving from developer demos into real business use. Rather than just generating text or answers, modern agents can open tickets, run reports, update CRMs, and trigger approvals without constant human prompts. That shift is making automation and reporting faster,