Ron Mitchell

Quick summary There’s a clear shift from one-off AI tools to intelligent, task-focused AI agents and enterprise copilots that can act on behalf of teams — run queries, draft emails, update CRMs, and generate reports. These agents combine large language models with connectors (to CRM, ERP, databases) and simple workflows so they can carry out […]

Quick summary Autonomous AI agents — software that can plan, act, and follow up across tools — have moved from demos to real business pilots. Companies are using agents to handle routine workflows: qualifying leads, triaging support tickets, extracting data from invoices, and generating recurring reports. The result: faster processes, fewer human bottlenecks, and clearer

Quick summary Major AI vendors and startups are pushing production-ready “AI agents” — small, goal-directed AI systems that can take multi-step actions, connect to apps, and run workflows on your behalf. Instead of a person asking a single question and getting a reply, agents can gather data, cross-check sources, update systems (CRM, spreadsheets, BI tools),

Big picture AI agents — autonomous, task-focused workflows powered by large language models — are no longer an experimental toy. Over the last year we’ve seen no-code agent builders, tighter CRM integrations, and improved retrieval (RAG) for company data that make it realistic for businesses to automate entire sales and operations tasks: lead triage, personalized

What’s happening Over the past year AI “agents” — think custom GPTs, agent frameworks and low-code automations that can act on your behalf — have moved from tech demos into real business workflows. These agents can read your CRM, generate and send outreach, pull together weekly sales reports, open tickets, and even trigger follow-up actions

Short summary AI agents — software that can plan, act, and follow up without constant human direction — have moved from research demos to real business pilots. Thanks to better connectors (CRMs, email, calendars), cheaper compute, and safer guardrails, teams are using agents to automate sales outreach, speed customer responses, and generate routine reports. Why

Short summary AI agents — autonomous, task-focused AI assistants that can read, act, and follow up across apps — are moving from experiments into production at sales and operations teams. Instead of asking reps to copy-and-paste between tools, agents can draft personalized outreach, qualify leads, update CRMs, summarize calls, and generate pipeline reports automatically. Why

Short summary AI agents — autonomous, multi-step AI assistants that can access systems, run workflows, and produce reports — moved from research demos into real business use over the past 18 months. Improvements in grounding (retrieval-augmented generation), agent orchestration platforms, and integrations with CRMs and data warehouses mean these agents are no longer just “cool

Quick summary Over the last 18 months we’ve moved from impressive demos to real deployments: autonomous AI “agents” that can read your CRM, pull data, draft emails, run follow-up sequences, and assemble reports are now being built into enterprise workflows. Vendors and open-source toolkits (think agent orchestration + retrieval-augmented generation) let these agents connect to

Short summary AI “agents” — autonomous tools that can read, act, and interact across apps — have moved from experiments to real business pilots. Over the last 12–18 months vendors and cloud providers have made it easier to hook agents into CRMs, calendars, docs, and reporting systems. That means AI can now do more than