Ron Mitchell

Short summary AI agents — custom, autonomous assistants built on models like GPT — are moving from experiments to real business tools. Over the last year we’ve seen platforms (custom GPTs, Copilot builders, agent frameworks) make it easier for teams to create agents that read your CRM, generate reports, triage requests, and trigger actions across […]

Quick summary AI “agents” — autonomous, LLM-driven tools that can run workflows, query systems, and take actions — are moving out of research labs and into everyday business tools. Modern agent platforms connect to CRMs, calendars, data warehouses, and reporting tools, and they can be configured with low- or no-code interfaces and human-in-the-loop guardrails. Why

Quick summary AI agents — autonomous, goal-driven AI processes that can read, act, and coordinate across apps — are no longer just R&D demos. Over the past year we’ve seen businesses move from one-off pilots to production use: agents that triage leads, generate and distribute sales reports, draft follow-ups, and orchestrate multi-step workflows across CRM,

Summary AI “agents” — autonomous, goal-driven software that can read, decide, act, and report — are moving from experiments into real business use. Over the last year we’ve seen low-code agent platforms, better integrations with CRMs and data warehouses, and practical guardrails that make agents safer for day-to-day work. Instead of single prompts, these agents

What happened – Over the last year more companies have moved from one-off AI pilots to deploying AI agents: automated workflows that think, fetch, and act across systems (example toolkits: Auto‑GPT, LangChain and similar agent frameworks). – These agents can do things like qualify leads, draft personalized outreach, update CRMs, and generate up-to-date sales and

Why AI agents are moving from pilot to profit — what business leaders need to know The story (short) Over the last 12–18 months, a clear shift has accelerated: AI agents — systems that can carry out multi-step tasks across apps with little human hand-holding — are moving out of proof-of-concept labs and into real

AI agents move into the enterprise — what this means for sales, reporting, and operations Summary of the story Over the past year we’ve seen AI agents — autonomous, workflow-focused AI that can take actions, fetch data, and coordinate systems — move from lab demos to real business pilots. Major AI platforms and toolmakers now

Quick summary AI agents—autonomous, task-focused AI assistants that can read your data, run processes, and take actions—are moving from labs into real business use. Instead of one-off chat responses, agents can handle workflows like qualifying leads, generating weekly sales reports, routing customer issues, or triggering follow-up tasks across systems. Why this matters for business –

Summary In recent months we’ve seen a clear shift: AI agents — autonomous, task-focused AI that can act across systems — are moving from experiments into real business use. Companies are deploying agents to qualify leads, automate routine sales follow-ups, generate up-to-date reports, and keep workflows moving without constant human handoffs. That means faster cycles,

Hook / quick summary AI agents — purpose-built, multi-step AI assistants that act on your behalf — are no longer just demos. Over the past year we’ve seen agent frameworks, function-calling APIs, and better data retrieval tools make reliable, task-oriented agents practical for real business use: automating follow-ups, updating CRMs, producing routine reports, and executing