Ron Mitchell

Quick summary AI agents — autonomous AI programs that can run tasks, call tools, and carry conversations — have moved from research demos into real business use. Over the last year we’ve seen platforms and enterprise models make agents safer, easier to connect to internal systems, and faster to deploy. That means companies can now […]

Quick summary AI “agents” — AI systems that can use tools, access data, and take multi-step actions — have moved from research demos to real business pilots. Instead of answering a single question, agents can pull CRM data, run analytics, send emails, update records, and generate reports — all in one flow. Why this matters

Quick summary AI “agents” — AI systems that can take multi-step actions, access company data, and work across apps — have moved beyond research demos into real-world pilots. Teams are using them to automate workflows, generate reports, triage customer requests, and assist sales reps. The tech is faster and more reliable than a year ago,

Summary AI agents — small, goal‑oriented AI programs that can act on your behalf — went from experimental to practical in the past two years thanks to no‑code/low‑code builders and easier connections to CRMs, databases, and messaging. Instead of a single query, agents can run multi‑step workflows: research a lead, draft outreach, follow up, and

Quick summary AI “agents” — software that can act autonomously, chain tasks, and talk to other systems — are moving from demos into real business use. Companies are using agents to qualify leads, run routine finance workflows, generate and update reports, and automate multi-step processes that used to require back-and-forth between teams. Why this matters

Quick summary Major AI platforms have pushed “custom agents” (sometimes called GPTs or agent apps) into the mainstream. These agents can connect to your data, follow multi-step instructions, and act autonomously across apps — for example, creating personalized outreach, compiling weekly sales reports, or triaging customer requests without a human typing every step. Why this

Quick summary AI agents — software that can take multi-step actions across apps and systems — have moved past lab experiments into real business use. Over the past year we’ve seen vendor toolkits, low-code orchestration platforms, and templates that let teams build agents for sales outreach, customer triage, invoice processing, and cross-system reporting. Why this

Short summary AI agents — autonomous, task-focused AI that can act on your behalf inside apps and workflows — have moved from research demos to real business use. Across sales, support, finance, and operations, companies are using agents to qualify leads, generate regular reports, route customer issues, and automate repeat processes. The result: faster decisions,

Quick summary AI agents — autonomous, task-oriented applications built on large language models — are moving from experiments into real business use. Companies are using agent builders and orchestration tools to connect LLMs with CRM, ERP, email, and reporting systems so agents can do repeatable work: qualify leads, draft and route proposals, automate monthly reports,

Quick summary AI “agents” — autonomous, task-focused AI tools that can read, act, and follow up across apps — are no longer just demos. Over the last 18 months we’ve seen more businesses deploy agents to handle real work: qualifying leads, drafting personalized outreach, updating CRMs, generating weekly performance reports, and routing exceptions to humans.