Ron Mitchell

Quick summary In the past year we’ve seen a clear shift: major AI platforms introduced agent frameworks that can act autonomously across apps — scheduling meetings, qualifying leads, pulling data, and even drafting proposals. Businesses are moving from “AI as a tool” (helpful prompts) to “AI as a worker” (agents that execute end-to-end tasks). That […]

Quick story AI agents — software that can plan, act, and complete multi‑step tasks with little human direction — have moved from lab demos into real business work. Companies are now using agents to run sales outreach, auto-generate weekly performance reports, triage customer requests, and automate routine procurement and invoice processing. The result: faster workflows,

Quick summary Autonomous AI agents—models that can act across apps, pull in your data, and complete multi-step tasks—are no longer just lab experiments. Over the past 18–24 months major vendors and startups have productized agent-like features (think custom GPTs, integrated copilots, and agent orchestration platforms). Businesses are starting to use these agents to draft personalized

Short summary: Autonomous AI agents — software that can plan, act, and follow up with little human hand-holding — have moved from research demos to real-world pilots. From marketing assistants that run outreach sequences to operations bots that monitor systems and create reports, companies are starting to deploy agents to handle repeatable, decision-driven work. Why

Short summary AI agents — software that can act autonomously to complete tasks, coordinate across apps, and generate reports — are moving from labs into real business workflows. What started as developer experiments (Auto-GPTs, orchestration libraries) is now showing up in sales ops, customer service, and finance teams as tools that automate repetitive decisions and

Short summary AI “agents” — autonomous, multi‑step programs that can research, act, and communicate on your behalf — have moved from experiments into everyday business use. Instead of one-off prompts, agents combine data access, rules, and generative models to do complete tasks: qualify leads, triage support tickets, assemble monthly reports, or automate invoice processing. Vendors

Short summary AI “agents” — autonomous workflows that combine language models with tools, APIs, and data — have moved quickly from research demos into practical business use. Where early projects were one-off experiments, vendors and open-source platforms now offer enterprise-ready agent frameworks with better security, audit logs, and connectors to CRMs, reporting tools, and databases.

Quick summary AI “agents” — autonomous, goal-driven software that can plan, act, and coordinate across systems — are no longer just lab demos. Over the last year, major cloud and software vendors rolled out agent frameworks and low-code builders that let teams create task-specific agents for things like lead qualification, contract review, and automated reporting.

Quick story AI “agents” — small, goal-directed AI programs that can run tasks, call tools, and follow workflows — have moved out of labs and into everyday business pilots. New developer tools and orchestration platforms have made it faster and cheaper to connect agents to CRMs, databases, calendars, and reporting tools. That means teams can

Summary Companies are moving from playing with chatbots to running private, enterprise-tuned language models (LLMs) and AI agents that act on internal data. Instead of sending sensitive documents to public APIs, firms are using RAG (retrieval-augmented generation) and hosted models to let AI read their CRM, ERP, and reports — then create concise monthly dashboards,