Ron Mitchell

We’re at a practical turning point: AI agents — goal-driven AIs that can read systems, pull data, and act — are no longer just lab demos. Companies are using them today to automate repetitive work, generate better sales and financial reports, and speed up customer responses. Why this matters for business – Faster decisions: Agents […]

Summary AI agents—software that can act on your behalf, pull from company data, and complete tasks—are moving out of demos and into real workflows. Today’s agents can draft personalized sales outreach, triage customer support tickets, compile weekly performance reports, and trigger downstream automation in your CRM or workflow tools. Why this matters for business leaders

Short summary Over the last year we’ve moved past the “chatbot” phase of generative AI into a new era: autonomous AI agents that can plan, act, and follow up across multiple systems. These agents can qualify leads, generate and deliver routine reports, open or close service tickets, and trigger downstream automations — often with less

Quick summary AI agents — autonomous workflows driven by large language models — are moving from experiments into everyday business tools. Recent product updates and growing adoption mean agents can now pull private data, run multi-step tasks across apps, and generate real-time reports. When paired with retrieval-augmented generation (RAG) and vector databases, they produce accurate,

Quick summary Across 2025 into early 2026, AI agents — autonomous, task-focused AI that can act on your behalf across systems — have moved out of lab demos and into real business pilots. More companies are using agents to run sales outreach, handle customer triage, automate cross-system workflows, and generate operational reports. Improvements in integration

Short summary AI agents — autonomous, task-focused AI that can access your data, run tools, and complete multi-step workflows — are rapidly moving from research demos into practical business use. Big vendors and open-source projects are packaging agents as “copilots” and APIs that connect to CRMs, databases, calendars, and automation tools. The result: routine work

Quick summary AI “agents” — autonomous programs that combine large language models, company data, and simple workflows — are moving from labs into real business use. Instead of one-off chatbots, agents can run sales outreach, generate weekly financial reports, triage customer requests, or coordinate multi-step tasks across apps with minimal human hand-holding. Why this matters

The story (quick summary) • Over the last year, AI “agents” — custom assistants built on large language models — have moved from experiments into real business use. Companies are using them to qualify leads, summarize deals, automate follow-ups, and generate routine reports. • These agents combine LLMs with company data (often via retrieval-augmented generation),

Quick summary AI agents — software that can plan, act, and complete multi-step tasks across apps — have shifted from proof-of-concept demos to real business pilots. Instead of only generating text, these agents connect to CRMs, calendars, analytics, and automation tools to qualify leads, update records, generate reports, and trigger processes without constant human hand-holding.

Quick summary Autonomous AI “agents” — systems that can act, fetch data, take multi-step actions, and talk to apps — are moving from demos into real business use. Companies are using agents to auto-generate reports, qualify leads, update CRMs, and run routine processes without human micromanagement. At the same time, businesses are learning the hard