Ron Mitchell

Quick summary AI “agents” — small, goal-driven AI programs that act across apps and data — are moving from experiments into real business use. Unlike single-response chatbots, agents can run multi-step processes: pull data from your CRM, run analysis, update dashboards, send follow-ups, and flag exceptions — all without a human clicking every step. Why […]

Quick summary Over 2024 we saw a surge in interest around autonomous AI agents — tools that can act on your behalf by reading documents, pulling data from systems, sending messages, and completing multi-step tasks. Frameworks like Auto-GPT, LangChain, and retrieval-augmented workflows plus vector databases made it practical for teams to build agents that actually

Quick summary – What happened: Since early 2024, tools like OpenAI’s Custom GPTs and a wave of vendor platforms have made it easy for non‑developers to build purpose‑built AI agents. These agents can read your documents, connect to CRMs and reporting tools, and run simple workflows automatically. – Why it matters: That shift turns AI

Quick summary AI agents — autonomous software that can read your data, take actions, and follow multi-step workflows — have moved from lab demos to real business pilots. Companies are using agents to qualify leads, automate order processing, generate recurring reports, and run outreach sequences that adapt in real time. The result: faster decisions, fewer

Story summary AI agents — software that can act autonomously across apps, find information, and complete tasks — have moved from demos to real business pilots. Large platform vendors and a wave of startups have made it easier to build agents that connect to CRMs, ERPs, calendars, email, and reporting systems. Companies are using them

AI agents — not just chatbots, but autonomous helpers that can research, take actions, and update your systems — are moving from lab demos into real business use. Over the last year we’ve seen toolkits, model improvements, and integrations that let companies build agents that qualify leads, generate outreach, update CRMs, and run routine reporting

Quick summary AI “agents” — models that act autonomously, call tools, and work across systems — moved fast in 2024–25 from research demos into real business pilots. Teams now use agents to qualify leads, draft outreach, update CRMs, triage support tickets, and generate on‑demand reports that pull from live data sources. Tooling (LangChain-style frameworks, vendor

Quick summary AI agents — autonomous software that can research, prioritize, draft messages, run reports, and trigger actions — have moved from demos into real business use. Improvements in retrieval-augmented generation (RAG), better connectors to CRMs and calendars, and low-code agent builders mean companies can safely automate multi-step tasks end to end. Why this matters

Quick summary AI “agents” — software that can act autonomously across apps, fetch data, and complete multi-step tasks — moved from demo to real deployments in 2024–25. Major platforms and developer frameworks lowered the barrier: agents can now connect to CRMs, calendars, email, and BI tools and run repeatable workflows without constant human prompting. Why

Quick summary – What’s happening: Autonomous “AI agents” — software that can run tasks end-to-end with little human intervention — are becoming practical for real business work. They can draft outreach, triage leads, pull and summarize data, and even trigger follow-up actions in your systems. – Why it matters for business: These agents cut repetitive