Ron Mitchell

Short summary AI “agents” — autonomous software that plans, acts, and iterates to complete multi-step tasks — have moved from research demos to practical business tools. Using frameworks like LangChain and orchestration platforms, companies are now running agents that draft outreach, reconcile reports across systems, automate approvals, and triage customer issues without constant human direction. […]

Summary AI agents — autonomous, connected AI tools that can read CRM data, send messages, schedule meetings, and generate reports — have moved beyond demos. Companies are now using them to qualify leads, automate routine customer support, and produce near-real-time reporting. The result: faster sales cycles, lower operational cost, and more consistent follow-up. Why this

Quick summary OpenAI’s rollout of custom “GPTs” (no-code, customizable AI assistants) has pushed AI agents into the mainstream. Businesses can now create purpose-built agents that access company data, run simple workflows, answer questions, and connect to external tools — all without building a model from scratch. Why this matters for businesses – Faster deployment: Teams

Story summary AI agents — software that can act autonomously across apps and data — have moved from demos into real enterprise use. Modern agents combine large language models with connectors, retrieval-augmented generation (RAG), and orchestration tools so they can complete multi-step tasks: qualify leads, pull and summarize sales reports, update CRMs, schedule follow-ups, and

Trending story (short summary) AI agents — small, purpose-built AI programs that can read your CRM, send emails, schedule meetings, and pull data from dashboards — went from proof-of-concept to practical in 2024–25. Advances in retrieval-augmented generation (RAG), secure connectors to business systems, and “no-code” agent builders mean companies can now create agents that handle

Quick summary AI agents — autonomous or semi-autonomous AI “workers” that can read, act, and follow multi-step workflows — have moved from experiments into real business apps. Improvements in large language models, retrieval-based pipelines, and low-code agent builders have made it much easier to connect agents to CRMs, ERPs, ticketing systems, and data warehouses. That

Quick summary AI “agents” — autonomous assistants that can read your systems, write messages, run reports, and trigger automations — moved from demos into real business use in 2024–25. Major platforms (enterprise copilots and assistant frameworks) now connect to CRM, email, calendars, and databases so an AI can do end-to-end tasks: qualify leads, draft proposals,

Quick summary AI agents — autonomous, app-connected assistants that can read, act, and follow multi-step instructions — are moving from tech demos into real business use. Companies are using them to draft personalized sales outreach, update CRMs after meetings, generate weekly performance reports, and automate approval workflows. The result: faster work, fewer manual errors, and

Quick summary Leading AI vendors and enterprise teams are pushing AI agents — autonomous, task-focused models that can act across apps, pull in data, and complete multi-step workflows. Companies are already using these agents for things like lead qualification, customer support triage, automated reporting, and routine finance/process tasks. The shift: agents are becoming reliable enough

Quick summary AI agents—autonomous software assistants that can read data, take actions, and talk to your apps—have moved from experiments into real business use. Instead of single-chat interactions, these agents can run workflows: qualify leads, update CRMs, generate and distribute reports, and escalate exceptions. That shift is making routine work faster, reducing errors, and freeing