Ron Mitchell

Quick summary AI agents — autonomous, task-focused systems built on large language models — have moved from demos into real business use. Companies are now pairing agents with retrieval-augmented generation (RAG) to give them safe access to company data (CRM notes, product specs, spreadsheets). The result: agents that can research leads, draft outreach, build executive […]

Quick summary AI agents — autonomous, task-focused AI that can read your systems, take actions, and report results — are moving from experiments to real business use. Open-source frameworks and commercial tools have lowered the technical barrier, so teams can build agents that qualify leads, update CRMs, generate executive reports, and even triage customer issues

Quick summary AI “agents” — software that can act autonomously across apps, pull from your data, and carry out multistep workflows — moved from demos to real business use in the last 18–24 months. Major platforms and open-source tools made it easier to connect agents to CRMs, data lakes, calendars and ticketing systems. That means

Summary AI “agents” — autonomous software that can carry out multi-step tasks (pull data, run analysis, take actions, and report back) — are catching on fast. Advances in large language models, agent frameworks, and secure connectors to CRMs and ERPs mean these agents can now handle everyday business work: generating weekly sales reports, qualifying leads,

Quick summary AI agent platforms from major vendors are making it easier for businesses to build small, task-focused AI “agents” that can do things like qualify leads, draft personalized outreach, update CRMs, or assemble weekly sales reports automatically. Instead of one-off chatbots, these agents can connect to company data, run multi-step workflows, and hand off

AI story summary Major vendors and platform providers have pushed AI agents out of labs and into low-code builders and production tools. That means businesses can now create purpose-built “agents” that handle tasks end-to-end: qualify leads, draft personalized outreach, triage customer tickets, or assemble monthly performance reports from multiple systems. Why this matters for business

Why AI agents are the next big lever for sales, ops, and reporting Summary — the story in plain terms AI agents — software that can autonomously run multi-step tasks, make decisions, and interact with people or systems — are moving from experiment to everyday business use. Instead of only generating text or summaries, these

Quick summary AI agents — software that can act autonomously to complete tasks, pull data, and talk to other systems — are moving from labs into real business use. Over the last year we’ve seen a surge in agent platforms and “agent-as-a-service” tools that let companies automate work like lead qualification, report generation, customer triage,

Quick story Autonomous AI “agents” — systems that combine language models, tools, and data to complete multi-step tasks — moved from experiments into real business use in 2023–24. Open-source projects (Auto-GPT, LangChain agents) and vendor copilots (Microsoft/Google/SaaS integrations) made it easy to stitch LLMs into workflows. The result: businesses are automating end-to-end tasks like lead

Quick summary AI agents — software that can act on behalf of people by using tools, pulling data, and taking multi-step actions — have moved from proofs-of-concept to practical business tools. Companies are using agents to qualify leads, automate follow-ups, generate timely sales reports, and coordinate routine finance and operations tasks. That shift is making