Ron Mitchell

Quick summary AI “agents” — autonomous, task-focused software that can research, write, and act inside systems — are no longer academic experiments. Companies are now using them in day-to-day workflows: qualifying leads in CRM, drafting and delivering weekly sales reports, routing and resolving routine customer requests, and orchestrating multistep processes across apps. Why this matters […]

Quick summary AI agents — software that can act on your behalf, complete multi-step tasks, and connect to your systems — have moved from experiment to production. Instead of one-off chat answers, companies are now using agents to run sales outreach, update CRMs, generate executive reports, and automate end-to-end processes. This shift is making AI

Story summary AI agents — software that can plan, act, and complete multi-step tasks on behalf of users — are accelerating from experimental demos into real-world business tools. Companies are deploying agents to handle lead qualification, customer follow-ups, data entry, and routine reporting. The result: faster response times, fewer manual handoffs, and measurable cost savings.

Short summary AI agents — software that can act autonomously to complete tasks — are no longer just a research demo. Companies are using them to qualify leads, automate order and inventory tasks, generate first-draft proposals, triage support tickets, and populate management reports. These agents can connect to CRMs, ERPs, email, and internal databases, acting

The story in brief AI “agents” — autonomous or semi‑autonomous AI assistants that complete multi‑step tasks across systems — are moving from labs to everyday business. Major software vendors and a wave of startups have added agent capabilities to CRMs, help desks, and analytics tools. These agents can schedule meetings, qualify leads, generate regular reports,

Quick summary AI agents — autonomous, task-focused AI that can take actions across apps and systems — are moving from labs into real business use. Recent advances in reliability, API integrations, and agent orchestration make it practical for teams to automate routine workflows: qualify leads, generate personalized outreach, update CRMs, run recurring reports, and even

Summary AI “agents” — autonomous, task-focused systems built on large language models — are no longer just experiments. Advances in LLMs, retrieval-augmented generation (RAG), and plug‑and‑play integrations mean companies are using agents today to qualify leads, automate recurring reports, summarize customer conversations, and run cross-system workflows without heavy engineering. Why this matters for business –

Summary AI agents—autonomous software that can carry out tasks by interacting with apps, data, and people—moved from research demos into everyday business use in 2024–25. Companies are now using agents to do things like draft and route customer responses, run sales outreach sequences, reconcile financial data, and auto-generate recurring reports. The result: faster workflows, fewer

Quick summary AI agents — autonomous, goal-driven software that can read your data, take actions, and follow up — moved from proofs-of-concept to real business deployments in 2023–2024. The combination of agents with retrieval-augmented generation (RAG) and vector databases means companies can run agents against their own documents, CRMs, and reports without sending everything to

Big picture (the story) AI agents — autonomous or semi-autonomous AI “workers” that can read, act, and talk across apps — have moved rapidly from demos to real enterprise use. Over the last year we’ve seen platform-level support (enterprise Copilots, cloud agent frameworks, and low‑code agent builders) plus better governance and observability tools. That combination