Ron Mitchell

Quick summary AI agents — autonomous software that can take multi-step actions (think: research a lead, update your CRM, and draft an outreach email) — are moving from labs into real business workflows. Advances in large language models, better connectors to enterprise systems, and low-code agent frameworks from major vendors have made practical deployments possible […]

The story (short) Autonomous “AI agents” have moved fast from research demos into real business tools. Modern agents can chain multiple steps, call external systems (calendars, CRMs, databases), and generate structured outputs like reports or follow-up emails. Large vendors and startups are packaging these capabilities into agent platforms and plugins that make it easier to

The story (short) AI “agents” — software that can run multi-step tasks by itself (research, interact with apps, and produce reports) — are moving from labs into everyday business tools. Major platforms and startups are embedding agent capabilities into CRMs, analytics suites, and workflow apps so a single AI can qualify leads, draft personalized outreach,

Quick summary AI “agents” — autonomous, multi-step AI tools that can access apps, fetch data, and take actions — have moved from research demos into real enterprise use. Over the past 18 months we’ve seen major frameworks and commercial offerings that make it easier to connect agents to CRMs, data warehouses, calendars, and ticketing systems.

Recent story (summary) AI “agents” — autonomous or semi-autonomous software that can run tasks, make decisions, and interact with tools — have gone from developer experiments to real business features. Major platforms (think Copilot-style integrations and custom GPTs) now let organizations deploy agents that draft emails, pull and summarize reports, run follow-ups, and even trigger

Quick summary Autonomous AI agents — software that can take multi-step actions (qualify a lead, book a demo, update your CRM, generate a follow-up email) — have moved quickly from research demos into real company workflows. Over the last 12–18 months more teams have stopped treating agents as experiments and started embedding them into sales,

Quick summary AI “agents” — autonomous, task-focused AI that can act on your behalf (draft emails, qualify leads, run reports, schedule meetings) — have moved out of prototypes and into real-world pilots. Major platform vendors and open-source projects now offer agent frameworks that connect language models to company data, apps, and APIs. That means businesses

Quick summary Autonomous AI agents — software that can read your calendar, send emails, update CRMs, and pull together reports with little human prompting — are no longer experimental. Over the past year we’ve seen more tools and frameworks that let agents connect securely to apps and take multi-step actions. That shift turns isolated AI

The story in one line Over the last year the rise of commercial “AI agents” — autonomous, task-focused AI that can run workflows, pull data, and interact with systems — has moved from tech demos into real business pilots. That shift matters: businesses can now automate not just single tasks, but end-to-end processes like lead

Quick summary AI “agents” — autonomous workflows powered by large language models — have moved out of labs and into real work this year. Vendors and open-source frameworks now make it straightforward to build agents that read your systems, take actions (update a CRM, send follow-ups, create a report), and learn from feedback. That shift