Ron Mitchell

The story (short) Over the last 12–18 months we’ve moved from curious demos to real-world AI agents that can complete multistep tasks: read your CRM, draft outreach, schedule meetings, fetch data, and assemble executive reports. Agent frameworks and retrieval-augmented generation (RAG) have made these systems faster to build and more reliable. That means teams can […]

Quick summary AI agents — think copilots that can read your emails, check your CRM, run reports, and take actions across apps — have moved from proof-of-concept to real deployments. Major vendors and enterprise teams are embedding these agents into sales, customer service, and operations so they can automate routine work, surface opportunities, and generate

Quick summary AI “agents” — autonomous assistants that combine large language models (LLMs) with tools, data retrieval, and simple decision logic — are moving from demos into real work. Think of an agent that reads your CRM, triages new leads, drafts follow-up emails, schedules meetings, and updates records — with minimal human input. Major platforms

Quick summary Over the last year major vendors and startups have pushed “copilot” and autonomous agent products into enterprise tools. These AI agents connect to your CRM, calendars, knowledge bases, and reporting systems to do real work: qualify leads, draft follow-ups, generate weekly sales reports, and run routine workflows that used to need a human

Quick summary Autonomous AI agents — large language models connected to tools, data, and workflows — are no longer just lab experiments. Over the last year we’ve seen these agents embedded in BI tools, CRMs, and help desks so they can find data, draft actions, and even trigger processes. That combination of natural-language reasoning +

Summary AI “agents” — autonomous software that chains tasks, talks to apps, and makes decisions — moved from demos into real work in 2024–25. Instead of a person copy-pasting between tools, an agent can assemble lead lists, run outreach sequences, update CRM records, and generate weekly pipeline reports automatically. That shift makes business AI less

Summary A recent wave of practical AI agents is moving from labs into everyday business tools. These agents combine large language models with automation and secure access to company data to do tasks like qualifying leads, drafting proposals, generating sales reports, and automating routine workflows. Instead of just producing text, they take action — pulling

Quick summary AI agents — autonomous or semi‑autonomous assistants built on modern LLMs — moved from experiments to production in 2024. They’re no longer just chatbots: today’s agents can connect to CRMs, databases, calendars and BI tools, run multi‑step workflows, monitor metrics, and even generate scheduled reports or outreach sequences without constant human guidance. Why

Quick summary AI “agents” — autonomous models that can read, act, and chain tasks across apps — are moving from demos into day‑to‑day business use. Instead of only generating text, modern agents can call APIs, pull CRM data, create invoices, schedule meetings, and produce consolidated reports with little human hand‑holding. That shift is making automation

Quick take AI agents — tools that can act on your behalf (query databases, draft emails, update CRMs, generate reports) — have moved from flashy prototypes to real, production use across sales and operations. Major vendors and open-source toolkits now make it easier to build agents that connect to your systems and run workflows autonomously.