Ron Mitchell

Quick summary AI “agents” — autonomous workflows that connect language models to your apps and data — have moved from experiments to practical business tools. Instead of just answering questions, today’s agents can pull CRM records, draft personalized outreach, update systems, run reports, and trigger follow-ups without constant human prompting. Why this matters for businesses […]

Quick summary AI “agents” — autonomous workflows built from large language models, retrieval-augmented knowledge, and automation tools — are moving from lab experiments into real business use. Instead of a human opening apps and copying information, agents can read CRM records, draft outreach, schedule meetings, update reports, and trigger downstream systems with minimal supervision. Why

Quick story Across 2024–25 we’ve seen a clear shift: AI agents—software that autonomously completes multistep tasks by talking to apps, pulling company data, and writing or taking actions—moved from experiments into real business use. Vendors are packaging connectors, retrieval-augmented models, and low-code agent builders so teams can automate workflows that used to be hand-offs between

Quick summary AI agents — autonomous tools that can read systems, take actions, and carry out multi-step tasks — have moved from prototypes to real business pilots. Companies are using agents to do things like qualify leads, draft and send personalized outreach, update CRMs, and pull together cross-system sales reports without manual spreadsheets. Why this

Quick summary There’s a clear shift: commercial “AI agents” — autonomous, task-focused AI that can read your systems, act on data, and carry out workflows — are moving out of labs and into real business use. Low-code/no-code agent platforms and tighter connectors to CRMs, ERPs, and data warehouses now make it practical for teams to

Quick summary AI agents — autonomous, task-focused AI assistants that can read your CRM, send emails, update records, and generate reports — are no longer just demos. Over the past year, more companies have put agents into production to handle lead qualification, follow‑up cadences, scheduling, and routine reporting. That shift is driven by easier integrations

Quick summary AI agents — autonomous tools that can take actions (write emails, update CRMs, generate reports, follow up with leads) — have moved from experimental demos into real, day-to-day business use. Companies are combining large language models, automation platforms, and CRM data to create agents that handle specific tasks end-to-end, not just suggest text.

Quick summary AI agents — autonomous, task-focused AI assistants built on large language models — went mainstream in 2024 thanks to no-code builders and enterprise APIs. Instead of one-off chatbots, businesses can now deploy agents that handle recurring workflows: read CRM records, draft outreach, generate weekly pipeline reports, and trigger automations across tools. Why this

Quick summary AI agents — purpose-built systems that combine large language models, connectors, and automation tools — are moving from tech demos into real business use. Companies are now using agents to generate routine reports, qualify leads, update CRMs, and automate parts of finance and support workflows. These agents can act autonomously on structured data,

Short summary AI agents — software that can act, decide, and complete tasks on behalf of people — are no longer just research demos. Companies are putting autonomous and semi‑autonomous agents into real workflows: qualifying leads, updating CRMs, running routine customer service, and generating weekly sales reports automatically. The result: faster processes, fewer repetitive tasks,