Ron Mitchell

Quick summary AI “agents” — autonomous assistants that can read your systems, run tasks, and act on their own — have moved from demos into real enterprise use. Over the last year vendors and startups have launched agent platforms that connect generative models to CRMs, ERPs, calendars, and reporting tools. That means an AI can […]

Summary AI “agents” — autonomous AI that can talk to systems, run multi-step processes, and make decisions — have crossed an important threshold. What was once a lab curiosity is now popping up in mainstream business tools: generative AI embedded in CRMs, finance systems, and BI platforms; plug-and-play agents that can book meetings, aggregate data,

Summary AI agents — small, goal-directed systems that combine large language models with connectors, rules, and automation — have moved from demos to real-world use. Instead of just answering questions, modern agents can pull CRM data, run queries in your BI tool, create and send emails, and trigger workflows. That means one “assistant” can qualify

What’s happening In recent months we’ve moved past “chat demos” to practical AI agents that act on behalf of employees — pulling from your systems, summarizing context, and performing tasks like follow-ups, report generation, or scheduling. The key enabler is retrieval-augmented generation (RAG): agents query your internal data (CRM, spreadsheets, docs), then generate accurate, context-aware

Quick summary Companies are moving from single-purpose AI tools to AI agents — systems that can carry out multi-step workflows (triage leads, update CRMs, draft outreach, and generate reports) with minimal human handoff. Vendors and startups are packaging these agents as low-code platforms you can connect to internal systems, and early adopters are already seeing

Quick summary Companies are increasingly piloting autonomous AI agents that connect to email, calendars, CRMs and internal databases to do real work: qualify leads, summarize meetings, update records, generate reports, and trigger routine actions. Low-code agent builders, better retrieval-augmented models, and enterprise controls have made these tools practical beyond R&D labs — and they’re moving

Quick summary AI agents — software that can act on behalf of users, connect to systems, and complete multi-step tasks — have moved fast from R&D demos into everyday business tools. Companies are using agent-style assistants to qualify leads, generate and distribute sales reports, automate order and billing workflows, and summarize customer interactions. That shift

Quick snapshot AI is moving beyond chat: autonomous AI agents now perform multi-step tasks—pulling data from your CRM, generating a tailored sales outreach, booking meetings, and updating records without a human in every step. This shift is practical (not just flashy): businesses are using agents to speed workflows, reduce manual errors, and produce better, real-time

Quick summary AI agents — software that can act autonomously across systems, fetch and synthesize data, and carry out tasks — are no longer just research demos. Over the last year we’ve seen steady shifts: enterprise copilots, integrations (think plugin-style connectors), and retrieval-augmented generation (RAG) are making AI agents reliable enough for real business work

Quick summary Autonomous AI agents — small, goal-directed software that can plan, execute, and iterate on tasks without constant human prompting — have moved from labs into real business tools. Over the last 12–18 months we’ve seen major AI platforms add agent frameworks and companies build “agent orchestration” tools that combine multiple models, data sources,