Ron Mitchell

What’s happening AI agents — software that can take multi-step actions across apps and data sources with little human direction — have moved from research demos into real business pilots. Organizations are using agents for things like: – Qualifying leads in CRM and routing hot prospects to reps – Automating recurring reporting by pulling from […]

Brief summary Autonomous AI agents have moved from demos to real business use. Over the last 18 months major vendors and open-source frameworks released agent-style tools that can read your data, take multi-step actions across apps, and learn from outcomes. That means — for the first time — AI can do end-to-end tasks like qualify

Quick story Major AI vendors and startups have moved AI agents from demos into practical tools you can deploy today. No-code and low-code “agent builders” (think custom GPTs, Copilot-style studios, and agent frameworks) make it easy to create task-specific assistants that read your systems, take actions, and automate routine work. Early adopters are using them

Short take: Autonomous AI agents — tools that can plan, act across apps, and complete multi-step work — are no longer just lab experiments. Over the last 12–18 months the tech stack (agents + connectors + enterprise controls) matured enough for real business use: sales cadence automation, automated reporting, customer follow-ups, and routine finance tasks.

What’s happening – AI agents — autonomous, task-focused AI programs that can read, act, and report across apps — went from experimental to enterprise-ready in 2025. – Why now: better instruction-following models, pre-built connectors to CRMs, ERPs and BI tools, and low-code agent platforms make deployment faster and safer. – These agents can do things

Quick summary AI “agents” — autonomous, task-focused bots that can read data, talk to apps, and take actions — are moving from labs into real business use. Modern toolkits (agent frameworks, API integrations and no-code connectors) let companies automate workflows that used to need human attention: generating weekly sales reports, enriching leads in your CRM,

Story summary In the past year businesses have moved beyond experimenting with chatbots and pilots to running real-world AI agents that take actions: qualify leads, update CRMs, generate weekly reports, and route customer issues. Toolchains — vector databases, retrieval-augmented generation (RAG), and agent frameworks — make these agents more capable. At the same time, regulators

Quick summary Autonomous AI agents — software that can take multi-step actions, talk to APIs, and learn from data — have moved fast from hobby projects into real business tools. Companies are using agents today to qualify leads, run daily sales reports, schedule follow-ups, and automate repetitive back-office work. The result: faster decisions, fewer manual

Quick summary AI “agents” — autonomous, goal-oriented AI workflows that can read, act, and iterate across tools — have moved well past research demos. Over the last 12–18 months we’ve seen vendor toolkits, open-source frameworks, and practical pilots that connect agents to CRMs, ticketing systems, BI tools and RPA platforms. That means businesses can now

Quick summary AI “agents” — autonomous or semi-autonomous assistants that complete multi-step tasks across apps — went from research curiosity to practical business tool. Major vendors and startups have released agent frameworks, and companies are using them for things like lead qualification, automated reporting, meeting summaries, and process orchestration. Why this matters to business leaders