Ron Mitchell

Summary AI “agents” — configurable, multi-step AI assistants that can act across apps and systems — are no longer experimental. Major platforms now let businesses build agents that do things like qualify leads, book demos, generate tailored proposals, and auto-update CRMs. They can also pull data from internal sources to produce regular reports and flag […]

Short summary AI agents — small, task-focused AI assistants that can act across apps and data — went from experimental labs to enterprise-ready in 2024–25. Major platforms (OpenAI, Microsoft, Google) now make it easy to build custom agents that plug into CRMs, reporting tools, ticketing systems, and email. That means businesses can automate multi-step workflows:

Summary Autonomous AI agents — software that can plan, act across apps, and complete multi-step tasks without constant human prompts — have moved from labs into real business tools. Over the last year many vendors packaged agent capabilities into SaaS products for sales, customer service, finance, and operations. That means companies can now automate whole

Quick summary Autonomous AI agents — systems that can take multi-step actions (research, write, call APIs, update systems) without constant human prompts — have moved from experiments to real business pilots. Companies are using agents for things like lead qualification, automated follow-ups, routine reporting, and triaging customer requests. The result: faster workflows, more consistent outputs,

Quick summary AI agents — small, goal-oriented AI programs that can run multi-step tasks — are moving from experiments into real business use. Teams are using agents to qualify leads, update CRMs, generate recurring sales reports, and automate repetitive approvals. The result: faster decisions, fewer manual errors, and more time for high-value work. Why this

Short summary Autonomous AI agents — AI programs that can act, decide, and complete tasks without constant human prompts — are no longer just experiments. More companies are using them for lead qualification, follow-up emails, meeting scheduling, and automated reporting. These agents connect to CRMs, calendars, and analytics tools to run routine workflows faster and

Quick summary AI agents — autonomous, goal-oriented AI that can access your CRM, calendar, files, and reporting systems — are moving from experiments into real business use. Vendors and platforms now offer secure connectors, retrieval-augmented workflows, and low-code tools that let these agents perform tasks like lead qualification, meeting preparation, follow-up, and automated sales reporting.

The story (short) AI “agents” — autonomous AI that can take multi-step actions across apps (think: read CRM records, draft outreach, schedule meetings, and update reports) — are no longer just demos. In the last year we’ve seen low‑code agent builders, off‑the‑shelf connectors to common business systems, and better methods (like retrieval-augmented generation) to keep

Summary AI “agents” — software that can carry out multi-step tasks on its own (think: research a lead, draft outreach, update your CRM) — are no longer just demos. Over the past year vendors and platforms have made agents easier to deploy, cheaper to run, and safer to connect to internal data. That means businesses

Short summary AI agents — the task-focused “bots” that can read your CRM, draft outreach, update invoices, and generate reports — are no longer just lab experiments. Over the past year we’ve seen more companies put these agents into production: connecting them to sales tools, automating routine workflows, and using retrieval-augmented generation (RAG) to create