Ron Mitchell

Quick summary AI “agents” — autonomous or semi-autonomous AI assistants that can follow multi-step instructions, access tools, and act on behalf of users — moved from demos into real business tools. Low-code agent builders, custom GPTs, and integrations with CRMs, calendar systems, and analytics platforms now let teams automate outreach, summarize meetings, generate reports, and […]

Quick summary AI “agents” — lightweight, autonomous apps that can read your data, run tasks, and talk to other systems — have moved from demos into real work. Over the last year, cloud vendors and startups rolled out agent platforms and integrations that let teams automate follow-ups, generate sales reports, triage leads, and trigger workflows

Short summary: AI “agents” — autonomous, goal-driven workflows built on large language models — are no longer just experiments. Teams are embedding agents and copilots into sales stacks, reporting pipelines, and customer ops to handle repetitive work: draft outreach, summarize meetings, pull and reconcile data, and trigger follow-up actions. That shift means faster responses, fewer

Brief summary Major AI platforms and cloud vendors are rolling out low-code and no-code tools for building AI agents — autonomous assistants that can read emails, pull CRM data, run reports, and take actions across apps. Instead of one-off scripts or isolated chatbots, businesses can now assemble agent workflows that connect to internal systems, trigger

Short summary AI agents — autonomous, goal-driven systems that combine language models, workflows, and system integrations — have moved quickly from lab demos into real business use. Companies are now using them to qualify leads, automate follow-ups, generate sales reports, and run routine back-office tasks without constant human oversight. Why this matters for your business

Quick hook AI agents — autonomous tools that can research, draft, follow up, and run routine processes — are no longer just developer experiments. Businesses are piloting them for sales outreach, automated reporting, and process automation, and the early results are turning heads. The story in plain language – What’s happening: New agent-style AI (think

Short summary AI agents — software that can plan, act, and follow up on tasks for you — have exploded from research demos into practical business tools. Over the last year we’ve seen vendors and startups embed agents into CRMs, reporting stacks, and workflow platforms so they can autonomously draft outreach, run recurring analyses, and

Quick summary AI agents — autonomous, task-focused AI that can read systems, take actions, and follow up — have moved from experiments into real business use. Teams are using them to automate sales outreach, update CRMs, generate and deliver regular reports, and trigger workflows across finance, HR, and customer support. That shift matters because agents

Quick summary AI “agents” — small, task-focused AI programs that act on behalf of users — have moved from experiments into real business use. Companies are using agents to pull data, build and distribute reports, enrich leads, trigger follow-ups, and even run simple negotiations or procurement tasks. The result: faster reporting cycles, fewer manual handoffs,

Quick summary AI agents — autonomous software that can read, decide, act and report — are no longer just demos. Over the past year we’ve seen companies move pilots into production: agents that qualify leads, draft proposals, run recurring sales reports, and trigger follow-ups across systems. Improvements in integration tools, lower API costs, and stronger