Ron Mitchell

Summary — what’s happening now AI agents — autonomous, LLM-powered programs that can read your systems, take actions, and follow goals — moved from experiments to practical tools in 2024–25. Companies are using agents to handle routine sales outreach, auto-fill and file reports, triage customer requests, and orchestrate multi-step workflows across cloud apps. Why this […]

Short summary A new wave of AI “agents” — purpose-built systems that can run multi-step tasks, call tools, and interact with people and data — is crossing from experimentation into production. Tools like customizable GPTs and agent frameworks make it easier to automate end-to-end workflows: lead qualification, sales outreach sequences, dynamic reporting, invoice reconciliation, and

Summary AI “agents” — autonomous software that can read, decide, act, and talk to other systems — have gone from research demos to real business tools. In the past year we’ve seen many platforms add agent-building features and low-code marketplaces that let non‑technical teams create agents for tasks like customer follow‑up, lead qualification, and recurring

Quick summary AI “agents” — autonomous tools that can act across apps, pull data, and complete multi-step tasks — are moving from lab demos to real business pilots. Platforms and APIs now let agents connect to CRMs, calendars, databases, and reporting tools. That means companies can automate entire workflows (e.g., lead qualification, weekly sales reporting,

Short summary AI agents — LLM-powered helpers that can read systems, draft messages, and take simple actions — are no longer just lab experiments. Over the past year companies have started embedding agents into CRMs, collaboration tools, and reporting pipelines to automate routine work: update records, summarize meetings, generate customer-facing content, and assemble monthly reports.

Short summary AI agents — autonomous software that can read, decide, and act across tools — are no longer just lab demos. More companies are using agents to automate routine sales tasks, generate and distribute reports, update CRMs, and handle follow-ups. These agents connect to email, calendar, chat, and databases, then run multi-step workflows with

Summary AI agents — autonomous workflows built on large language models plus company data — are finally arriving in production. After a few years of vendor demos and “copilot” pilots (think Salesforce, Microsoft and others), companies are now deploying agents that do real work: draft outreach, update CRMs, summarize meetings, and generate recurring reports. Why

Quick summary AI “agents” — autonomous workflows built on large language models — are moving from demos into real business use. Instead of one-off chat prompts, agents can watch data, take actions across apps (CRM, calendar, email), and run repeatable processes: qualify leads, route opportunities, generate pipeline reports, or draft personalized outreach. Why this matters

Quick summary AI agents — autonomous or semi-autonomous software assistants that can read your data, take actions, and communicate with people — are moving from experiments into real-world workstreams. Sales and operations teams are using agents to qualify leads, send follow-ups, update CRMs, and generate reports automatically. The result: faster response times, fewer manual steps,

Short summary The big story right now: AI agents — autonomous, task-focused AI that can act across apps, pull data, and take next steps — are moving out of labs and into real business workflows. Over the past year we’ve seen more vendor releases, mature connector ecosystems, and dozens of pilots where agents handle sales