Ron Mitchell

Quick summary AI agents — small, goal-driven systems that combine large language models with APIs and tools — are no longer just a developer experiment. Companies are using them to qualify leads, auto-generate reports, triage support tickets, and orchestrate routine processes across CRM, calendar, and BI systems. When set up with clear rules and the […]

Quick summary AI agents — autonomous, task-focused systems that can research, draft, follow up, and update systems — are no longer just R&D experiments. Businesses are using them to automate sales outreach, generate reliable weekly reporting, triage customer requests, and perform repetitive back-office work. The result: faster decisions, fewer manual errors, and teams that spend

Quick story Over the last year the fast growth of “AI agents” — customizable assistants that can connect to CRMs, calendars, email and BI tools — has moved from demos into real business pilots. Platforms like OpenAI’s custom GPTs and enterprise copilots from major cloud vendors make it easier to build agents that run multi-step

Quick summary AI agents — task-focused, persistent AI assistants that can act across apps — have moved from tech demos to practical business tools. Over the past year we’ve seen vendor platforms and orchestration tools mature, making it much easier to connect agents to CRMs, calendars, databases, and RPA systems while adding logging and governance.

Summary AI “agents” — systems that combine large language models with tools, APIs, and automation — are no longer just demos. Over the last year we’ve seen businesses connect LLMs to CRMs, calendars, reporting databases and RPA tools to automate real, repeatable workflows: automated lead qualification, meeting scheduling, dynamic sales playbooks, and hands‑free performance reporting.

Quick summary – Over the past year we’ve moved past “cool demos” to practical AI agents that run real business tasks: qualifying leads, scheduling meetings, enriching CRM records, and generating near-real-time sales and operations reports. – These agents combine language models, automation tools, and connections to CRMs/BI systems so they can take actions (update records,

Quick summary AI agents — autonomous workflows powered by large language models that can read data, take actions, and talk to apps — have moved from experiments to business-ready tools. Improvements in model reliability, connectors to CRMs/ERPs, and agent-orchestration platforms mean companies can now build agents that qualify leads, generate monthly reports, or automate routine

Recent months have made one thing clear: AI agents—small, task-focused AI assistants that act on your data and systems—are moving from experiments to practical tools for business. Major platforms now make it easier to build custom agents that handle lead qualification, do outreach drafts, update CRMs, and generate executive reports automatically. Why this matters for

Hook / the story AI agents — autonomous software that can read your data, take actions, and talk to team members — have moved from prototypes to real business tools. At the same time, AI-powered reporting (natural-language summaries, automated dashboards, and instant “ask my data” queries) is making it easier for non-technical teams to get

Summary AI “agents” — autonomous assistants that connect to your CRM, email, calendars, documents, and dashboards — have moved from experiments to practical tools. Modern agent platforms let businesses build low-code workflows that can qualify leads, draft personalized outreach, generate weekly sales reports, and summarize customer meetings without human prompting every step. Why this matters