Ron Mitchell

Summary AI agents—software that plans, acts, and connects across apps—have moved from demos into real business use. Lately we’ve seen companies automate routine workflows (lead qualification, scheduling, follow-ups), generate recurring reports, and let agents surface insights from messy data across CRMs, spreadsheets, and chat logs. The result: faster decisions, fewer manual tasks, and clearer visibility […]

Summary A new wave of generative AI “agents” — multi-step, tool-enabled systems that can read your data, take actions, and learn from past interactions — is moving from labs into everyday business work. Built with agent frameworks (like LangChain and similar toolkits) and offered by cloud vendors as “agents-as-a-service,” these systems can draft proposals, run

Short summary AI agents — small, task-focused systems built on large language and multimodal models — are rapidly moving from lab experiments into real business use. Instead of a person copy-pasting prompts, agents can fetch data, run queries, generate summaries or outreach, and take follow-up actions across apps. That shift makes AI useful for end-to-end

Quick summary AI “agents” — autonomous workflows that combine large language models, connectors, and simple logic — moved from experiments into real business tools over the past year. Companies are using agents to draft personalized outreach, run daily sales and ops reports, triage customer issues, and automate routine approvals. The result: faster decisions, fewer manual

Quick summary AI agents — autonomous, task-focused AI that can interact with systems, people, and data — are moving from experiments into everyday business use. Major vendors are embedding agents into CRMs, productivity suites, and analytics tools, and more companies are applying agents to routine workflows: lead qualification, customer follow-ups, scheduling, invoice reconciliation, and recurring

Quick summary AI “agents” — software that can plan and execute multi-step tasks on its own — have gone from research demos to real business pilots. Companies are using them to draft outreach, triage support tickets, compile and distribute reports, and automate repetitive back-office work. The result: faster responses, fewer manual hand-offs, and clearer, more

Quick summary AI agents — software that can act on your behalf (qualify leads, book meetings, pull and summarize data) — are no longer just experiments. More companies are rolling them into production to automate repeatable tasks, deliver real-time reporting, and keep sales teams focused on high-value work. That shift is turning AI from a

Quick summary AI “agents” — autonomous, goal-driven AI tools that can use apps, call APIs, and loop until a task is done — went from research demos to practical business pilots in 2024–25. Major vendors and toolkits (custom GPTs, plugin ecosystems, and agent frameworks) made it easier to build agents that handle routine sales tasks,

The story in short – In 2024–25 we’ve seen a clear shift: AI agents — autonomous, connector-enabled assistants that can read, act, and follow up across apps — are leaving lab pilots and being used in production for sales, operations, and reporting. – Advances that enabled this shift: better tool use (APIs and connectors), retrieval-augmented

Short summary Recent months have seen a clear shift: AI agents — autonomous, task-focused AI that can act across apps and data — are no longer just research demos. Businesses are deploying agents to handle routine sales work (prospecting, outreach follow-ups, scheduling), automate recurring operational tasks, and generate near-real-time reports. That means faster response times,