Ron Mitchell

Quick summary AI agents — autonomous, tool-using AI that can carry out multi-step tasks — have gone from research demos to practical tools companies can use today. Instead of a person copy‑pasting between apps, an AI agent can read your CRM, pull the right data, draft a customer follow-up, and schedule it — or generate […]

Short summary In the past year we’ve seen a rapid shift from isolated AI tools to purpose-built AI agents: autonomous, multi-step systems that can read your CRM, pull data from finance systems, draft customized outreach, and produce polished reports — all with little human hand-holding. For business leaders, that means repetitive sales and ops work

Short summary AI “agents” — autonomous workflows that combine language models, tools, and company data — are moving from labs into real business use. Instead of one-off chat answers, agents can run multi-step tasks: pull sales data, draft personalized outreach, update your CRM, and generate a weekly performance report — all without constant human hand-holding.

Quick summary AI “agents” — autonomous assistants that can access your tools, run tasks, and generate answers — moved from labs to day-to-day business in 2024 and are accelerating now. Major AI platforms let companies build custom agents that connect to CRMs, calendars, databases and reporting tools. That means routine work like lead qualification, monthly

Story summary AI agents — software that plans, fetches data, runs tools, and carries out tasks autonomously — have moved out of demos and into real business workflows. Over the last 18–24 months we’ve seen vendors and platforms (from cloud giants to niche startups) combine large language models with retrieval, tool integration, and workflow orchestration.

Quick story Over the last year vendors and startups have moved from demos to deployable products: platforms now let companies build custom AI agents that connect to your CRM, calendar, files, and reporting tools. These agents can draft outreach, enrich leads automatically, summarize meetings, and generate recurring reports — with much less engineering work than

Summary AI “agents” — software that combines large language models with tool access (CRMs, databases, APIs) to act on behalf of users — have crossed a tipping point. What used to be experimentation (single-chat prompts and isolated automations) is now reliable, integrable, and measurable. Vendors and open-source frameworks have made it easier to connect agents

Quick summary AI agents — autonomous, task-focused AI that can act across apps — are no longer an experiment. Companies are using agents to qualify leads, personalize outreach, update CRMs, and generate near-real-time reports that used to take analysts hours or days. These agents combine large language models with connectors, retrieval-augmented generation (RAG), and simple

Summary Autonomous AI agents — software that can take multi-step actions, call tools, and carry out end-to-end tasks — have moved beyond lab demos into real business use. Companies are using agents to qualify leads, automate repetitive sales and support workflows, prepare reports, and trigger downstream systems without constant human hand-holding. Why this matters for

Quick summary AI agents — software that can take multi-step actions on your behalf (think: qualify a lead, update your CRM, book a demo, and send a follow-up) — are moving from labs into real business workflows. Over the past year more sales and operations teams have begun piloting agents to handle routine tasks that