Ron Mitchell

Quick summary AI “agents” — autonomous software that can follow instructions, connect to your apps, run workflows, and take actions — have moved from experiment to everyday tool. Over the past year we’ve seen a surge in platforms and integrations that let agents: – pull CRM and finance data, – generate and send personalized outreach, […]

Short summary AI agents — autonomous AI assistants that can run tasks, make decisions, and talk to other systems — have gone from labs to real business pilots. Over the last year vendors and open-source projects released easier ways to build and orchestrate agents, and major cloud platforms added agent-friendly services. That combination is making

The story (short) – Autonomous AI agents — software that can plan, act, and follow up with little human supervision — have moved beyond experiments and into everyday business tools. – Major cloud providers and startups are packaging these agents for tasks like lead qualification, customer triage, automated reporting, and routine process automation. Companies are

Quick summary Autonomous AI agents — tools that can plan, act, and chain together multiple steps — are no longer just research demos. Businesses are deploying them to do things like qualify leads, update CRMs, draft follow-up emails, and generate near-real-time sales reports. These agents combine language models, data connectors, and simple automation so a

Quick summary AI “agents” — autonomous or semi-autonomous AI tools that can fetch data, run workflows, and produce answers — have moved from experiments into real business use. Over the last year major platforms and vendors expanded agent capabilities and integrations, making it easier to connect agents to CRMs, ERPs, and analytics stacks. That means

Summary AI agents — software that can plan and execute multi-step tasks on its own — are no longer just a research demo. Businesses are increasingly using agents to handle real workflows: qualifying leads, summarizing meetings and updating CRMs, automating invoice processing, and generating routine reports. These agents combine LLMs, connectors (to CRM, email, finance

Quick summary AI agents — software that can act on your behalf, pull data, draft messages, and even trigger workflows — are moving from labs into everyday business apps. Big vendors (think Copilot-style assistants in Microsoft/Google ecosystems) and fast-growing startups are embedding agents into CRMs, calendars, and reporting tools. At the same time, regulations like

Quick summary – What’s happening: Autonomous AI agents—customized versions of large language models that can access your data, call tools, and act on behalf of users—have moved from demos into real commercial tools. Platforms like customizable GPTs, retrieval-augmented systems (RAG), and orchestration frameworks let companies automate tasks that used to need lots of human time.

Summary AI agents — autonomous software that can run tasks, ask follow-ups, and pull information — are moving from demos into real business use. Combined with retrieval-augmented generation (RAG), these agents can read your internal docs, databases, and dashboards, then generate accurate, context-aware reports or take actions (e.g., flag risks, update CRM records, route leads).

Short summary AI “agents” — automated assistants that can read your data, take actions, and follow up on tasks — are no longer just demo tech. Companies are starting to use agents to do repeatable business work: qualify leads, update CRMs, generate weekly reports, and even coordinate simple account-management tasks. That shift is accelerating because