Ron Mitchell

Short summary AI agents — autonomous, task-driven AI programs that can fetch data, take actions, and collaborate with other tools — have moved from research labs into real business use. Companies are now using agents to run sales outreach, stitch together CRM and analytics data, prepare weekly reports, and automate routine back-office tasks. The result: […]

Quick summary AI “agents” — autonomous, goal-driven AI assistants that can run workflows, pull data, and take actions across apps — moved from labs into real business use in 2024–25. Companies are using agents to automate sales outreach, generate real-time reports, update CRMs, book meetings, and triage customer issues without constant human oversight. Why this

Quick take AI “agents” — small, autonomous programs that connect to email, CRM, calendars and reporting tools — are moving from experiments into real business use. Companies are using them to qualify leads, send follow-ups, update CRMs, generate pipeline reports, and even draft proposals. That means faster sales cycles, fewer manual errors, and clearer, up-to-date

Quick summary AI “agents” — LLM-powered systems that act across apps and data (think: read your CRM, schedule meetings, draft follow-ups, and update reports) — are moving out of labs and into everyday business workflows. Companies are no longer just testing chatbots; they’re building autonomous assistants that execute tasks end-to-end: outreach and lead qualification, customer

AI story (short summary) AI agents — software that can take multi-step actions, talk to systems, and make decisions — are no longer just a research demo. Over the last two years we’ve seen a wave of business-ready agent platforms and tooling: easy connectors to CRMs and data warehouses, low-code orchestration, better retrieval-augmented generation (RAG)

Quick summary AI “agents” — software that can take multi-step actions on your behalf (draft emails, pull data, update CRMs, generate reports) — are moving from experiments into real business use. Better large language models, easier integrations, and agent toolkits mean companies can automate complex, repeatable tasks without custom engineering for every use case. Why

Summary Major AI platforms are moving from chatbots to autonomous “AI agents” that can act on your behalf — calling APIs, updating CRMs, pulling data for reports, and coordinating workflows across tools. These agents combine language models with connectors, retrieval-augmented generation (RAG), and simple decision rules so they can complete multi-step tasks without constant human

Quick summary AI agents — software that can act autonomously across apps and data to complete tasks — have stopped being a novelty and are becoming practical tools for business teams. Recent product launches and enterprise integrations mean agents can now: – Pull and summarize live data for AI-powered reporting – Update CRMs, qualify leads,

Quick summary AI agents — autonomous software that can read, decide, and act across apps — are moving from experiments into everyday business use. Companies are using agents to automate lead follow-up, update CRMs, generate weekly sales reports, and triage customer requests. The result: faster processes, fewer manual handoffs, and clearer reporting. Why this matters

Quick summary AI “agents” — software that uses large language models plus APIs to act autonomously — have moved from research demos into real business use. Instead of only answering questions, modern agents can qualify leads, pull and summarize CRM/ERP data, run recurring reports, trigger workflows, and follow up by email or chat. Major vendors