Ron Mitchell

Quick summary In the last year we’ve seen a major shift: “AI agents” — systems that autonomously run multi-step workflows (fetch data, analyze it, take actions, and follow up) — are no longer just demos. Toolkits and platforms from major vendors and open‑source communities make it practical to build agents that connect to your CRM, […]

Story summary AI “agents” — configurable AI assistants that can browse, call APIs, read your documents, and take actions — are moving from labs into real business workflows. Platforms and marketplaces now make it easy to build custom agents that connect to CRMs, calendars, analytics tools, and internal docs. Companies are using these agents to

Story summary Autonomous AI agents — software that can take actions, run multi-step tasks, and make decisions with minimal human oversight — moved from labs into real business pilots in 2023–24. Tools and frameworks such as AutoGPT-style agents, LangChain workflows, and vendor “copilot” offerings made it easier to automate tasks like prospect research, email outreach,

How GPT‑4o and modern AI agents are unlocking faster automation and better business reporting Short summary (the story) In mid‑2024 OpenAI released GPT‑4o — a faster, more multimodal model built to work smoothly with external tools, real‑time data, and agent frameworks. That release (and the parallel growth of agent toolkits like LangChain and embedding-based retrieval)

Summary AI agents — autonomous systems that combine large language models, retrieval (RAG), and connectors to apps like CRM and BI tools — have moved from demos into real business use. Companies are now using agents to research prospects, draft and send personalized outreach, prepare meeting briefs, and generate narrative reports from dashboards. The result:

Quick summary Major AI vendors and startups are pushing autonomous “AI agents” — software that can plan, act, and follow up across tools (email, CRM, calendars, reporting). What used to be experimental (AutoGPT-style demos) is now becoming productized: agents that qualify leads, schedule demos, generate weekly sales reports, and run routine automations without constant human

Story summary AI agents — autonomous, multi-step AI assistants that can research leads, draft outreach, update CRMs, and generate executive reports — have moved from lab demos into real pilots at commercial organizations. Rather than a single reply to a prompt, these agents string tasks together, call external tools (email, calendar, CRM), and produce end-to-end

Quick summary AI “agents” — autonomous tools that can run tasks end-to-end (research leads, book meetings, draft reports, triage support tickets) — have moved out of labs and into real business workflows. Instead of one-off prompts, companies are stitching agents into CRMs, knowledge bases, and analytics pipelines so the agent can act on live data,

Short summary AI “agents” — autonomous, task-focused software that can read your systems, draft messages, run follow-ups, and generate reports — moved from experiments into real business use in the last 12–18 months. Enterprises and fast-moving SMBs are using agents to automate repetitive sales work, enrich CRM records, create on-demand performance reports, and trigger cross-team

Quick summary AI agents — autonomous, task-focused tools that can research, draft, and act inside your systems — are shifting from R&D experiments into real business use. Firms are using agents to qualify leads, draft personalized outreach, update CRMs, and assemble sales and executive reports automatically. That means faster sales cycles, fewer manual errors, and