Ron Mitchell

Quick summary AI “agents” — autonomous, multi-step AI workflows that can take actions across apps and data — have stopped being just an R&D toy. Over the last 18–24 months we’ve seen vendors and platforms add agent orchestration, built-in connectors, better safety controls, and tools for domain-specific knowledge (so agents stop hallucinating). That shift is […]

Quick summary Over the last year major vendors (OpenAI, Microsoft, Google and others) rolled out easy ways to build “AI agents” — configurable, goal-driven assistants that can read your data, call APIs, and take multi-step actions. These tools make it much faster and cheaper to automate tasks that used to need custom software: things like

Summary — the story in plain language In the past year we’ve shifted from talking about chatbots to seeing autonomous AI agents actually run parts of business workflows. These agents combine large-language models, connectors (to CRMs, ERPs, analytics), and automation tools to do things like draft personalized sales outreach, pull and summarize multi-system reports, triage

Quick summary AI agents — autonomous, LLM-driven helpers that use tools, APIs, and company data to complete tasks — have moved beyond experimentation and into real business use. Companies are deploying agents to qualify leads, automate routine workflows, generate narrative reports from data, and handle 24/7 customer touchpoints. The result: faster execution, lower operational cost,

Quick story Over the last year we’ve seen AI “agents” — language models that can act autonomously across apps, fetch data, and complete multi-step tasks — move out of labs and into real business workflows. Tool-enabled models (think agent frameworks, RAG + vector search, and the new crop of copilot-style products) are being used to

Quick summary AI agents — autonomous, task-focused AI that can act across apps and systems — moved from experiments to real business pilots in 2023–2024. Vendors released agent frameworks and orchestration tools, and companies are now testing agents to handle scheduling, lead qualification, multi-step follow-ups, and automated reporting. That shift matters because agents can work

Summary AI agents — small, semi‑autonomous software assistants that can read, act, and chain tasks — are moving from experiments into real business use. Instead of just answering questions, these agents can pull customer data, update CRMs, triage leads, generate outreach, and feed results into dashboards. The result: repeated tasks get faster, human work shifts

Summary AI “agents” — autonomous software that can perform tasks end-to-end (draft emails, update CRMs, generate reports, schedule follow-ups) — are no longer just R&D experiments. Over the last 18–24 months, we’ve seen more teams move agent pilots into production to automate routine sales tasks, speed reporting, and reduce administrative overhead. That shift is making

Quick summary AI “agents” — autonomous AI assistants that can read your apps, run processes, and act on your behalf — are no longer just a demo trick. Over the past year major platforms and enterprise tool vendors have shipped agent frameworks and connectors that make it feasible to run agents against CRMs, ERPs, calendars,

Quick summary There’s a clear shift happening: AI agents — autonomous, task-focused AI that can read your data, take actions, and follow up — are no longer just research demos. In the last year major vendors and cloud platforms have pushed agent frameworks and marketplaces that make it easier to build domain-specific agents for things