Ron Mitchell

Short summary Over the last year the big shift has been clear: AI agents — autonomous, task-oriented systems that can act across apps and data — have moved from experimentation into everyday business use. Vendors and startups are packaging reusable agents and developer frameworks, while companies are deploying them to qualify leads, automate order processing, […]

Story pick (summary) Over the past year we’ve seen a clear shift: AI agents — conversational, goal-driven systems that can act across apps — are moving out of experimental pilots and into real business workflows. Advances in large language models, better connectors to CRMs, ERPs and BI tools, and tighter RPA (robotic process automation) integrations

Big idea in plain language AI agents — software that can carry out multi-step tasks on its own — have moved from demos into real business use. Major vendors are packaging agent tools and connectors that let companies automate workflows, pull live data, and even run meetings or customer follow-ups without building everything from scratch.

Quick summary AI agents that combine large language models with retrieval-augmented generation (RAG) and vector search are moving from experiments into real business use. Instead of asking a generic chatbot, companies are building agents that pull answers from their own sales data, product docs, CRM notes, and dashboards — then act (route leads, draft proposals,

Quick summary AI agents — software that can perform tasks, make decisions, and carry out multi-step workflows with little human intervention — are no longer just a lab curiosity. Early adopters are using agents to handle things like lead follow-up, invoice reconciliation, automated reporting, and scheduling. The result: faster responses, fewer errors, and lower labor

Quick summary AI agents — autonomous, task-focused systems that can read your data, take actions, and talk to other apps — moved from labs into real business use in 2024–25. Companies now use agents to qualify leads, schedule meetings, draft proposals, update CRMs, and generate routine reports. When set up well, these agents speed response

Short summary AI “agents” — configurable assistants that act on data, apps, and APIs — have moved from research demos into real business deployments. Over the last year vendors and platforms shipped better agent frameworks, easier connectors to CRMs and calendars, and safer function-calling tools. That makes it easier for non‑technical teams to create agents

Summary AI agents — autonomous, task-focused AI tools that can read your data, take actions, and produce reports — are no longer a niche experiment. Over the past year major cloud vendors and a wave of startups have made it much easier to connect these agents to CRMs, databases, calendars, and messaging apps. That means

What’s new AI agents — automated “digital workers” that can read systems, take actions, and carry out multi-step tasks — are moving out of pilots and into everyday business operations. Instead of one-off chatbots, companies are building agents that connect to CRM, ERP, calendar, and reporting systems to run sales outreach, qualify leads, update records,

Quick summary AI agents — autonomous, task-focused assistants built on large language models — have moved from experiments to real business tools. Instead of only answering questions, these agents can pull data from your CRM, schedule meetings, generate and send personalized outreach, and produce regular sales and performance reports automatically. Why this matters for business