Ron Mitchell

Quick update: Autonomous AI agents — software that plans, runs tasks, and calls other apps with minimal human help — are moving from demos into real business pilots. Over the last year, more companies have started using agents to automate repetitive work like customer follow-ups, invoice reconciliation, data entry, and scheduling. Vendors are embedding agent […]

Big picture: Autonomous AI agents — software that can plan, act, and complete multi-step tasks without constant human direction — are moving from research demos into real business use. Over the past year we’ve seen major platform moves (custom GPTs, Copilot expansions, and agent frameworks) and a wave of pilots that tie agents to CRMs,

Recent trend snapshot AI agents—software that combines large language models (LLMs), retrieval systems, and automation tools to perform multi-step tasks—are moving from labs into real business use. Platforms like LangChain, Microsoft Copilot, and newer agent orchestrators let AI act on documents, databases, and apps to complete workflows: summarize contracts, generate customer responses, build reports, and

AI agents — autonomous, multi-step AI assistants that can plan, act, and integrate with systems — are moving from labs into real business use. Over the past year we’ve seen a sharp rise in agent frameworks (like LangChain-style agents and commercial “copilot” services) and tool integrations that let companies automate tasks such as lead qualification,

Short summary (for LinkedIn / business audiences) AI “agents” — autonomous, goal-driven AI tools that can read data, call apps, and take multi-step actions — are moving from labs into real business use. Over the past year, major vendors and startups have wrapped agent frameworks into CRM, helpdesk, and analytics tools so these systems can

Quick summary Autonomous AI agents—software that plans, executes, and iterates on tasks with little human direction—have moved from research demos into real enterprise pilots. Major cloud providers and open-source toolkits have lowered the barrier to building agents that can handle customer triage, data synthesis, IT ops automation, and sales enablement. For business leaders, the promise

AI agents — autonomous workflows built from large language models, tool connectors, and real-time orchestration — are moving from labs into the core of business operations. Across sales, customer service, finance, and supply chain, organizations are using agents to qualify leads, triage tickets, auto-generate reports, and handle routine exceptions. The result: faster decision cycles, fewer

AI agents are the next big step in business automation. Instead of one-off apps or simple chatbots, companies are building “agents” that combine large language models, retrieval-augmented generation (RAG), and workflow tools to perform end-to-end tasks—like summarizing contracts, routing customer issues, or preparing sales outreach—automatically and at scale. Why this matters for business leaders –

Short summary (for SEO/meta): Autonomous AI agents are moving from experiments to real business tools. Learn what they do, why leaders should care, and how to safely pilot and scale them across sales, operations, and customer support. Quick LinkedIn-friendly post: AI agents — small, goal-driven programs powered by large language models — are no longer

Quick summary Autonomous AI agents—software that uses large language models (LLMs), tool access, and retrieval-augmented generation (RAG) to act on your behalf—are moving from experiments to production across industries. These agents can draft emails, coordinate data between systems, run reconciliations, triage support tickets, and execute routine decisions 24/7. Major vendors and startups are embedding agent