Ron Mitchell

Quick summary AI “agents” — autonomous workflows that combine language models, tools, and data — moved from hacker projects into real business pilots in 2023–24. Tools and frameworks like Auto-GPT patterns, LangChain and commercial Copilot-style offerings made it much easier to automate tasks that used to need human attention: lead qualification, follow-up emails, CRM updates, […]

Summary Businesses are increasingly using autonomous AI agents — software that can take multi-step actions on its own — to do things like qualify leads, generate personalized outreach, update CRMs, and produce weekly performance reports. Large vendors are embedding agent capabilities into productivity and CRM platforms, and startups are building specialized agents for sales, ops,

Summary AI agents — autonomous, workflow-focused AI assistants — are no longer just lab experiments. Over the past year we’ve seen tools and platforms make it much easier to connect agents to CRMs, calendars, databases, and reporting pipelines. That means an agent can now draft follow-up emails, pull and summarize sales data, triage support tickets,

Quick summary Large software vendors are embedding AI agents directly into business apps. Microsoft’s Copilot features in Dynamics 365, Salesforce continues to expand Einstein GPT, and Google and niche vendors are bringing agent-style assistants into CRMs, help desks, and reporting tools. These agents can read your CRM, pull in documents, draft emails, update records, and

Summary AI “agents” — software that can act autonomously across apps (email, CRM, calendar, docs) — moved from demos to real business use in 2024–25. No longer just experiments, low-code agent platforms and enterprise-grade integrations are letting teams automate common sales and operations work: lead triage, follow-up emails, meeting summaries, and routine reporting. Why this

Quick summary AI “agents” — systems that can plan and act across apps to finish tasks — are moving from demos into real business use. At the same time, Retrieval-Augmented Generation (RAG) — using searchable company data to give accurate, up-to-date answers — is becoming the standard way to keep those agents grounded in your

Big idea — what happened Over the last year we’ve seen AI agents move from research demos to real business tools. These aren’t just chatbots — they’re autonomous workflows that combine large language models with connectors (CRMs, spreadsheets, email), retrieval (RAG), and simple logic to complete multi-step tasks. Companies are using agents to automatically prepare

Why this matters now AI agents — autonomous, task-focused AI that can act across apps and data — moved from lab experiments to real business pilots in 2024–2025. Companies are using agents to run outreach, triage support tickets, assemble weekly reports, and automate recurring workflows. That shift matters because agents can reduce repetitive work, accelerate

Quick summary AI agents — small, task-focused systems powered by large language models — are shifting from pilots into regular use across sales, finance, and operations. Teams are using them to automate repetitive work: qualify leads in the CRM, generate weekly sales and finance reports, triage support tickets, and run routine reconciliations. The result is

What happened AI agents — autonomous software powered by large language models that can take actions (send emails, update CRMs, run queries, create reports) — are suddenly practical for real business work. Improvements in model reliability, easier integrations (APIs, Zapier/Workato, CRM connectors), and safer control layers (human-in-the-loop, permissioning) mean companies are moving from pilots to