Ron Mitchell

Quick summary Autonomous AI agents — software that can take actions across apps, fetch data, and complete tasks with little human prompting — have moved beyond lab demos into real business use. Companies are using agents to automate routine sales work (prospecting, follow-ups), generate and distribute regular reports, and triage customer requests. The result: faster […]

Story summary AI “agents” — autonomous or semi-autonomous software that can read, decide, act, and integrate with business systems — are moving from labs into everyday business use. Over the past year we’ve seen no-code agent builders, better retrieval‑augmented generation (RAG) for accurate context, and deeper CRM and calendar integrations. That makes agents practical for

Quick summary AI agents — autonomous, task-focused AI that can research, write, and take actions inside apps — are no longer just demos. Over the past 12–18 months more sales and operations teams have moved from pilots to production: agents research prospects, draft and personalize outreach, log activity to CRMs, and auto-generate pipeline and performance

Story Over the last year the AI-agent market moved from demos into real business tools. Low-code agent builders, plug‑and‑play connectors, and better data integrations mean companies can now deploy AI agents to handle sales tasks, customer outreach, reporting, and routine operations — with less custom engineering than before. Summary — why this matters for business

Quick summary Over the past year many companies have moved from experimenting with chatbots to deploying AI agents — autonomous or semi-autonomous systems that can access your data, take actions across apps, and carry out multi-step tasks (e.g., qualify leads, build reports, or update your CRM). These agents combine large language models, retrieval-augmented generation (RAG),

Summary AI agents — autonomous, multi-step software that can research, draft messages, take actions in apps, and learn from results — are no longer just demos. Companies are embedding these agents into sales and operations workflows to do prospecting, qualify leads, schedule meetings, update CRMs, and trigger follow-up sequences. The result: faster pipeline creation, more

Quick summary AI “agents” — task-focused systems that combine large language models, retrieval (RAG), and workflow automation — moved from experiments into real business use during 2023–24. Major vendors rolled out assistant/copilot features and startups built agent frameworks that can read your CRM, draft emails, run follow-ups, and produce consolidated reports without manual copy‑paste. Why

The story in short In the past year, AI agents — software that combines large language models with automation and connectors to systems (CRMs, calendars, data warehouses) — have moved from demos into real business pilots. Major vendors and startups now offer agent-building tools that let teams create task-specific assistants: lead qualifiers, meeting summarizers, order

Quick summary AI “agents” — autonomous, task-focused AI that can act across apps and workflows — have moved from demos into everyday business tools. Instead of only suggesting text or insight, modern agents can gather data, fill CRM fields, generate and deliver reports, run follow-ups, and trigger downstream processes. That means less manual work, faster

The story in a sentence AI “agents” — autonomous software that reads, decides, and acts across apps — have moved from demos into real business use. More companies are using agents to run outreach, update CRMs, generate reports, and automate repetitive decisions, turning piloted experiments into measurable cost savings and faster sales cycles. Why this