Ron Mitchell

Summary Over the past year major AI platform vendors made it much easier to build “AI agents” — autonomous, LLM-powered assistants that can read your systems, take actions, and produce human-readable outputs like reports, emails, or sales playbooks. Instead of one-off chatbots, businesses can now deploy agents that join workflows: draft proposals from CRM data, […]

Quick summary AI agents—software that can act on your behalf, carry out multi-step tasks, and talk to other systems—have moved from experiments into everyday business use. Big vendors (and many startups) are shipping agent capabilities that integrate with CRMs, calendars, data warehouses, and messaging tools. That means these systems can now qualify leads, schedule meetings,

Hook: This year saw enterprise-grade AI agents move from experiments into real business tools. They can now work across apps, pull private data, run workflows, and produce automated reports — and that changes how sales and operations get work done. The story in brief – Major cloud and AI vendors have pushed “agent” capabilities into

Quick summary Major cloud providers and AI startups have moved beyond chatbots. Today’s AI agents can connect to your apps, fetch and update CRM records, run reports, and even trigger workflows — with memory, tool use, and API access. That shift makes AI less a single tool and more an operational capability that can automate

What happened (short summary) – In the past year we’ve seen a clear trend: AI agents — task-specific, autonomous assistants built on large language models — are being embedded into real business workflows. – These agents can read your CRM, pull the latest sales metrics, draft outreach messages, schedule follow-ups, and trigger automations across apps

Why AI agents are rising fast — and how your business can capture the gains Summary (the story) In recent months more companies have moved from experimenting with chatbots to deploying autonomous AI agents — software that can carry out multi-step business tasks (book meetings, qualify leads, update CRMs, run reports, even draft proposals) with

Quick summary of the story AI agents — software that can plan, act, and follow up with little human prompting — have moved from demos into real business use. Over the last year we’ve seen more companies deploy agents to handle tasks like scheduling, lead follow-up, data cleanup, and routine reporting. Vendors and in-house teams

Quick summary Autonomous AI agents—software that can plan, act, and follow up on tasks without constant human prompting—are moving from experiments into real business use. Companies are using agents to automate routine sales outreach, generate timely reports, triage customer issues, and coordinate multi-step workflows across systems. That shift is driven by better language models, cheaper

Story summary AI agents — autonomous software that can read, reason, and act across apps — are moving from lab demos into real business use. Companies are now using agents to qualify leads, run follow-up emails, compile weekly reports, and trigger workflows across CRM and finance systems. Advances in large language models, retrieval-augmented generation (RAG),

Story summary In the past year major vendors made it much easier for businesses to build custom AI assistants and agents. Low-code platforms and “assistant builders” from providers like OpenAI, Microsoft and Anthropic let teams create task-specific agents that connect to company data, run workflows, and answer questions in plain language — without hiring a