Ron Mitchell

Big idea Over the past year, major AI vendors and platforms made it far easier for companies to build custom AI agents — small, task-focused AI assistants that connect to your systems, read your data, and take actions. Think of a sales agent that drafts personalized outreach and updates your CRM, or a reporting agent […]

What’s happening Over the last year businesses have moved beyond one-off chatbots to deploy “AI agents” — autonomous or semi-autonomous systems that combine large language models with task-specific tools (calendars, CRMs, BI dashboards, automation platforms). These agents can draft proposals, qualify leads, generate reports, and trigger workflows without constant human direction. Why this matters for

Quick summary AI agents — autonomous, goal-driven applications built on generative models — are moving from experiments into real business use. Teams are already using them to generate monthly narratives from BI dashboards, triage sales leads, run exception checks in finance, and keep customer follow-ups happening around the clock. When paired with retrieval-augmented generation (RAG),

Summary AI “agents” — autonomous or semi-autonomous AI assistants that act across apps and workflows — moved from demos to real business pilots over the past year. Vendors like OpenAI, Microsoft, and Google made it easier to build custom agents that can draft emails, update CRMs, pull data for reports, and even run follow-up tasks

Quick summary AI agents — software that can act autonomously across apps and data to complete tasks — have moved from lab experiments into real business use. Over the past year, major AI platforms and a wave of purpose-built vendors added better connectors, retrieval (RAG) capabilities, and observability tools that let agents interact safely with

Quick summary AI “agents” — small, goal-directed AI programs that act on your apps and data — have moved from lab demos into real business use. In 2024–25 we’ve seen major vendor and open-source advances that make agents faster, more reliable, and easier to connect to CRMs, databases, and BI tools. At the same time,

Short summary Autonomous AI agents — systems that can carry out multi-step tasks on their own (research, outreach, data pulls, report writing) — moved from demos to real business pilots in 2024–25. Large AI providers and startups now offer agent frameworks and connectors to CRMs, calendars, databases and BI tools. That means companies can automate

Quick summary – What’s happening: A new wave of accessible “autonomous AI agents” — systems that combine large language models with data connectors, task orchestration, and simple rules — is making it practical to automate whole sales and operations tasks, not just individual prompts. – Real examples businesses are using today: agents that pull CRM

The story (quick summary) Autonomous AI agents — software that can act on your behalf to research leads, write outreach, schedule meetings, and generate reports — have moved from experiments to practical tools for businesses. Large vendors now offer “copilot” features inside CRMs, email, and reporting platforms. That means routine sales tasks and recurring reports

Quick summary AI has moved beyond chatbots. Today’s “autonomous agents” can read your systems, start tasks, and follow multi-step workflows — for example, pulling CRM data, drafting outreach, scheduling emails, and updating dashboards without someone doing every click. Open-source agent frameworks and major vendors adding agent-like features have pushed this from labs into real business