Ron Mitchell

Short summary AI agents — autonomous assistants that can read your systems, take actions, and talk to people — have moved out of experiments and into everyday business apps. You’re seeing them in CRMs, meeting tools, and reporting suites where they qualify leads, summarize meetings, update records, draft follow-ups, and generate sales and operational reports […]

Summary AI “agents” — autonomous software that can read, write, act, and connect systems — are no longer just lab experiments. Over the past year we’ve seen more companies deploy agents to handle routine sales tasks, customer triage, and real-time reporting. Instead of a human copying and pasting between apps, an agent can qualify leads,

Big idea in plain terms Autonomous AI agents — tools that can carry out tasks across apps with little human prompting — moved from labs into real business use in 2024. They can draft personalized outreach, update CRMs, generate weekly reports, and even coordinate cross-team tasks. Companies are already using them to cut manual work,

Quick summary AI agents — autonomous, task-focused AI that can read, decide, and act — are no longer just demos. Advances in large language models, retrieval-augmented workflows, and integrations with business systems mean agents can now handle real tasks: draft personalized outreach, update CRMs, generate weekly reports, and triage customer issues. That shift turns AI

Short summary The last year has seen a fast-growing wave of AI agents — small, goal-driven systems built on large language models that can read your data, take multi-step actions, and integrate with apps via APIs. Companies can now spin up customizable assistants (think sales copilots, finance auditors, or automated reporting agents) without building a

Big idea (short): Autonomous AI agents — software that can act across apps, pull context, and complete multi-step tasks — are moving from labs into real business use. Sales, operations, and finance teams are using agents to automate repetitive work (outreach, meeting prep, reporting) and free people for higher-value activities. Why this matters for business

The story (short) AI agents — autonomous software built with large language models that can use tools, access your data, and carry out multi-step tasks — are no longer just demos. Across industries, teams are combining LLMs, retrieval-augmented generation (RAG), and automation connectors to let agents handle work like lead research, outreach sequencing, order updates,

Summary Over the past year major AI platform vendors made it much easier to build “AI agents” — autonomous, LLM-powered assistants that can read your systems, take actions, and produce human-readable outputs like reports, emails, or sales playbooks. Instead of one-off chatbots, businesses can now deploy agents that join workflows: draft proposals from CRM data,

Quick summary AI agents—software that can act on your behalf, carry out multi-step tasks, and talk to other systems—have moved from experiments into everyday business use. Big vendors (and many startups) are shipping agent capabilities that integrate with CRMs, calendars, data warehouses, and messaging tools. That means these systems can now qualify leads, schedule meetings,

Hook: This year saw enterprise-grade AI agents move from experiments into real business tools. They can now work across apps, pull private data, run workflows, and produce automated reports — and that changes how sales and operations get work done. The story in brief – Major cloud and AI vendors have pushed “agent” capabilities into