Ron Mitchell

The story in one line AI agents — autonomous, task-focused AI that can read systems, take multi-step actions, and interact with people — have moved from research demos into real business use. Sales, operations, and finance teams are now using agents to qualify leads, update CRMs, automate reporting, and run routine workflows without constant human […]

A quick read: Advances in large models and low-cost deployment tools have moved AI agents from experiments to real business impact. Companies are no longer just testing chatbots — they’re building autonomous assistants that pull from internal data, run workflows, and generate reports on demand. That shift is changing how teams sell, serve customers, and

Summary AI “agents” — autonomous workflows built from large language models that can use tools, call APIs, and complete multi-step tasks — have crossed an important threshold. Improvements in model reliability, agent frameworks, and secure integrations mean companies are no longer just prototyping agents; they’re putting them into production for real business tasks like lead

Quick summary – Over the past year enterprises have moved from experimenting with chatbots to deploying AI agents: small, task-focused AI that can access your apps (CRM, calendar, email, ERP), run workflows, and produce reports automatically. – Real results are showing up: faster sales cycles, fewer manual reporting hours, and more consistent follow-ups — not

Short summary AI agents — autonomous, task-focused AI assistants that can read your systems, take actions, and talk to people — are no longer just a research demo. Over the past year we’ve seen vendors and in-house teams move from experiments to production: prebuilt agents for lead qualification, automated invoicing, and real-time reporting are being

Quick summary AI has moved beyond answering questions. Today’s AI agents can take actions: read your CRM, pull data from finance systems, draft outreach, run reports, and even trigger downstream workflows. Businesses are shifting from one-off pilots to production deployments because agents finally connect language models to real tools, enterprise data, and rules — so

Quick summary AI “agents” — software that combines large language models with automation and company data — are moving from labs into real business workflows. Vendors like Salesforce and Microsoft have embedded these agent capabilities into CRMs and BI tools, and startups are launching autonomous helpers that can qualify leads, draft outreach, schedule meetings, and

What happened (short): AI “agents” — models that act autonomously, use external tools, and carry out multi-step workflows — have moved from research demos into real business pilots. Frameworks and toolkits (agent builders, connectors to CRMs, RPA, and data warehouses) make it faster to stitch generative AI into day‑to‑day processes: think autonomous lead qualification, automated

Quick summary AI agents — software that can take multi-step actions (write emails, update CRMs, schedule meetings, build reports) — are no longer just demos. Over the past year we’ve seen major CRM and cloud vendors embed agent-style copilots and third-party tools bring autonomous workflows into real sales and operations environments. That means businesses can

Short summary AI “agents” — AI that can take multi-step actions (research, write emails, run reports, update systems) — have moved from demos into practical tools businesses can use today. Platforms and frameworks like LangChain, AutoGPT-inspired tools, and major cloud vendors now make it easier to connect models to calendars, CRMs, databases and APIs so