Ron Mitchell

Short summary AI “agents” — task-focused systems built from large language models (often called custom GPTs) — are quickly moving from experiments to business tools. Companies can now build agents that read your documents, call your APIs, summarize meetings, draft emails, and even run parts of routine workflows without heavy engineering. This trend is lowering […]

Recent trend snapshot Many companies — from startups to major cloud vendors — are pushing “AI agents”: small, goal-driven systems built on large language models that can act across apps, pull data, and complete multi-step tasks without constant human prompting. These agents are being used today for things like intelligent sales outreach, automated contract review,

AI agents — small, goal-driven software that plan, act, and use tools on your behalf — are moving from experiments to real business value. Over the last year, companies have layered agents on top of large language models, RAG (retrieval-augmented generation), and API integrations so these systems can handle tasks end-to-end: from drafting customer replies

The trend AI “agents” — autonomous, goal-driven software that can read, write, schedule, and call APIs — are moving fast from lab demos into real business use. Over the last year we’ve seen more enterprise-grade agent platforms, tighter integrations with CRMs and ERPs, and better tools for giving agents safe access to company data (think

AI agents — small, goal-driven AI programs that can plan, act, and learn — are moving from labs into real business work. Over the past year we’ve seen more companies pilot agents that handle tasks like customer follow-ups, report generation, lead qualification, and cross-system automation. Big platform vendors and open-source frameworks are making it easier

Short update for business leaders: AI “copilots” are no longer an experiment. Major vendors are embedding generative AI into everyday apps—CRMs, analytics tools, service desks, and productivity suites—so employees get contextual, task-ready help inside the tools they already use. That means faster decision-making, fewer manual tasks, and more consistent outputs across teams. Why this matters

Quick summary AI agents — autonomous systems that combine large language models (LLMs) with tools — are moving from demos to real business use. When you connect agents to company data using retrieval-augmented generation (RAG) and vector databases, they can answer complex questions, generate reports, and run multi-step processes while staying grounded in your documents

Quick summary Autonomous AI agents — software that can plan, act, and complete multi-step tasks with little human input — moved from research demos into real business pilots in 2024. Leading cloud platforms and startups are shipping agent frameworks that let companies automate workflows like invoice processing, customer follow-ups, data reconciliation, and routine IT work.

AI teams are increasingly pairing large language models (LLMs) with retrieval-augmented generation (RAG) and vector databases to get accurate, up-to-date answers from company data. Instead of relying on the model’s memory alone, RAG pulls relevant documents, product specs, contracts, or support articles and feeds them to the LLM. That reduces “hallucinations,” improves compliance, and makes

Quick summary Retrieval-Augmented Generation (RAG) — pairing large language models (LLMs) with vector databases that store your company knowledge — is one of the clearest, fastest-growing AI trends for businesses. Instead of relying only on a model’s built-in knowledge (which can be out of date or inaccurate), RAG fetches relevant, company-specific documents at query time