Ron Mitchell

Quick summary Autonomous AI agents — software that plans and executes multi-step tasks using large language models and connected tools — went from research demos to real enterprise pilots in 2024. Vendors and startups released agent platforms and integrations that link LLMs to CRMs, workflow systems, and data stores. That means AI can now do […]

What’s trending AI agents — autonomous systems built from large language models (LLMs) that call tools, query company data, and take multi-step actions — are moving from experiments into real business use. Platforms and frameworks (from commercial vendors and open-source projects) now let companies build agents that can draft reports, triage support tickets, run sales

The trend AI “agents” — autonomous, workflow-driven AI tools that can act on behalf of users — are moving from experiments into everyday business use. Companies are wiring these agents into CRM, helpdesk, ERP, and BI systems so AI can draft replies, route tickets, pull financial reconciliations, and generate reports without a human in every

Quick take: AI agents—software that can act on your behalf across apps, pull data, and complete tasks—are moving from experiments to real business use. Recent advances in multimodal models, retrieval-augmented generation (RAG), and secure private LLM deployments are making these agents faster, cheaper, and safer for enterprise use. Companies are already using agents to automate

A growing wave of autonomous AI agents — think tool-chaining bots built on LLMs (LangChain, agent frameworks, Copilot-style agents) — is moving from research demos into real business pilots. Companies are using these agents to automate multi-step tasks like sales outreach, contract review, lead qualification, order processing, and first-line customer support. Rather than just answering

Big picture: Companies are increasingly building private AI copilots that search and summarize internal documents, CRM data, and SOPs. These copilots use a technique called retrieval-augmented generation (RAG) plus vector databases (e.g., Pinecone, Qdrant, Weaviate) to find relevant facts before the model answers. The result: faster employee onboarding, smarter sales conversations, and automated reporting —

Short summary: AI “agents” — autonomous, multi-step AI assistants that can read, act, fetch data, and talk to other systems — are moving from demos into real business use. Teams now use agents to automate procurement approvals, generate monthly reports, triage IT tickets, and run customer onboarding flows. The result: faster decisions, fewer manual steps,

A growing wave of autonomous AI agents — systems that can plan, act, and complete multi-step business tasks with minimal human supervision — is moving from labs into the enterprise. Companies are already using agent-based tools to auto-generate sales outreach, triage customer issues, streamline reporting, and orchestrate cross-system workflows. For business leaders, this is less

Quick summary Autonomous AI agents — software that can plan, act, and complete multi-step tasks across apps with minimal human oversight — are moving from demos into real business use. Companies are using agents to handle end-to-end tasks like processing invoices, qualifying leads, creating first-draft reports, and coordinating customer support handoffs. The upside is faster

Big trend in plain language There’s a fast-growing shift in how businesses use AI: combining AI agents (custom bots) with Retrieval-Augmented Generation (RAG) and vector databases so AIs can act on accurate, company-specific knowledge — not just guess from general training data. Instead of static chatbots or one-off reports, companies are building AI-driven workflows that