Ron Mitchell

Short summary AI agents — autonomous, goal-driven bots that can chain tasks, interact with systems, and learn from feedback — are no longer just a developer curiosity. Over the last year we’ve seen a wave of pilots and early production deployments where agents automate multi-step business processes: composing outreach sequences, updating CRMs, generating weekly sales […]

Short summary AI agents — task-focused assistants built on large language models — are moving from experiments into real business workflows. Instead of one-off chatbots, companies are stitching agents into CRMs, analytics tools, calendars and ticketing systems so the agent can act (not just answer). That means faster reporting, automated outreach, and repeatable processes that

Summary AI “agents” — autonomous, task-focused AI assistants built from models like GPT — are moving from labs into real business workflows. Major platforms (custom GPTs, Copilot-style integrations, and CRM-driven AI features) have made it easier for teams to deploy agents that qualify leads, generate reports, triage support tickets, and automate repetitive processes. Why this

Quick summary Autonomous AI agents — software that can plan, act on data, use tools, and follow up without constant human prompts — are moving from experiments into real business use. Tools that combine language models, retrieval (RAG), APIs, and low-code integrations now let companies automate lead qualification, create real-time reports, and handle routine customer

Short summary AI “agents” — autonomous software that can read your systems, take actions, and generate results — are finally practical for business teams. Instead of one-off chat replies, modern agents can run workflows across your CRM, email, calendar, and reporting systems to complete tasks like lead outreach, invoice triage, or weekly performance reports. Why

Summary Autonomous AI agents — software that can act on behalf of people, chain tasks, and talk to your systems — moved from research demos into real business use in 2024. Frameworks like LangChain and enterprise copilots from major vendors made it easier to build agents that can search documents, schedule tasks, create reports, and

Summary AI agents — autonomous, multi-step software that can research, decide, and act — have moved from labs into real business use. Modern agents combine large language models with retrieval (RAG), connectors to CRMs and ERPs, and task orchestration. That means they can qualify leads, assemble weekly sales reports, triage customer requests, and run routine

Quick summary AI models and agent frameworks have reached a tipping point: faster, cheaper, and more capable large models plus mature toolkits (think agent orchestration, retrieval-augmented generation, and low-latency APIs) mean businesses can safely automate complex, multi-step work. That includes things like sales outreach, proposal generation, customer triage, and automated reporting — not just one-off

Quick summary AI agents — systems that combine a large language model with tools, APIs, and business data to do work end-to-end — have moved fast from research demos to real-world deployments. Instead of only generating text, modern agents can pull CRM records, run queries against your data, create and send outreach, schedule meetings, and

Recent story (short summary) AI “agents” — autonomous software that can carry out multi-step tasks, learn from feedback, and connect to business systems — are moving from labs into real-world use. Organizations are now combining agents with existing tools (CRMs, dashboards, RPA, and data warehouses) so the agent can pull context, take action, and produce