Ron Mitchell

The story — short and timely In 2024 more companies and platforms rolled out “AI agents” — customizable, task-focused assistants you can train to act on your data, run workflows, and communicate with people and systems. Tools like OpenAI’s custom GPTs, agent frameworks (LangChain-style), and vendor copilots made it much easier to build agents that […]

Quick summary AI “agents” — models that can carry out multi‑step tasks on their own — are moving from labs into everyday business tools. Instead of just answering questions, these systems can research leads, draft outreach, update CRMs, and build weekly performance reports with little human prompting. That shift is making AI less of a

Summary This year the market shifted from “AI that writes” to “AI that acts.” Vendors and open-source projects rolled out purpose-built AI agents — software that can read your data, interact with apps (CRMs, calendars, ticketing systems), and complete multi-step tasks without constant human prompting. Think agents that draft and send personalized outreach, assemble monthly

Quick summary Over the last year we’ve seen AI “agents” move out of demos and into business tools. These aren’t just chatbots — they’re LLM-powered agents that can connect to your CRM, ERP, BI tools, calendars and email to perform tasks end-to-end: qualify leads, generate and send follow-ups, create near-real-time reports, or route customer cases

What’s happening AI agents — purpose-built software that can read your systems, take actions, and talk to people — have moved from lab demos to real business pilots. Companies are using agents to pull together sales intelligence, draft outreach, update CRMs, and generate repeatable reports. Instead of asking humans to stitch tools together, these agents

Quick summary AI “agents” — small, goal-driven AI programs that can take actions, access tools, and follow multi-step workflows — moved from labs into real business pilots over the last 18 months. Companies are using agents to do things like qualify leads, generate routine sales follow-ups, synthesize reports from multiple data sources, and automate repetitive

Quick summary AI “agents” — autonomous, goal-driven AI assistants that can read, act, and iterate — went mainstream in 2024. Open-source frameworks (like LangChain and AutoGPT-style patterns), plus major cloud vendors adding agent capabilities, made it easier to connect LLMs to CRMs, databases, and business systems. The result: companies are using agents for lead qualification,

Quick summary AI “agents” — low-code or no-code tools that combine large language models with connectors, workflows, and decision rules — are no longer just experiments. Over the last year we’ve seen more platforms and vendor features that let non‑engineers assemble agents to: – pull data from CRMs, ERPs and data warehouses – generate and

Short summary AI “agents” — autonomous, multi-step AI assistants that carry out tasks across apps and data — are no longer just a research idea. Over the last year we’ve seen toolkits, frameworks, and vendor features that make it much easier for organizations to build agents that do real work: fetch data, run analyses, update

Quick summary AI agents — autonomous workflows built on large language models — are moving from tech demos into real business use. These agents can read your CRM, pull data from spreadsheets, generate a sales outreach sequence, assemble weekly performance reports, and even trigger follow-up tasks — all with minimal human input. Why this matters