Ron Mitchell

Quick summary AI “agents” — software that can act on your behalf, carry out multi-step tasks, and connect to apps like CRM, calendar, and BI tools — moved from experiments into real business use in 2024–2025. Major vendors released easier agent-building tools and more companies are running pilots that let AI draft outreach, update deals, […]

Quick summary In April 2024 OpenAI made it much easier for companies to build branded AI agents by releasing custom GPTs and the Assistants API. These tools let organizations create purpose-built, interactive AI assistants that connect to internal data, business systems, and external services — without needing to train a full model from scratch. Why

Why this story matters AI “agents” — autonomous, goal-driven programs built on large language models — are no longer just lab demos. Over the past year we’ve seen more off-the-shelf and custom agents plugged into CRMs, BI tools, and ticketing systems to handle tasks like lead qualification, follow-up scheduling, routine reporting, and first-line customer replies.

Quick summary AI “agents” — software that can act autonomously across apps and data — have moved from experiment to practical tool inside many companies. Instead of one-off chatbots, modern agents can read your CRM, update records, draft outreach, compile reports, and trigger workflows across SaaS tools with minimal human input. Why this matters for

Short summary AI “agents” — software that can act autonomously across apps, fetch data, run decisions, and even complete workflows — moved from demos into real business use in 2023–24. Tools built on agent frameworks (AutoGPT-style agents, LangChain, vendor copilots) are being used to qualify leads, generate recurring sales reports, audit invoices, and trigger routine

Quick summary AI “agents” — autonomous, task-focused AI that can access tools, apps, and data — have moved from labs into real business use. Over the last 12–18 months organizations have started building custom agents that can run sales outreach sequences, assemble weekly sales reports from multiple systems, triage support tickets, and automate parts of

Quick summary Over the last year AI “agents” — autonomous workflows that combine large language models with connectors, retrieval (RAG) and simple business logic — moved from lab experiments into real enterprise pilots. Companies are using agents to draft outreach, summarize calls, auto-update CRMs, and run recurring reports. The result: faster insights, less manual work,

Short summary Autonomous AI agents — software that plans, acts, and chains tasks with little human prompting — are no longer just demos. Businesses are using them to automate routine sales work (lead qualification, outreach sequencing), produce faster, narrative-rich reports from messy data, and orchestrate cross-system processes that used to require manual handoffs. Why this

What’s happening A new wave of AI agents and low‑code agent builders is making it practical for businesses to automate complex, multi‑step work. These AI agents can read your data, take actions in apps (like CRMs or ticketing systems), communicate with customers, and generate reports — all with far less developer time than older integrations

Quick summary There’s been a clear surge in businesses testing and deploying autonomous AI agents — small, goal-focused AI programs that can research, act, and report without constant human input. These agents combine language models, retrieval (RAG) from your company data, and simple automation to do tasks like qualify leads, update CRMs, draft proposals, and