Ron Mitchell

Quick summary AI agents — software that can act autonomously on your behalf (think: analyze data, write messages, or trigger workflows) — are moving from experiments into real business use. Over the last year we’ve seen cloud vendors and startups ship agent-capable tools that connect to CRMs, data warehouses, and task systems so the agent […]

Summary AI “agents” — software that can plan, act, and follow up on tasks with little human hand-holding — have moved from demos to real work this year. Enterprises are using agent platforms to automate repetitive workflows (meeting scheduling, lead qualification, invoice reconciliation), generate and update business reports, and orchestrate cross‑system actions that used to

Quick summary AI agents — autonomous, task-focused systems that can research, take actions, and talk to your tools — are no longer just experiments. Major vendors and startups are packaging agents into business products that can do things like draft sales outreach, reconcile reports, book meetings, and automate routine approvals. Why this matters for your

Quick summary AI agents — software that can plan and act on your behalf across apps — have moved from lab experiments to real business pilots. Major cloud and AI vendors now offer agent frameworks that connect to CRMs, calendars, email, and BI tools. That means tasks like lead enrichment, meeting follow-ups, and routine reporting

Short summary Over the last year we’ve seen a clear shift: businesses are moving from experimenting with chatbots to piloting autonomous AI agents that can run routine workflows — for example, qualifying leads, generating weekly sales reports, or routing customer requests. These agents aren’t just answering questions; they’re connecting to systems (CRMs, BI tools, ticketing),

Quick summary AI “agents” — software that connects to your apps, pulls data, and takes multi-step actions — are moving out of labs and into business workflows. Instead of a human pulling a sales report, emailing stakeholders, and creating follow-up tasks, an agent can run the report, write an executive summary, update the CRM, and

Quick summary AI agents — software that can act on behalf of people to complete tasks, run workflows, and pull insights from data — are no longer just experiments. Over the last year many teams have started using agents to do things like triage leads, create sales reports, draft follow-ups, and automate recurring operational work.

Summary AI agents — autonomous, task-focused apps built on large language models and connectors to your tools — are no longer just experiments. Companies are using them to run routine workflows end-to-end: sales outreach that personalizes and sequences messages, automated weekly and executive reporting that pulls data from CRM and BI systems, and process automation

Summary AI agents — autonomous, task-focused systems that can read data, take actions, and interact with people — are no longer just R&D experiments. Over the past year we’ve seen cloud vendors and AI platforms mature agent toolkits, and more companies are running production agents for lead qualification, follow-up sequences, and automated reporting. Why this

Quick summary AI agents — autonomous, task-focused AI programs that can read, act, and interact — are moving fast from demos into real business use. Tooling (agent frameworks, cloud agent services, and no-code builders) plus better integrations with CRMs, ERPs, and analytics platforms make it practical to deploy agents for things like lead qualification, customer