Ron Mitchell

Short summary AI “agents” — customizable, task-focused AI assistants — moved from experiment to practical tool over the last year. Big model providers and marketplaces now let teams build or buy agents that connect to company data (CRMs, help desks, analytics) and run multi-step tasks like qualifying leads, drafting proposals, generating weekly reports, or opening […]

Quick take A new wave of “autonomous” AI agents is moving from labs into real business workflows. These agents can take multi-step actions—like qualifying leads, scheduling follow-ups, updating CRM records, and creating summary reports—without constant human prompts. For companies focused on sales and operations, that means routine work gets faster, teams stay focused on high-value

Quick summary AI “agents” — autonomous workflows powered by large language models and tool integrations — have moved from research demos into real business tools. Frameworks like LangChain and agent-style features in major platforms let models call APIs, read databases, schedule actions, and generate reports with minimal engineering overhead. Teams are already using agents for

Quick summary AI “agents” — autonomous assistants that can read your data, take actions, and run multi-step workflows — are moving from research demos to real business tools. Companies are now combining private LLMs with CRM, ERP, BI, and helpdesk systems so agents can answer questions, create reports, update records, and even trigger approvals with

Quick summary Companies are no longer treating AI as a series of one-off experiments. Over the last 12–18 months we’ve seen a clear shift: autonomous AI agents and copilots are being embedded into real workflows (sales, customer support, finance) and connected to CRMs, internal data, and automation systems. That means these tools are now driving

Quick summary AI agents — software that can act autonomously across systems (think: triage support tickets, enrich leads in your CRM, or generate weekly sales reports) — are no longer just demos. In 2025 we’re seeing companies move agents from experiments into everyday operations. Improvements in retrieval-augmented generation (RAG), cheaper fine-tuning, and safer deployment options

Quick summary Autonomous AI agents — software that can act on behalf of people (qualify leads, send follow-ups, update CRMs, generate reports) — have moved from experiments into real, measurable business use. Forward-looking teams are using these agents to automate repetitive sales tasks, keep pipelines clean, and generate near-real-time, natural-language reports that non-technical leaders can

Quick summary Companies are moving beyond one-off AI tools and adopting autonomous AI agents — systems that combine large language models, retrieval-augmented knowledge, and task automation to do real work: research prospects, update CRMs, generate weekly reports, and trigger follow-ups. This shift is happening now because agents can connect to business data, act across apps,

Quick summary – Over the past year, a new wave of autonomous AI agents — tools that can perform multi-step tasks, talk to systems, and follow simple business rules — moved out of labs and into real company pilots. – These agents can do things like research leads, draft outreach, update CRMs, generate recurring reports,

Quick summary AI “agents” — autonomous, multi-step AI workflows that can read systems, take actions, and follow up — have started moving out of tech demos and into real business use. Companies are using agents to triage leads, draft and send personalized follow-ups, update CRMs, and generate recurring performance reports without a human doing every