Ron Mitchell

Summary The big story: customizable AI agents — think branded, task-focused versions of GPT-style models and agent frameworks — moved from labs into real business use in 2024. These agents can access your apps, run workflows, pull and summarize data, and act on instructions with minimal human supervision. That shift makes AI not just a […]

Big picture Large cloud and AI vendors have moved from demos to production-ready “AI agents” — software that can autonomously run multi-step tasks like qualifying leads, generating client reports, updating CRMs, and scheduling follow-ups. These agent frameworks (think vendor agent toolkits, enterprise plugins, and secure connectors) are being adopted not just by tech teams but

Quick summary AI agents — autonomous software that can act across apps, run multi-step workflows, and learn from data — have moved from experiments to real business tools. Today’s agents plug into CRMs, calendar systems, document stores and reporting dashboards to do things like triage leads, draft proposals, update records, and produce executive-ready reports. Why

The story (short) AI agents and enterprise “copilots” — autonomous tools that can research, act on behalf of users, and run end-to-end tasks — moved from experiments into real business usage in 2024–25. Big vendors and a wave of startups shipped agents that connect to CRMs, calendars, databases and Slack, enabling workflows like automated outreach,

Quick summary AI “agents” are the new way companies are putting large language models to work. Instead of asking an LLM one question at a time, agents combine LLMs with connectors, retrieval (RAG), business rules, and automation to perform multi-step tasks: qualify leads, draft follow-ups, update CRMs, generate reports, or triage support tickets. Open-source frameworks

Story summary AI “agents” — goal-directed systems that can read company data, take actions, and follow up — moved from demos into real business pilots in 2024. Major vendors and developer frameworks made it easier to connect agents to CRMs, ERPs, and business analytics. That means tools that used to only suggest answers can now

Quick summary AI agents—autonomous, multi-step AI programs that can read data, take actions, and interact with apps—have shifted from research demos into real business products. Major platforms (Copilot, Gemini, Einstein GPT) and open frameworks (LangChain, AutoGen) make it easier to build agents that can qualify leads, update CRMs, generate and distribute reports, and automate routine

Quick summary Major AI vendors and startups are making AI agents — goal-driven, multi-step assistants that can act across apps — easier to build and safer to deploy. Instead of one-off chat answers, these agents can run tasks (e.g., gather data, update CRM, send emails, create reports) and keep working until a goal is reached.

Quick summary AI “agents” — autonomous workflows that combine language models, connectors, and business rules — have moved from pilots into real production across many companies. Instead of just answering questions, these agents now handle day-to-day tasks: qualify leads, update CRMs, book meetings, summarize calls, and generate automated sales and operational reports. The result is

Summary AI “agents” — autonomous software that can read, decide, and act across apps — moved from research demos to real-world pilots in 2024. Companies are connecting agents to CRMs, ERPs, email, and BI systems so the agents can qualify leads, assemble reports, triage support tickets, and trigger downstream tasks without a human in the