Ron Mitchell

Quick story AI “agents” — autonomous assistants that can read data, take actions, and talk to systems — have moved from demos into real business use. Major AI platforms and enterprise tools now let companies create custom agents that connect to CRM, ERP, email, and reporting systems. Early adopters are using them to qualify leads, […]

Summary Major AI platforms and toolmakers have made it much easier to build “autonomous agents” — AI that can act on behalf of users: qualify leads, draft outreach, schedule meetings, update CRMs, and generate routine reports. Early adopters are running pilots that move repetitive work out of inboxes and spreadsheets, freeing reps and ops teams

Short summary Companies from Microsoft and Salesforce to smaller SaaS vendors are embedding AI agents into daily tools — not just chatbots, but agents that schedule, qualify leads, pull together reports, and trigger actions across systems. These agents combine large language models with company data and automation so they can complete multi-step tasks instead of

Quick summary AI agents — autonomous, multi-step AI programs that can use tools, talk to systems, and complete tasks — are no longer an experiment. Over the last year major platforms have released agent frameworks and tool integrations that let businesses automate complex workflows end-to-end: think automatic lead qualification that books demos, recurring financial close

Story summary AI “agents” — autonomous, goal-driven AI that can access systems, take actions, and carry out multi-step workflows — are shifting from experimental pilots into real business operations. Companies are using agents for things like lead qualification, invoice processing, customer triage, and automated reporting. The result: faster cycle times, fewer manual handoffs, and clearer,

Short summary AI “agents” — autonomous, workflow-focused versions of large language models — have shifted from experiments to real business tools. Over the last year, vendors and open-source projects delivered easier ways to connect agents to CRMs, calendars, document stores, and reporting systems. That means a single AI can now qualify leads, summarize calls, update

Summary AI agents—software that can plan, act, and learn across apps—are moving from experiments into real business use. Over the last year we’ve seen more off-the-shelf agent frameworks (think LangChain-style agents and vendor “copilots”), plus better connectors to CRMs, ERPs, and BI tools. That combination makes it possible for an AI to take on multi-step

Hook: If your team still spends hours on lead research, manual CRM updates, and pulling monthly reports, AI agents offer a fast, practical way to change that. The story in plain terms – Over the past year, major AI platforms (OpenAI, Microsoft, Google and others) released tools that make it far easier to build “AI

Quick summary AI “agents” — autonomous software that can act across apps, pull data, and complete multi-step tasks — are no longer just experiments. Companies are rolling them into sales, finance, and operations to automate workflows, generate executive reports, and even run customer triage. These agents combine large language models with connectors, rules, and low-code

Summary AI “agents” — software that can act on your behalf (draft emails, update CRMs, pull reports, even trigger workflows) — are maturing fast. Vendors and in-house teams are combining large language models with automation platforms and business data, so these agents can do end-to-end tasks instead of just suggesting text. That means faster reporting,