Ron Mitchell

Quick summary AI agents — software that can carry out multi-step tasks, talk to systems, and learn from feedback — have shifted from lab demos to real business deployments. New agent orchestration tools, safer model APIs, and better data connectors mean companies can now automate complex workflows (like building reports, handling customer queries, or qualifying […]

If you’ve been hearing more about “AI agents” lately, that’s because major cloud and SaaS vendors — and startups — are packaging AI that can act, not just assist. These agents can read your inbox, pull data from a CRM, draft outreach, generate weekly sales reports, and even trigger follow-up tasks across systems. What this

Quick summary AI “agents” — AI systems that can take multi-step actions, use external tools, and work across apps — jumped from labs into real business use over the last 12–18 months. Vendors and open-source projects now offer agent frameworks that can search your data, draft emails, update CRM records, generate reports, and trigger workflows

Quick summary AI “agents” — autonomous AI that can read your docs, pull CRM data, send emails, schedule meetings, and generate reports — moved from demo-stage to real business use in the past year. Improvements in large language models, retrieval-augmented generation (RAG), and pre-built connectors mean companies can now safely automate repeatable workflows that used

Quick summary Major AI platforms have rolled out simple tools for building custom AI agents (think OpenAI “GPTs,” Microsoft Copilot Studio, and similar vendor features). Instead of only using a generic chatbot, companies can now create purpose-built agents that do things like draft personalized outreach, pull and summarize sales reports, or trigger routine automations across

Quick summary Autonomous AI “agents” — tools that can take multi-step actions, talk to your systems, and complete tasks without constant human prompts — are moving from research demos into real business tools. Companies are using agents to generate sales outreach, update CRMs, run routine reporting, and even triage customer requests. The result: faster work,

Quick summary AI agents — autonomous, task-focused AI that can act on your systems and data — are rapidly shifting from experiments to real business value. Companies are using agents to qualify leads, run outreach sequences, automate routine reporting, and orchestrate cross-team workflows. The result: faster sales cycles, fewer manual errors, and more time for

Quick summary AI agents — autonomous, goal-oriented tools that can read, act, and connect systems — have shifted from experiments to practical business use. Companies are using agents to qualify leads, automate recurring reports, handle order exceptions, and run multi-step processes across CRM, ERP, and communication tools. The result: faster decisions, fewer manual handoffs, and

Quick take AI “agents” — autonomous workflows built on large language models that connect to your apps and data — have moved from experiments to real business tools. Companies are using them to handle routine sales tasks, automate reporting, triage support tickets, and generate insights from messy data. That shift matters: it’s where AI stops

Quick summary AI “agents” — autonomous workflows that can take multi-step actions (like outreach, follow-ups, data lookups, and report generation) — are increasingly being embedded in CRM and productivity tools. Major vendors and startups are rolling these agents into sales and operations workflows so teams can automate repetitive tasks and get near-real-time insights without manual