Ron Mitchell

Big picture in one line: AI agents — autonomous workflows built on large language models — are moving out of pilots and into everyday business use for sales, automation, and reporting. What’s happening (short summary) Over the last 12–18 months the tech pieces needed for practical AI agents have come together: cheaper, more capable language […]

Quick summary AI “agents” — autonomous systems that can plan, act, and interact without constant human prompts — are moving from demos into real company workflows. Big platform vendors and startups are embedding agents into tools for sales, customer service, finance, and reporting. At the same time, companies are combining RPA, CRM and BI systems

Quick summary – Over the past 18 months, “AI agents” — autonomous or semi-autonomous AI that can complete end-to-end tasks (think: draft outreach, update your CRM, run a weekly sales report) — have moved from demos into real deployments across sales, finance, and operations. – These agents combine large language models, retrieval-augmented generation (RAG) for

Big picture summary AI is shifting from “smart assistant” to “active operator.” Over the last year major platforms and enterprise tools have released agent-style features that can take actions across apps — triaging email, updating CRM records, generating and sending proposals, and running recurring reports. That means AI is no longer only a helper for

Quick takeaway AI agents — autonomous, task-focused AI that can act on your systems and data — are moving from experiments into real business use. Companies are using them to automate repetitive sales work, generate timely reports, and triage customer issues. That shift matters because it saves time, reduces human error, and scales expertise without

Quick summary AI “agents” — autonomous, goal-driven AI programs that can pull data, take actions, and talk to systems — have moved from lab experiments into practical business use. Companies are now using agents to qualify leads, triage customer messages, assemble automated reports, and orchestrate multi-step workflows without handoffs at every step. Why this matters

Quick summary AI agents — autonomous, task-focused AI “helpers” that can read your systems, take actions, and coordinate with other tools — have moved from experiments into real business use. Vendors (think Copilots and domain-specific agents) and new orchestration platforms now make it practical to deploy agents that handle tasks like lead outreach, deal summarization,

Why autonomous AI agents are the next big win for business automation Summary — the story in plain terms Autonomous AI agents — systems that can take multi-step actions (search, summarize, update systems, trigger follow-ups) without constant human direction — have moved from labs into real business pilots. Tools and frameworks like LangChain, AutoGPT-style workflows,

Quick summary AI agents — autonomous, task-focused AI assistants that can read systems, take actions, and carry out multi-step workflows — are moving from labs into everyday business use. Recent vendor toolkits and low-code agent builders let teams connect CRM, ERP, chat, and reporting tools so agents can do things like qualify leads, generate weekly

Short summary Autonomous AI agents — systems that can plan, act, and follow up on tasks with little human prompting — moved from research demos into practical tools in 2023–2024. Frameworks like LangChain, low-code integrations, and vendor “copilot” offerings made it easier to connect agents to CRMs, data warehouses, and business apps. The result: businesses