Ron Mitchell

The story in short AI agents — autonomous, task-focused AI that can read systems, take actions, and follow multi-step instructions — are no longer just lab experiments. More companies are deploying them to handle routine but time-consuming work: qualifying leads, generating proposals, reconciling data, and producing narrative sales reports. The result: faster workflows, fewer manual […]

Short summary AI agents — autonomous software that reads, plans, and acts across apps — have moved from experiments into enterprise tools. Over the last year major vendors and startups have shipped agentbuilders and integrations that let AI complete tasks like scheduling, lead follow-up, purchase-order approval, and routine reporting. That means AI can now do

Quick summary AI agents — software that can autonomously carry out workflows across apps (think: qualify leads, update CRMs, generate reports, or triage support tickets) — are moving from lab experiments into real business use. Advances in agent frameworks, plugins, and connectors make it far easier to link an agent to your CRM, calendar, and

Quick summary AI agents — software that can act across apps, take multi-step actions, and run end-to-end workflows — are moving out of labs and into real business use. Instead of a human copying notes between systems or drafting repetitive emails, an AI agent can qualify leads, schedule follow-ups, update your CRM, and generate a

Quick summary AI agents — autonomous, task-focused AI that can act inside apps, fetch data, write, and take next steps — are no longer just research demos. Over the past year we’ve seen major platform features and a wave of startups making agents practical for day-to-day business work: automating outreach, producing finance and sales reports,

Summary AI “agents” — autonomous workflows that combine large language models, connectors (to CRMs, databases, and BI tools), and automation — have moved beyond experiments. Vendor frameworks and orchestration tools (LangChain-style toolkits, platform copilots, RPA integrations) make it easier to build agents that qualify leads, run outreach, generate executive reports, or triage customer issues without

Short summary AI agents — software that can plan, act, and complete multi-step tasks on its own — moved from demos to real business use in 2024. Companies are pairing agents with internal data, automation tools, and human approvals to handle tasks like lead qualification, routine customer support, financial reconciliation, and automated reporting. Why this

Quick summary AI agents — autonomous AI tools that can plan, act, and follow up on tasks — are no longer just experiments. Major cloud and software vendors have released agent frameworks and templates that let companies automate multi-step work: writing sales outreach, triaging support tickets, generating weekly reports, and even coordinating cross-team approvals. Why

What’s happening Over the past year we’ve moved from “experimental” AI demos to practical, business-ready AI agents — purpose-built virtual assistants that can read your data, take actions (send emails, update CRMs, run reports), and coordinate multi-step tasks. New agent frameworks, integrations, and better retrieval methods make it possible to automate complex workflows while keeping

Summary of the story AI “agents” — autonomous or semi-autonomous AI systems that can run tasks, follow workflows, and talk to other apps — have moved from research demos into real business pilots. Companies are using agents to automate follow-ups, draft personalized proposals, triage customer support, and generate recurring reports. These tools combine large language