Ron Mitchell

The story (short) Over the past year we’ve seen a clear shift: AI agents — autonomous, task-focused AI that can access tools and company data — are moving out of labs and into real business workflows. Large vendors (Copilot-style assistants, platform-built agents) plus open frameworks make it easier to automate customer follow‑ups, generate sales insights, […]

Quick summary AI agents—software that can act autonomously to complete tasks, talk to apps, and follow multi-step processes—have moved from research demos into real business use. Over the last year we’ve seen more vendor integrations with CRMs, calendar systems, and reporting tools, plus low-code agent builders that let non‑technical teams deploy automation faster. Why this

Quick summary AI agents — autonomous, multi-step software helpers powered by large language models and retrieval systems — are moving out of labs and into real business work. Companies are using them to perform tasks that used to require human time and coordination: generating sales outreach, updating CRM records, compiling weekly performance reports, and even

Short summary AI agents — autonomous, task-focused systems built from large language models plus tool access — are moving out of labs and into real business workflows. Instead of only answering questions, modern agents can pull CRM data, draft outreach, create reports, and trigger downstream automation. Companies are using them for sales playbooks, customer triage,

Summary A new wave of AI agents — systems that can plan, act, and carry out multi-step tasks with little human prompting — is moving rapidly from labs into real business workflows. Major platforms and startups have made agent frameworks and customizable “GPTs” easier to deploy, so teams are already using agents to draft outreach,

The story in one line Over the last year, “AI agents” — autonomous, multi-step AI assistants that can access your apps, fetch data, take actions, and report results — moved from experiments into enterprise products. Major platform vendors and startups are shipping agent frameworks and low-code connectors that let teams automate complex workflows end-to-end, not

Quick summary AI “agents” — autonomous, goal-driven workflows built on large language models — are moving from experiments into real sales stacks. These agents can research accounts, write and personalize outreach, book meetings, update CRMs, and generate performance reports without constant human prompting. Combined with retrieval-augmented generation (RAG) and API integrations, they use live company

Quick summary AI “agents” — software that can act autonomously across apps (schedule meetings, update CRMs, run reports, follow up with leads) — are no longer just demos. Over the last 12–18 months they’ve become more robust and enterprise-ready thanks to better data connectors, safer guardrails, and measurable ROI from early adopters. That means more

Summary AI agents — software that can act autonomously to complete multi-step tasks — are moving from lab experiments into real business use. Companies are using agents to handle routine sales outreach, assemble and update reports, triage support requests, and run follow-ups that used to eat salesperson and operations time. The result: fewer manual steps,

Short summary AI “agents” — models that can access connectors, run actions, and fetch company data — moved from experiments to practical tools in 2024. With features like function calling, plugins, and retrieval-augmented generation (RAG), these agents can now pull CRM records, run queries against your data lake, generate sales outreach, and produce regular performance