Ron Mitchell

Quick summary AI “agents” — autonomous workflows built on large language models that can read your data, take actions, and talk to apps — are no longer just demos. Over the past year, more companies have moved agent-based solutions into live use for tasks like lead qualification, proposal drafting, invoice reconciliation, and automated monthly reporting. […]

Summary AI agents — software that can act autonomously to complete tasks — are no longer just lab experiments. Across industries, companies are piloting agents that follow up on leads, generate weekly sales reports, and automate routine approvals. These agents combine large language models with workflows, connectors to CRMs/ERPs, and retrieval-augmented generation (RAG) so they

Short summary AI agents — autonomous, multi-step AI assistants that can read documents, take actions in apps, and follow business rules — have moved from demos into real business use. Over the past year we’ve seen platforms and prebuilt agent templates make it much easier to connect agents to CRMs, reporting systems, and automation tools.

Quick summary AI agents — autonomous or semi-autonomous software that can read data, call APIs, and act across apps — have moved from proof-of-concept to business-ready tools. Organizations are using agents to qualify leads, generate personalized outreach, automate multi-step workflows, and produce up-to-date management reporting without manual data wrangling. Why this matters for business leaders

Quick summary AI “agents” — software that can take multi-step actions on behalf of a person — moved from hobby projects into real business deployments in 2024. Companies are using them to gather data, run routine workflows, draft customer outreach, and produce recurring reports. The result: faster decisions, fewer manual steps, and lower operating costs.

Quick summary AI agents — intelligent assistants that can act across apps, pull data, and complete multi-step tasks — are moving from demos into real business use. Companies are already using agents to qualify leads, update CRMs, generate sales reports, and automate repetitive customer tasks. That shift matters because it turns AI from a tool

Quick summary There’s a clear shift this year: AI “agents” — autonomous, multi-step assistants built on large language models — are no longer just lab experiments. Improvements in model reliability, low-code agent builders, and ready-made integrations with CRMs, BI tools, and ticketing systems mean teams can automate entire workflows (not just single tasks). That makes

Quick summary AI “agents” — autonomous assistants that combine large language models with real-time data, workflows, and APIs — are moving from experiments into everyday business tools. Vendors and platforms are embedding agents into productivity suites and CRM systems, and low-code builders make it easier for teams to create task-specific agents (sales outreach, invoice triage,

Quick summary AI agents — autonomous systems that can read, plan, act, and learn across apps — are no longer just experiments. Companies are using them to run parts of sales outreach, update real-time dashboards, triage customer requests, and automate routine finance tasks. The result: faster decisions, fewer manual steps, and measurable time and cost

Quick summary AI “agents” — models that can take multi-step actions across apps (CRM, calendar, email, databases) — have moved from demos into real business pilots. New no-code platforms plus better grounding (retrieval-augmented generation), tool integrations, and monitoring make it possible to give agents limited autonomy for tasks like lead qualification, follow-up sequences, and automated