Ron Mitchell

Short summary A new wave of AI agents — software that plans, acts, and connects to your systems — is moving from tech demos into real business use. These agents can autonomously do tasks like outreach, meeting scheduling, CRM updates, routine analysis, and generating reports. Companies are deploying them as “digital teammates” that reduce repetitive […]

Quick summary AI agents — autonomous, multi-step AI tools that can run tasks, follow up, and connect to software — have moved beyond experiments. Vendors and in-house teams are now shipping agents with ready-made connectors (CRMs, calendars, email), better guardrails, and analytics that make them practical for day-to-day business work. Why this matters for businesses

Quick summary A new wave of “agentic” AI is moving from research demos into real business systems. Today’s AI agents can securely connect to CRMs, calendars, email, and reporting tools to execute multi-step tasks — like triaging leads, scheduling meetings, populating opportunity records, and generating weekly performance reports — without constant human prompting. Why this

Hook: AI agents — autonomous, task-oriented AI that can read, act, and report — are no longer experimental. Companies are putting them into everyday workflows to automate routine work, generate regular reports, and surface sales and operations insights faster. What’s happening (short summary) – New low-code agent builders, private LLM deployments, and plug‑and‑play connectors are

The story (short) Over the past year, AI agents — autonomous, multi-step AI workflows that can read data, take actions, and talk to other apps — have moved from demos and hobby projects to real business pilots. Major cloud platforms and vendors have built agent frameworks, and more enterprises are testing agents for tasks like

What’s happening – Autonomous AI agents — software that can take multi-step actions (research, draft messages, update systems, schedule meetings) — have moved from demos into real business use. – Vendors and open-source projects have made it easier to connect agents to CRMs, knowledge bases, ticketing systems, and reporting tools so they can act on

Quick summary AI “agents” — LLM-powered systems that can act (write emails, update CRMs, run reports, book meetings) — have moved from experiments to real business use. Improvements in reliability, tool integration, and low-cost deployment mean teams can now offload routine, repeatable tasks to agents without rewriting core systems. Why this matters for your business

Summary AI “agents” — autonomous software that can read data, act across apps, and carry out multi-step tasks — moved from hype to real use in 2025. Companies are now using agents to draft sales outreach, update CRMs, generate and distribute reports, and even coordinate cross-team approvals. These systems combine large models, retrieval-from-your-data (RAG), connectors

Quick summary AI “agents” — models that can fetch data, call tools, and complete tasks without step-by-step human prompting — have moved from demos into practical deployments. Businesses are pairing these agents with secure data retrieval (RAG), CRM and BI systems, and workflow tools so agents can draft proposals, qualify leads, update records, and generate

Quick summary – Autonomous AI agents — small, goal-driven AI programs that can read, act, and coordinate across apps — have moved beyond demos. Businesses are now using them to qualify leads, run outreach sequences, consolidate data, and generate regular reports without handoffs between teams. – Why it matters: AI agents cut repetitive work, speed