Ron Mitchell

Quick summary AI agents — autonomous, tool-enabled models that can complete multi-step tasks (think: draft an email, pull CRM data, schedule a demo, and update a pipeline) — are no longer just prototypes. Businesses are deploying them for sales outreach, customer support triage, automated reporting, and repetitive back-office work. These agents connect to your apps, […]

Quick summary Major cloud and AI vendors have pushed AI agents from experimental demos into enterprise-ready products. These agents can autonomously fetch data, run reports, trigger workflows, and interact with systems like CRMs and ERPs. The result: organizations are starting to see real savings in time and cost, faster sales cycles, and more up-to-date business

Big picture Enterprise AI is entering a practical phase. Over the last year major platforms and startups have pushed “AI agents” — autonomous assistants that can query systems, summarize, draft, and even take multi-step actions — into real business apps like CRM, calendars, and reporting tools. That shift means AI is no longer just a

Summary A recent wave of practical AI agent deployments is changing how companies handle routine work. Instead of one-off demos, organizations are rolling out autonomous agents that handle tasks like lead qualification, follow-up emails, data entry, and automated reporting. These agents combine conversational AI, workflow automation, and real-time access to company data to act on

Quick summary AI “agents” — autonomous, task-focused AI assistants that can read systems, make decisions, and act — moved out of labs and into real business workflows in 2024–2025. Instead of just answering questions, these agents now qualify leads, update CRMs, triage support tickets, draft follow-ups, and auto-generate recurring reports. They connect to calendars, email,

Quick summary Big vendors and startups are rolling AI agents into everyday business tools — think copilots that finish reports, update CRMs, draft outreach, and automate follow-ups. These agents combine large language models, workflow connectors, and business data to act on behalf of teams (not just suggest text). The result: routine work gets faster, reporting

Short summary AI agents — autonomous software that can read your data, take actions, and talk to customers or staff — have shifted from lab demos to real business use. Over the past 12–18 months companies have started embedding these agents into CRMs, calendars, customer support stacks, and reporting systems so the agents can qualify

Quick summary AI “agents” — software that can act autonomously across apps (qualify leads, pull reports, follow up on tasks) — moved from experiments into real business use over the last 18 months. Major enterprise platforms added agent builders and more companies started deploying agents for sales, operations, and reporting. The shift isn’t about cool

What happened Businesses are rapidly adopting AI agents — software that can carry out multi-step tasks on its own (for example: qualify leads, assemble tailored sales decks, or generate weekly performance reports). Advances in retrieval-augmented generation (RAG), vector databases, and agent frameworks (LangChain / tool orchestration and custom model deployments) have made these systems more

Quick summary AI “agents” — autonomous, multi-step AI assistants that can access systems, follow rules, and complete tasks — are moving out of labs and into everyday business tools. Over the last year major platforms and vendors released agent frameworks and low-code connectors so these agents can safely interact with CRMs, ERPs, calendars, and data