Ron Mitchell

Quick summary AI “agents” — autonomous AI that can access tools, read documents, and follow multi‑step instructions — have exploded from research demos into practical business pilots. Frameworks like LangChain and integrations into platforms (workplace apps, CRMs, reporting tools) make it easier to build agents that do real tasks: triage customer requests, assemble weekly sales […]

Summary – Over the past year we’ve moved from talking about AI models to using AI agents: software that can follow multi-step instructions, pull data from your systems, and take actions — like qualifying leads, generating reports, or routing support tickets. – These agents are now easier to build and safer to deploy thanks to

What happened In the past year we’ve seen AI agents — autonomous, connector-ready assistants built on large models — go mainstream. Major platforms made it easier to build agents that read your CRM, pull from internal docs, send emails, update tickets, and generate reports. That means companies can automate repeatable sales and ops tasks end-to-end,

Quick summary – What’s happening: Businesses are increasingly deploying AI agents — autonomous software that can read systems, take actions, and generate reports — to handle tasks like lead qualification, CRM updates, personalized outreach, and routine reporting. – Why it matters: These agents cut manual work, speed decision-making, and improve personalization at scale. That means

Short summary AI agents — autonomous, task-focused assistants built from large language models — have moved from experiments to real-world business work in 2024–25. Companies are using agents to research leads, draft outreach, stitch together data from CRMs and BI tools, and run repeatable processes without manual handoffs. The result: faster decisions, less busywork, and

Big idea: Over the past year we’ve seen AI agents — autonomous assistants that can read, act, and integrate with your systems — move from experiments to real business tools. Vendors (and open-source projects) now make it faster to build agents that qualify leads, automate reporting, reconcile invoices, and trigger workflows without a developer on

What’s happening Autonomous AI “agents” — systems that can take multiple steps, use external tools, and make decisions without constant human prompting — have leapt from proof-of-concept demos into real business use. Teams are now using agents to qualify leads, generate and send proposals, reconcile data across systems, and generate narrative business reports that highlight

Short summary Autonomous AI agents — software that can schedule, research, draft messages, and query systems on their own — have moved from demo projects to real business pilots. Big vendors and open-source frameworks now make it easier to connect agents to CRMs, internal docs, and BI tools. That means companies can automate repetitive sales

Short summary AI “agents” — small, goal-directed AI programs that use tools, memory, and web access — have moved out of labs and into real business apps. Over the past year we’ve seen major vendors (custom GPTs, Copilot-style assistants, tool-enabled LLMs) make it easy to connect language models to calendars, CRMs, databases, and reporting tools.

Quick summary Companies are increasingly using autonomous AI agents—software that can take multi-step actions across apps—to do real work, not just create flashy demos. These agents can research leads, draft outreach, update CRMs, run cross-source analyses, and even generate scheduled performance reports. Paired with retrieval-augmented generation (RAG) and low-code connectors, they’re making business AI more