Ron Mitchell

Short summary AI agents — autonomous or semi-autonomous software that can research, write, act on systems, and coordinate tasks — moved from labs into real business use in 2024–25. Companies are using them to draft personalized outreach, enrich leads, run recurring analysis, and even update CRMs automatically. That means faster sales cycles, fewer manual errors, […]

Quick summary AI “agents” — small, goal-directed AI assistants that can act across apps, pull data, and run workflows — are rapidly moving from lab demos into real business use. Instead of one-off chat queries, companies are using agents to qualify leads, update CRMs, generate weekly sales reports, route requests, and even handle simple customer

What happened (short summary) – In the last year we’ve seen a surge in practical “AI agents” — autonomous or semi-autonomous AI assistants that can act across apps, pull and push data, and complete multi-step tasks (think: qualify a lead, update your CRM, create a follow-up sequence, and generate a performance snapshot — automatically). –

Quick summary AI agents — autonomous, task-oriented systems built on large language models — have moved from demos to real business pilots. Modern agents can browse data, call APIs, update CRMs, generate reports, and follow multi-step workflows without constant human prompts. That’s making them practical for sales, operations, and finance teams. Why it matters for

Quick summary Major cloud providers and AI startups are turning experimental “AI agents” into production tools. Instead of single chatbots, these agents can call apps, pull data from CRMs, run workflows, and take multi-step actions autonomously. Companies are already using them for sales outreach, order processing, customer triage, and routine finance tasks — and early

The story in one line No-code and low-code “AI agent” builders from big vendors and startups have put autonomous, task-specific AI within reach for non-technical teams. Businesses can now spin up agents that connect to CRMs, calendars, databases, and reporting systems — and act on behalf of people to automate sales tasks, generate regular reports,

The story Over the last 12–18 months we’ve seen a clear shift: autonomous AI agents — systems that can act, fetch data, and take multi-step actions without constant human prompting — are moving out of labs and into real business workflows. Advances like customizable GPTs, agent frameworks (e.g., LangChain-style orchestration), and tighter integrations with CRMs,

First line (hook): AI agents are no longer a sci‑fi promise — they’re being embedded into CRMs, outreach tools, and reporting dashboards to automate routine work and boost productivity. The story in short – Over the past 18 months major vendors and startups have pushed integrated, LLM‑driven agents into business tools. Think copilots that draft

The story (short) Over the past year the conversation around AI has shifted from “what if” to “what now.” Autonomous AI agents — systems that combine large language models with tools, data connectors, and workflow logic — are no longer just research demos (think Auto-GPT and LangChain prototypes). They’re being used in real business contexts:

AI story (short summary) AI agents — self-directed assistants that can browse, query systems, run actions, and hand off to humans — have moved from labs into real business pilots. Companies are using lightweight agents to qualify leads, generate and distribute sales reports, automate routine approvals, and triage customer requests. The common pattern: give an