Ron Mitchell

What happened (short summary) Over the last year we’ve stopped talking about “AI demos” and started seeing AI agents — models that can fetch data, call tools, and act on behalf of users — appear in real business workflows. Vendors from big cloud providers to startups added features like tool calling, retrieval-augmented generation (RAG), and […]

Short summary Big tech and enterprise software vendors are pushing AI agents — configurable “copilots” that can access company data, run tasks, and produce reports on demand. Tools from Microsoft, Google, and Salesforce (plus many startups) now make it practical for teams to build agents that: pull sales and operations data, draft outreach, automate routine

Quick summary AI “agents” — autonomous or semi-autonomous AI programs that can research, write, schedule, and take multi-step actions — moved from demos into real business use in the last year. Teams are now using agents to qualify leads, assemble weekly sales reports, automate follow-ups, and orchestrate cross-system tasks (CRM → calendar → billing) without

Quick summary AI agents — autonomous software that can read your data, take actions, and talk to people and systems — are no longer just developer showpieces. Businesses are using them to automate routine sales tasks, generate real-time reporting, and run follow-up workflows that used to eat up hours each week. Why this matters for

Short summary AI “agents” — software that uses large language models plus tools (APIs, calendars, CRMs, web search, internal docs) to act on your behalf — are moving from demos into real business pilots. Instead of only answering questions, these agents can pull data, update records, generate reports, follow-up with prospects, and hand off work

Quick summary AI “agents” — autonomous assistants built from large language models and workflow tools — are moving from labs into real business work. Instead of a person running every step, an agent can gather sales data, draft outreach, book meetings, and generate weekly performance reports with little manual hand-holding. Recent product releases and a

Quick summary AI agents — software that can act on your behalf across apps and data — have moved from “cool demo” to practical business tools. Over the past year we’ve seen more enterprise-grade agent frameworks, better integrations with CRMs and BI tools, and examples of agents handling tasks like lead follow-up, meeting coordination, and

Quick summary Over the last year, autonomous AI agents — software that can plan, act, and complete multi-step tasks with little human direction — have moved from experiments into real business use. Major cloud and AI vendors released agent frameworks and integrations with common apps (CRMs, calendars, email, analytics). That means companies can now automate

Quick summary AI “agents” — autonomous assistants that can read your data, take actions, and talk to other systems — are no longer just R&D toys. Over the last year, agent-building frameworks and enterprise integrations have matured, letting companies deploy agents that qualify leads, generate regular sales reports, update CRMs, and even automate multi-step customer-service

Quick summary AI “agents” — software that can act autonomously, use tools, and carry out multi-step tasks — have moved out of labs and into real business use. Major cloud vendors and startups now offer low-code agent frameworks and connectors to CRM, ticketing, and analytics systems. That means teams can build virtual assistants that qualify