Ron Mitchell

Short summary AI “agents” — software that plans, acts, and connects across apps — are moving from experiments into real business use. Open-source agent frameworks (like LangChain-based tools), vendor offerings (Copilot/assistant products), and low-code builders now let companies automate multi-step work: gathering data, making decisions, and taking actions inside CRMs, calendars, ticketing systems, and BI […]

Quick summary Major platform vendors are shipping “AI agent” features that can act autonomously across systems — scheduling, qualifying leads, drafting proposals, and generating on-demand reports. Instead of one-off chat prompts, these agents can hold context, call internal tools (CRM, calendars, BI), and run multi-step workflows without constant human direction. Why this matters for business

Short summary AI agents — autonomous, task-focused AI programs that can run workflows, pull data, and interact with apps — are no longer just proofs of concept. Businesses are deploying them to handle sales outreach, automate repetitive processes, and create up-to-date reports by combining internal data with large language models. These agents can schedule meetings,

Quick summary There’s been a clear shift this year: businesses are moving from simple chatbots and rule-based automation to autonomous AI agents that can plan, act across apps, and complete multi-step tasks. Instead of waiting for human direction at every step, these agents can draft outreach, pull and summarize data, update records, and trigger workflows

Short summary AI “agents” — autonomous tools that can follow instructions, access systems, and take multi-step actions — are no longer an experiment. Over the past year, more businesses have started using agents to handle repeatable tasks like lead qualification, booking meetings, CRM updates, invoice processing, and automated reporting. These agents combine language models, connector

Quick summary – What’s happening: AI agents — autonomous or semi-autonomous tools that complete multi-step tasks (think: qualify a lead, book a demo, pull and summarize sales reports) — are being embedded into everyday business apps. Major cloud and SaaS vendors, plus new agent orchestration platforms, have made it easier to deploy agents that connect

Quick summary Over the past year, AI “agents” — autonomous models that can use tools, fetch company data, and complete multi-step tasks — have moved from research demos into real business products. Major cloud vendors and startups are shipping agents that connect to CRMs, calendars, databases, and reporting tools so the AI can prepare briefs,

Summary AI “agents” — software that can plan, act, and follow up on tasks with minimal human prompts — have moved from research demos to real business pilots. Companies are using agents to triage leads, draft personalized outreach, update CRMs, and generate routine reports. The result: faster response times, fewer manual errors, and teams freed

Quick summary AI “agents” — automated systems that can read, act, and learn across apps — are moving out of labs and into real business workflows. Companies are using agents to do end-to-end tasks like finding prospects, drafting personalized outreach, updating CRM records, and generating reports. Big platform players and startups are packaging agent capabilities

Big picture (the story) – Over the past year the biggest move in AI hasn’t been a new chatbot — it’s been autonomous AI agents. Frameworks and tools like LangChain, Microsoft’s Semantic Kernel, and new “function-calling” patterns from major models have made it far easier to build agents that act on your data, run multi-step