Ron Mitchell

Quick summary AI “agents” — task-focused AI that can act, fetch data, and update systems — have shifted from experiments to practical pilots across industries. Over the last year many vendors and startups have released agent-building tools and companies are using them to automate routine work: qualifying leads, triaging support tickets, generating regular reports, and […]

Big picture: Autonomous AI agents — small software workers that read your systems, take actions, and talk to humans — are moving out of labs into real business use. Sales teams are using agents to qualify leads, update CRMs, and personalize outreach. Operations teams are using agents to trigger workflows, reconcile data, and generate regular

Quick summary AI “agents” — custom, autonomous assistants that can read your data, act across apps, and follow business rules — have moved from experiments into real deployments. Companies today are using agents to do things like: generate weekly sales reports from multiple systems, run lead qualification and follow-up sequences, and automate repetitive back-office tasks

Quick summary AI agents—autonomous, task-focused systems that can read data, take actions, and coordinate across apps—are no longer just proofs-of-concept. Over the last 18–24 months we’ve seen vendors and enterprises embed agent-style assistants into CRMs, finance systems, customer support, and reporting pipelines. The result: faster task completion, automated status updates and reports, and fewer repetitive

Quick summary AI “agents” — autonomous or semi‑autonomous systems that can research, act on, and summarize information — are moving from lab demos to everyday business use. Companies are now using agents to qualify leads, create real‑time sales reports, summarize customer calls, and trigger routine workflows across CRM, email, and BI tools. Why this matters

Short summary AI “agents” — autonomous, multi-step AI assistants that can read your systems, take actions, and follow up — have moved quickly from demos into real business use. Major vendors (Copilot-style features, Salesforce Einstein GPT, Google’s business AI tools) plus open-source agent frameworks and vector search have made it practical to connect AI to

Summary AI “agents” — autonomous systems that can perform multi-step tasks, talk to your tools, and act on behalf of users — are no longer experimental. Over the last year we’ve seen more off‑the‑shelf agent frameworks (LangChain-style agents, platform-built copilots) and enterprise integrations that let these systems access CRMs, ticketing, calendar, and BI tools. For

Short summary AI agents — software that can perform multi-step tasks with little human direction — have moved from experiment to practical tool. Companies are now using agents to qualify leads, draft and deliver routine reports, triage customer issues, and automate repetitive back-office work. These agents combine large language models with connectors (to CRMs, databases,

Summary AI “agents” — autonomous systems that read your data, take actions, and talk to other software — are no longer just experiments. Advances in large language models, retrieval-augmented generation (RAG), and agent frameworks (the glue that connects models to your CRM, email, calendar and databases) are letting businesses automate multi-step workflows: qualify leads, schedule

Quick summary This June Apple introduced “Apple Intelligence” — a system that brings generative AI features across iPhone, iPad and Mac with a strong emphasis on on-device processing, deep app integration, and privacy controls. Rather than only depending on cloud-only models, Apple’s approach lets devices do more locally while still connecting to cloud services when