Ron Mitchell

Summary AI agents — virtual helpers that can read, act, and follow up across apps — are moving from labs into real business use. Teams are using them to draft outreach, summarize meetings, update CRMs, run routine analyses, and trigger workflows without human handoffs. That shift is making daily work faster and reducing repetitive errors. […]

The story (short) Autonomous AI agents — software that uses large language models and connectors to carry out multi-step tasks on their own — have moved from experiments to real business pilots. Advances in models, agent frameworks, and low‑code automation tools mean companies can now build agents that research leads, update CRMs, generate reports, and

Short summary There’s been a clear surge in practical AI agents — software that can act autonomously to complete tasks like lead qualification, customer follow-up, data pulls, and even multi-step problem solving across apps. These aren’t sci‑fi bots; they’re workflows made intelligent: read a document, pull data, draft an email, update the CRM, and escalate

Quick summary AI agents — autonomous, task-focused assistants powered by large language and multimodal models — are moving out of labs into day-to-day business tools. Companies are using these agents to qualify leads, draft contracts, automate reporting, and run routine back‑office workflows. The result: faster decisions, fewer manual handoffs, and measurable time savings across sales

Quick summary – Generative AI is no longer just for prototypes. Business intelligence tools and low‑code agent builders are being combined so software can both analyze data in natural language and act on those insights. That means dashboards that write their own summaries, agents that run routine reports, and automated workflows that update systems or

Story pick — the trend AI agents — autonomous, conversational systems that can act on your behalf across apps and data — are moving from experiments into real business use. Over the last year we’ve seen an acceleration: vendors and cloud platforms now make it easier to build domain-specific agents, companies are connecting those agents

Summary AI agents — autonomous software assistants that can read, act, and learn across apps — moved from demos to real business pilots in 2023–24. Companies are using them to qualify leads, update CRMs, draft proposals, and generate routine reports. Instead of asking a human to copy data between systems or write the same follow-up

Quick summary AI platforms from leading vendors now make it simple for non‑developers to build “AI agents” — preconfigured, task-oriented assistants that can read documents, talk to your CRM, draft emails, summarize meetings and trigger workflows. Think of them as customizable, always-on teammates that automate repetitive work and surface insights in plain language. Why this

Short summary AI “agents” — small, task-focused AI programs that can read, act, and communicate — are moving out of labs and into real business workflows. Using techniques like retrieval-augmented generation (RAG) and simple orchestration tools, companies are building agents to handle things like lead qualification, customer Q&A, routine reporting, and task automation. These agents

Short summary AI agents — software that can plan, act, and complete multi-step tasks on its own — are now being embedded directly into CRMs, collaboration tools, and reporting stacks. Major vendors (and a wave of startups) are shipping agent-capable copilots that can qualify leads, draft and send outreach, update records, and produce near-real-time BI