Ron Mitchell

Quick summary AI “agents” — autonomous assistants that act on data, tools, and workflows — are moving out of labs and into business teams. Instead of just answering questions, modern agents can pull CRM data, draft outreach, update pipelines, generate weekly reports, and trigger follow-up tasks automatically. This is powered by large language models + […]

Quick story AI “agents” — autonomous, LLM-powered programs that can read your systems, take actions, and generate reports — have moved from lab demos into real enterprise pilots. Organizations are using agents to qualify leads, update CRMs, draft follow-up emails, schedule meetings, and produce fast sales and performance reports. The result: repetitive work gets faster,

Quick summary Over the last year we’ve moved from “chatbots and prototypes” to real, production-ready AI agents that can act across apps — qualify leads, summarize meetings, update CRMs, generate weekly reports, and trigger workflows without constant human hand-holding. Major AI platforms and no-code agent builders have made it easier for non-engineers to assemble agents

Short summary AI agents — autonomous or semi-autonomous AI programs that carry out multi-step tasks — are no longer just a research headline. Over the past year these platforms have matured: low‑code orchestration tools, better connectors to CRMs and ERPs, and improved guardrails mean companies can safely automate complex workflows (lead qualification, order processing, recurring

Summary A new wave of production-ready AI agents is making it practical for companies to automate complex, cross-system work — not just chat. These agents can qualify leads, update CRMs, prepare weekly reports, route invoices, and run multi-step customer follow-ups with minimal human handoff. Thanks to better APIs, low-code builders, and tighter integrations with business

Quick summary AI agents — software that can take multi-step actions for you (think: scheduling, following up on leads, generating reports, and updating systems) — are no longer just a research demo. Businesses are adopting agents to automate recurring workflows, speed decision-making, and reduce manual work across sales, ops, and finance. Why this matters for

Summary AI agents — autonomous or semi-autonomous “copilots” built on large language models — have moved from demos into real business use. Companies can now create custom agents that connect to CRMs, calendars, databases, and reporting tools to automate routine tasks: generate sales outreach, compile weekly KPI reports, triage support tickets, and kick off workflows

Summary Autonomous AI agents—software that can carry out multi-step tasks, make decisions, and talk to systems on their own—are moving from labs into real business use. Companies are using agents to qualify leads, draft and send personalized outreach, update CRMs, generate reports, and even route customer issues to the right person. The payoff is faster

Story summary AI “agents” — models that can plan, take actions, and use tools on behalf of users — have moved from research demos to real business pilots. Over the past year we’ve seen major platform vendors release easier ways to build custom agents and connect them to company systems (CRMs, databases, email, analytics). That

Quick summary AI agents — software that can plan, act, and complete multi-step tasks without constant human prompts — are no longer just lab experiments. Over the past year we’ve seen a wave of agent orchestration tools, plug‑and‑play connectors for CRMs and ERPs, and prebuilt workflows for sales, customer service, and finance. That means businesses