Ron Mitchell

Story summary AI agents — software that can take multi-step actions, talk to systems, and run tasks with little human prompting — moved from demos to day-to-day business use in 2023–24. Tooling like LangChain-style agent frameworks, no-code automation platforms adding AI, and enterprise assistants from major cloud vendors made it easier to connect agents to […]

Big story in plain terms This year, enterprise-grade AI agents moved from demos into real business tools. Vendors and startups are shipping agents that can securely connect to calendars, CRMs, ERPs and databases, carry out multi-step tasks (like scheduling, quoting, or closing simple support tickets), and produce automated reports that pull from live systems. In

Story pick (quick context) Over the past year, a new wave of “agentic” AI tools has moved from demos into real business use. These AI agents can read and write across multiple apps — sending outreach, updating CRM records, running queries, and producing reports — without a human clicking each step. Big vendors and startups

The story (short) Autonomous AI agents — software that can plan, act, and follow up on tasks with little human direction — moved from research demos into practical business pilots over the last year. Frameworks and toolkits (the “agent” pattern, LangChain-style stacks, RAG for knowledge access, and orchestration platforms) made it realistic for teams to

Quick summary AI “agents” — purpose-built AI assistants that can access your systems, pull documents, run processes, and act on behalf of users — moved from experiments to real business projects in 2024–25. Companies are combining retrieval-augmented generation (RAG), vector databases, and smaller/private models to build agents that do tasks like generating sales outreach, preparing

AI story (short): Over the last 12–18 months, “AI agents” — autonomous, multi-step AI assistants that can access business systems, run workflows, and act on behalf of users — have moved from demos into real-world pilots. Major platform vendors and open-source frameworks now make it easier to build agents that connect to CRMs, calendars, knowledge

Summary AI “agents” and built-in copilots are no longer experimental tech — they’re showing up inside everyday business apps (CRMs, analytics, workflow tools) and in no-code agent builders. That means companies can now automate complex tasks end-to-end: generate sales outreach, run recurring performance reports, triage support tickets, and trigger follow-up workflows without manual handoffs. Why

Short summary AI “agents” — LLM-powered bots that can use tools, access systems, and complete tasks end-to-end — have moved beyond lab experiments into real business deployments. Modern agent frameworks connect language models to CRMs, ticketing systems, document stores, and reporting tools so a single agent can qualify leads, create and update CRM entries, generate

Quick summary AI agents—autonomous, task-focused AI programs that can read data, take actions, and carry on multi-step workflows—are moving from pilot projects into everyday business use. Companies are using agents to automate routine work (think lead qualification, reporting, and customer follow-ups), speed up decision-making, and deliver more personalized customer experiences without hiring large teams. Why

Brief summary Over the past year major AI providers and enterprise toolmakers pushed “AI agents” — autonomous, task-focused models that can act, fetch data, run workflows, and talk to your systems. Put simply: these agents don’t only answer prompts. They can look up CRM records, run queries against internal docs, trigger automations, draft emails, and