Ron Mitchell

Quick summary AI agents — autonomous, workflow-focused AI that can read, act, and coordinate across apps — are no longer just lab experiments. More companies are using agents to draft sales outreach, triage customer requests, create recurring management reports, and automate multi-step processes that used to need human handoffs. Why this matters for business – […]

Quick summary Major software vendors and startups are embedding AI agents — autonomous assistants that can execute multi-step tasks, fetch data, and interact with apps — into business workflows. These agents can qualify leads, generate and distribute reports, draft personalized outreach, and automate routine approvals. The result: faster decisions, fewer manual steps, and scaled personalization

Quick summary – AI “agents” — autonomous software that combines language models, retrieval (RAG), and connectors to apps like CRMs — are no longer just demos. More companies are using them to run routine sales tasks, update records, and produce regular reports without manual handoffs. – These agent platforms are becoming easier to build thanks

Quick summary AI “agents” — autonomous workflows that act on your behalf (think: draft emails, schedule meetings, update CRMs, generate reports) — moved from experiments into practical business tools in 2024. Tooling got easier (low‑code builders and APIs), models became more reliable, and companies started using agents to automate repeatable customer- and operations-facing tasks. Why

Quick summary Large AI platforms and enterprises are moving from chatbots to autonomous “AI agents” that can run multi-step workflows: research leads, draft outreach, update CRMs, summarize meetings, and generate weekly reports. Instead of answering single questions, these agents chain tasks, call external systems, and hand back structured outputs you can act on. Early pilots

Short summary AI agents — autonomous, multi-step AI “helpers” — are moving fast from experiments into real business workflows. Advances in large language models, easy integrations (APIs, connectors), and low-code agent builders mean you can automate tasks that used to need a person: lead qualification, personalized outreach, scheduling, and consolidated reporting across systems. Why this

Quick summary AI “agents” — autonomous software that can take multi-step actions (research, message, update systems, create reports) — have moved from experiments to practical tools for businesses. Over the last year vendors and open-source frameworks made it much easier to build agents that connect to CRMs, calendars, and data warehouses while enforcing guardrails for

Summary AI agents — software that can act autonomously to complete tasks, coordinate systems, and carry out workflows — have crossed a key threshold. What used to be research demos and proofs-of-concept are now available as turn-key tools and low‑code builders from major vendors. Businesses can now assemble agents that qualify leads, generate personalized outreach,

Short summary Over the last year businesses have moved from experimenting with chatbots to deploying AI agents — autonomous or semi-autonomous systems that can run multi-step tasks, pull data from multiple systems, and trigger actions (for example: update a CRM, generate a sales report, or route leads). Low-code agent builders and integrations from cloud vendors

Quick summary This year saw a clear shift: AI agents — autonomous, task-focused AI assistants — moved from demos to real business use. Major providers released agent frameworks and companies launched industry-focused agent templates for sales, customer service, operations, and reporting. Instead of asking a chatbot a question, teams can now deploy agents that act