Ron Mitchell

Summary There’s a clear shift happening: AI agents — autonomous, goal-driven software that can act across apps and data — are moving out of developer sandboxes and into real business use. Over the last year we’ve seen more off-the-shelf agent frameworks, agent marketplaces, and low-code connectors that let teams automate tasks without custom engineering. Why […]

Quick summary – In recent months the big cloud and AI vendors have pushed low‑code “AI agent” builders — think custom assistants you can configure (not code) to do tasks like summarize calls, draft outreach, run data queries, or trigger workflows. – These tools combine a language model with connectors (CRMs, email, sheets, databases) and

Short summary AI agents—software that can plan, act, and connect to multiple systems on their own—are moving from experimental labs into real business workflows. Instead of asking a single model a question, companies are now using agents that combine language models, retrieval (RAG), integrations (CRMs, ERPs, ticketing), and simple automation to complete end-to-end tasks: qualify

Quick summary AI “agents” — autonomous or semi-autonomous assistants built from large language models and automation tools — are now practical for real business work. Think beyond chatbots: these agents can pull data from your CRM, run queries, draft proposals, update pipelines, and generate repeatable reports without a human starting every step. You’ve already seen

Quick summary Over the last year the market moved from “AI that answers questions” to “AI that acts.” Platforms (custom GPTs, agent frameworks like LangChain/Auto-GPT, and vendor copilot tools) make it easier to build autonomous AI agents that can pull data, update systems, draft messages, and generate reports — without a human typing every step.

Quick summary AI agents — autonomous, goal-driven software built on large language models — are no longer just lab experiments. Over the past year major cloud and SaaS vendors have added agent-like features, and more companies are quietly moving pilot projects into production to automate sales outreach, generate real-time reports, and manage routine tasks. Why

Story summary AI “agents” — autonomous, multi-step AI workflows that can read your data, take actions, and hand off to people — have moved from research demos to practical business tools. Over the last year we’ve seen more off-the-shelf and custom agent solutions that connect to CRMs, calendars, email, and dashboards. That makes it easier

What happened Over the last 12–18 months we’ve seen a rapid rise in autonomous AI agents and agent orchestration tools that can take end-to-end actions: capture leads, qualify them via chat or email, log activity in your CRM, draft personalized proposals, and even generate revenue forecasts and executive-ready reports. These systems pair large language models

Short summary Over the last year we’ve seen a clear shift: autonomous AI agents are moving out of experiments and into real business workflows. These aren’t sci‑fi robots — they’re software agents that can read your data, take actions (open tickets, update a CRM, generate reports), and follow simple rules without constant human prompting. Companies

What’s happening A new wave of autonomous AI agents — small, task-focused AI programs that can act across apps and systems — is moving from labs into real business use. These agents can ingest emails, CRM records, analytics, and documents; make decisions; and take actions like qualifying leads, updating systems, or generating reports. Improved large