Ron Mitchell

Short summary Autonomous AI agents — large-language-model assistants that can access your CRM, calendar, documents, and trigger workflows — are no longer just a novelty. Companies are starting to deploy them to do real tasks: triage leads, draft personalized outreach, update records, generate weekly sales reports, and even schedule follow-ups. Those agents combine connectors (to […]

Summary AI agents — autonomous LLM-driven assistants that can read, act, and connect to apps — have moved from demos into real enterprise workflows. Advances like secure connectors, retrieval-augmented generation (RAG) for reliable facts, and orchestration tools mean agents can now pull CRM data, draft emails, update records, and produce automated reports with much less

Summary AI agents — autonomous AI assistants that can run multi-step tasks — are moving from labs into real sales ops. Thanks to better language models and retrieval-augmented workflows, these agents can qualify leads, draft personalized outreach, update CRMs, and generate weekly sales reports with minimal human handoffs. Why this matters for business – Faster

Summary Autonomous AI agents — tools that can carry out multi-step tasks on their own (think: research leads, draft outreach, update CRMs, schedule meetings) — went from developer experiments to real business pilots over the last 18–24 months. Frameworks like LangChain and agent prototypes (Auto-GPT, BabyAGI) opened the door, and vendors have started productizing those

The story in brief AI “agents” — small, task-focused AI apps that can read your data, take actions across tools (email, CRM, calendars), and produce natural-language answers or reports — have moved from experimentation into real business use. Across industries, teams are using agents to qualify leads, automate outreach, summarize meetings, and generate reporting that

Quick summary AI “agents” — autonomous AI workflows that can search data, take actions, and interact with tools — have moved from research demos to real business tools. Over the last 18 months we’ve seen major vendors (Microsoft Copilot, Google’s agent features, enterprise LLM platforms and developer frameworks like LangChain) bundle action-capable models into everyday

Quick summary AI agents — systems that can plan, act, and follow up across apps — are no longer just research demos. Over the past 18–24 months businesses have moved beyond one-off chatbots to multi-step agents that can pull company data, update CRMs, compose personalized outreach, and generate reports automatically. Frameworks like LangChain and agent

There’s a clear shift happening: AI agents — tools that can act across apps, pull data, and execute tasks — are moving out of labs and into everyday business workflows. New connectors, better retrieval-augmented generation (RAG), and easier orchestration mean these agents can now handle real work: updating CRMs, drafting personalized outreach, generating reports, and

Summary AI agents — software that can plan, act, and complete multi-step tasks with little human direction — moved from research labs into real business use in 2023–2024. Tools like Auto-GPT and enterprise agent frameworks let companies automate end-to-end workflows: researching leads, drafting personalized outreach, updating CRMs, and generating management reports. The result is faster

Summary AI “agents” — autonomous assistants that can run tasks, fetch data, and take actions across apps — have moved quickly from demos into real business use. Over the last 18–24 months we’ve seen enterprise copilots, tool-enabled LLM agents, and workflow automation platforms that let AI do more than draft text: they schedule, update CRMs,