Ron Mitchell

Quick summary AI agents — software that can act autonomously across apps and data — are no longer just a lab experiment. Over the past year businesses have begun using agent frameworks and orchestration tools to run real work: triage customer questions, generate weekly sales reports, personalize outreach at scale, and automate routine approvals. The […]

Quick summary AI “agents” — autonomous tools that can read your systems, take multi-step actions, and generate reports — are moving from lab demos into real business use. Major vendors and startups have added enterprise features: secure connectors to CRMs, audit logs, role-based access, and retrievable memory for consistent answers. That means agents can now

Quick summary AI agents—small, task-focused AIs that connect to your apps and data—are moving from labs into real business workflows. Instead of one-off chat responses, these agents can run day-to-day processes: update CRMs, draft outreach, triage support tickets, generate weekly reports, and trigger follow-up actions across systems. The shift is practical, not theoretical: teams are

Summary AI “agents” — software that can act autonomously to complete tasks, chase goals, and chain together tools — are no longer just prototypes. Over the last year we’ve seen companies move agent-based workflows into real business systems: agents that triage leads, assemble weekly sales decks, generate personalized outreach, and pull KPIs from multiple systems

Quick summary AI agents — autonomous, task-focused AI tools that can plan and act across apps — have moved from experiments into real business pilots. Companies are using agents to run multi-step workflows (like qualifying leads, compiling weekly reports, or routing customer requests) so teams spend less time on repetitive work and more time on

Big story (short): Over the past year, platforms like OpenAI’s Custom GPTs and similar “AI agent” tools from other vendors made it simple for non‑developers to build specialized assistants. These agents can read your documents, connect to your CRM, generate reports, draft outreach, and run repeatable workflows — all with conversational prompts. That shift turns

Summary AI “agents” — small, task-focused systems built on large language models that can read your CRM, draft emails, schedule meetings, and update reports — stopped being just a lab curiosity in 2024. Organizations are now wiring these agents into real business systems (CRMs, calendars, help desks, spreadsheets) to automate routine sales and operations work.

What happened AI agents — software that can take multi-step actions on your behalf (research, draft outreach, update systems, generate reports) — moved rapidly from proof-of-concept to production in 2024–25. Big vendors embedded agent-like features into everyday tools (think Copilot-style assistants in productivity suites, CRM vendors adding automated deal helpers) and open frameworks made custom

Quick summary AI agents — autonomous workflows that combine large language models, task tools, and automation — are no longer just research demos. Over the past year we’ve seen companies move from “playground” Auto-GPT-style experiments to production agents that qualify leads, generate and distribute reports, manage routine customer tasks, and trigger process automation across apps.

Short summary AI “agents” — autonomous AI programs that complete tasks by calling tools, APIs, and data — have moved from demos into real business use. Teams are now using agents to qualify leads, generate regular sales and financial reports, triage customer messages, and automate multi-step processes that used to need human handoffs. Why this