Ron Mitchell

Short summary AI “agents” — AI systems that plan, act, and connect to apps and data — moved from labs into real business tools during 2023–2024. Major vendors released agent and “custom AI” frameworks that let organizations build assistants which can run tasks (book meetings, pull and summarize sales data, open tickets, route emails) across […]

Quick summary AI “agents” — autonomous bundles of AI models, connectors, and simple rules that can act on your behalf — are moving from labs into real business use. Vendors and startups are packaging agents that plug into CRMs, BI tools, calendars, and email so a single automated worker can draft outreach, update records, run

Quick summary AI agents — small, LLM-based programs that can read, act, and follow up across apps — have moved from experiments to production-ready tools. Over the last couple of years we’ve seen cloud providers, open-source frameworks, and vendor toolkits converge with better retrieval (vector databases), secure private LLM hosting, and workflow integrations. The result:

Short summary A new wave of “AI agents” — autonomous, task-focused AI assistants that can use tools, fetch data, and take actions — is moving from labs into everyday business systems. Big vendors (think Copilot-style features in CRM and ERP), plus open frameworks (LangChain, AutoGen, etc.), now let companies build agents that draft outreach, update

Quick summary AI agents — autonomous, task-oriented systems that can read, act, and interact with tools and people — are no longer just lab experiments. Companies are now using them in sales, customer success, finance, and operations to handle multi-step workflows: researching leads, drafting outreach, updating CRM records, generating monthly performance reports, and flagging anomalies

Story (short): AI agents — autonomous, conversational tools built on large language models (think GPT-powered “agents,” Copilot-style assistants, and custom task bots) — are moving from experiments into real business workflows. Companies are using them to qualify leads, generate and reconcile reports, automate order processing, and run recurring analytics jobs without a human in the

Summary AI agents — autonomous, multi-step AI assistants that can research, act on systems, and follow up — have stopped being a novelty. Over the past year companies have moved from experimenting with single-use chatbots to deploying agents that run parts of workflows: enriching leads, preparing executive reports, scheduling and following up on outreach, and

Quick summary AI agents — autonomous, goal-driven software that can read data, take actions, and follow rules — are no longer just lab projects. Over the past year many companies have moved pilots into production to automate sales outreach, qualify leads, handle routine customer support, and generate executive-ready reports. Improvements in model reliability, cheaper compute,

Story summary AI agents — software that can act autonomously to run tasks, talk to systems, and make decisions — have moved from experiments into real business use. Over the past year more companies and cloud providers have released agent frameworks, and teams are using them for things like lead qualification, customer follow‑up, automated reporting,

Quick summary AI agents—customizable, goal-driven “virtual workers” that can run tasks end-to-end—are moving from labs into everyday business use. Marketplaces and tooling (think prebuilt agent templates, API orchestration, and low-code connectors) are making it faster and cheaper to deploy agents for sales outreach, customer triage, data cleanup, and automated reporting. The result: businesses can scale