Ron Mitchell

AI trend summary AI agents — goal-directed, autonomous software that can read data, interact with systems, and complete tasks — moved from pilot projects into real business use in 2024. Leading tools and platforms (from big cloud vendors to open-source frameworks) now let companies build agents that join calendars, summarize customer threads, run queries across […]

Keywords: AI agents, autonomous agents, enterprise automation, RAG, vector databases, AI governance, process automation Short summary (for business leaders) Autonomous AI agents — systems that can plan, use tools, and execute multi-step tasks without constant human direction — are moving from research demos into real business use. Companies are piloting agents to handle things like

There’s been a clear surge in autonomous AI agents — software systems that can plan, act, and adapt across tools with little human supervision. Built on large language models plus task-specific connectors (CRMs, calendars, databases), these agents can draft emails, run research, update records, and trigger workflows automatically. Businesses are piloting them to speed repetitive

Quick summary Autonomous AI agents — small, goal-driven systems that combine large language models (LLMs) with tool access, data retrieval, and workflow logic — are moving from lab experiments to real business use. Paired with Retrieval-Augmented Generation (RAG) and vector databases, these agents can fetch private data, run analytics, file tickets, and generate reports automatically.

AI agents and workplace “copilots” are moving from hype to hard business impact. Over the past year the market has seen a surge in purpose-built AI agents — systems that can act autonomously across apps, fetch and verify data, draft and execute tasks, and hand off to humans when needed. Companies are piloting these agents

AI agents — autonomous, task-focused AI that can use tools, apps, and data on their own — are suddenly moving from tech demos into real business use. Over the last year we’ve seen major vendors and startups release more capable agent frameworks, plus more off-the-shelf connectors to CRMs, ERPs, and reporting tools. That combination is

What’s new Autonomous AI agents — systems that can plan, act, and coordinate tools to complete multi-step tasks — have moved from labs and demos into real business pilots. Toolkits like LangChain and agent frameworks, plus faster private models and vector databases, let teams build agents that can research prospects, draft multi-step reports, and run

Summary: The rise of AI agents — small, goal-driven AI programs that can read company data, call tools, and carry out multi-step tasks — is moving from labs into real business use. Over the past 12–18 months, major AI providers and open-source toolkits have added agent capabilities and better integrations with enterprise systems. At the

Trending AI update (short) Enterprises are increasingly pairing private large language models (LLMs) with retrieval-augmented generation (RAG) pipelines to improve accuracy, protect sensitive data, and unlock real business value. Instead of trusting a model’s free-form memory, RAG lets AI search a company’s verified documents, sales decks, knowledge bases, and databases, then generate answers grounded in

AI trend snapshot: Autonomous AI agents — think Auto-GPT-style systems and enterprise copilots — are moving from experiments into everyday business use. Companies are combining large language models with retrieval-augmented generation (RAG), vector databases, and simple orchestration to automate tasks like customer follow-ups, report synthesis, candidate screening, scheduling, and routine procurement. The result: faster turnaround,