Ron Mitchell

Quick take: AI agents — autonomous systems that can plan, act, and complete multi-step tasks — are moving fast from labs into real business use. From scheduling and customer triage to automated reporting and order reconciliation, smart agents are already reducing repetitive work and speeding decisions. For leaders, the opportunity is simple: free people to […]

Big picture: This year major cloud and software vendors have pushed AI copilots and retrieval‑augmented generation (RAG) into everyday business apps. That means AI can now pull exact answers from your internal docs, draft emails, summarize meetings, and automate routine tasks—right inside the tools your teams already use. The result: faster decisions and big productivity

Keywords: AI agents, enterprise automation, RAG, LLMOps, AI adoption, sales automation Quick summary AI “agents” — autonomous, multi-step AI assistants that can call APIs, access documents, and act on behalf of users — are moving from labs into real business use. Major cloud and software vendors and startups have released agent-builder tools and integrations that

A new wave of autonomous AI agents is changing how companies automate work. These agents combine large language models, tool use (calendars, CRMs, databases), and rules to carry out multi-step tasks with minimal human direction. Businesses are already using them to draft emails, qualify leads, manage scheduling, triage support tickets, and automate parts of procurement

AI update: Autonomous AI agents — systems that combine large language models with tool access (APIs, browsers, databases, and SaaS connectors) to perform multi‑step tasks on their own — are moving from experiments into real business use. Companies are using these agents to draft proposals, run sales follow-ups, build reports, update CRMs, and automate routine

Quick summary AI is moving from research demos to real business systems because of RAG — retrieval-augmented generation — paired with vector databases. Instead of asking a large language model (LLM) to “remember” everything, RAG lets the model pull exact, relevant facts from your company data (documents, CRM notes, SOPs, product specs) at query time.

Quick summary AI agents — small, goal-driven systems built on large language models (LLMs) that can take multi-step actions, use external tools, and learn from context — are moving from demos into real business use. Companies are already testing agents to draft proposals, run customer follow-ups, monitor systems, and automate repetitive decisions. Improvements in tool

Quick summary AI “agents” — autonomous systems that can read, plan, act, and learn on behalf of users — are moving from lab demos into real business use. Paired with retrieval-augmented generation (RAG) and vector databases, these agents can securely access company knowledge (documents, CRM records, SOPs), make decisions, and complete tasks like drafting emails,

AI trend summary Autonomous AI agents — software programs that use large language models (LLMs) to plan, act, and complete tasks with minimal human input — are moving quickly from lab demos into real business use. Companies are pairing these agents with internal data, APIs, and RPA (robotic process automation) systems to automate end-to-end workflows

Quick summary Autonomous AI agents—small, purpose-built systems that can read your data, take actions in apps, and complete multi-step tasks—are moving from proofs of concept to real business use. Companies are using these agents for things like invoice processing, customer follow-ups, sales research, and scheduling. They can run 24/7, reduce manual steps, and free staff