Ron Mitchell

Quick summary AI agents — configurable, goal-driven AI “workers” that can act across apps and data — are moving from research demos to real business use. Developer frameworks (LangChain, Auto-GPT patterns), platform integrations (Copilot-style assistants), and easier data connectors mean companies can now automate multi-step work: outreach, seller enablement, report generation, and simple approvals. Why […]

Quick story AI “agents” — autonomous, multi-step AI assistants that can read systems, take actions, and coordinate tasks — have moved from demos into real business pilots. Companies are using agents to enrich leads, run weekly sales reports, draft follow-ups, and trigger cross-system workflows without manual handoffs. That shift means AI is no longer just

What’s happening Major AI platforms are making it easy to build “AI agents” — autonomous assistants that can read email, update a CRM, summarize meetings, run reports, and even take actions across apps. Vendors from cloud giants to specialized startups are shipping agent toolkits, pre-built connectors, and low-code flows that let non-engineers create task-specific bots

Quick summary AI agents — autonomous workflows powered by large language models that can read, act, and connect to your tools — have moved from experiments to practical business use. Instead of just giving answers, these agents can qualify leads, update CRMs, draft proposals, run recurring reports, and trigger actions across apps with minimal human

Short summary AI-driven agents — software that can run multi-step tasks, talk to your apps, and take actions — are moving out of labs and into real business workflows. Companies are piloting agents for lead qualification, meeting prep, first-line support, and automated reporting. The result: faster responses, fewer manual handoffs, and cleaner data feeding your

A quick update you can use: Over the past year we’ve seen a clear shift: AI agents — autonomous, multi-step AI workflows that can act on data, talk to systems, and follow business rules — are moving from experiments into production across sales, customer support, and operations. Companies are using these agents to qualify leads,

Quick summary AI “agents” — autonomous, multi-step AI workflows that can use tools, fetch data, and take actions — are no longer just research demos. In the past 18–24 months major LLM platforms and enterprise vendors made agent frameworks easier to deploy, and businesses are starting pilots that let AI do entire tasks end-to-end (lead

Summary AI agents — autonomous software that can perform tasks, follow-up, and make decisions based on data — are no longer just demos. Businesses are using them to qualify leads, update CRMs, generate sales reports, and run routine outreach. When set up correctly, these agents speed work, reduce human error, and free sales teams to

Quick summary Over the last 12–18 months we’ve moved from talking about “AI models” to deploying purpose-built AI agents that actually do work: qualify leads, generate meeting briefs, answer product questions, and pull tailored reports. No-code and low-code builders (think Custom GPTs, Copilot-style studios, and agent frameworks) let non‑engineers create agents that connect to CRMs,

What’s happening AI agents — smart, task-focused systems that connect to your calendar, CRM, email, and internal documents — are moving from experiments into real business use. Instead of just answering questions, these agents complete multi-step tasks: draft personalized outreach, book meetings, update CRM records, and generate weekly sales reports automatically. Companies using them report