Ron Mitchell

What happened – Over the past year we’ve moved from demos to real deployments of autonomous AI agents — small, purpose-built systems that can read your data, call APIs, take actions, and keep working without a human in every step. – Advances like reliable tool‑use (function calling), retrieval‑augmented generation (RAG), and orchestration frameworks (LangChain-type stacks) […]

Quick summary AI “agents” — autonomous, task-focused AI that can read, act, and iterate — are rapidly moving out of labs and into sales, operations, and finance teams. Instead of one-off chat responses, companies are now using agents to qualify leads, draft proposals, reconcile reports, and trigger actions across CRMs and BI tools. Vendors have

Story summary There’s a clear surge in AI agents — purpose-built assistants that combine large language models with tool access (calendars, CRMs, databases, web tools) to act autonomously on tasks. Over the past 12–18 months major cloud and software vendors and startups have released agent-building platforms, and more companies are running pilots that let an

Quick summary Autonomous AI agents — software that can take an instruction and carry out multi-step tasks with little human intervention — are moving fast from labs into real business use. Think agents that research prospects, draft personalized outreach, schedule meetings, and update your CRM without manual handoffs. Vendors (cloud providers, CRM platforms, and startups)

Quick summary AI “agents” — software that acts autonomously across apps and data — have moved from experiments into real business use. Instead of a person copying data between systems or running the same set of manual checks, an agent can research a lead, draft outreach, open calendar slots, update your CRM, and generate the

Quick summary There’s a clear surge in interest around autonomous AI agents — systems that can carry out multi-step tasks (research, draft, update systems) with minimal human direction. Companies are combining these agents with retrieval-augmented generation (RAG) and vector databases so the agents can work directly from a company’s documents, CRM records, and reports. The

Short summary AI agents — software that can plan, act, and complete multi-step tasks on behalf of a user — have moved from demos into real business pilots. Over the past year we’ve seen frameworks (like LangChain) and major cloud vendors add agent capabilities, and companies are now using them for things like lead qualification,

Hook: AI agents — autonomous, conversational tools that can research, act, and follow up — are no longer a novelty. Businesses are moving beyond pilots and using agents to boost sales outreach, automate reporting, and speed routine operations. What’s happening (quick summary) – AI agents combine large language models with tools, data access, and decision

Quick story AI agents — autonomous software that can act across email, CRM, calendars and reporting tools — are no longer just demos. Over the past year many vendors and early adopters have moved from proof-of-concept pilots to production agents that can qualify leads, draft personalized outreach, book meetings, and auto-generate sales reports. The result:

Summary AI agents — autonomous, LLM-powered assistants that can perform multi-step tasks — are moving fast from lab experiments into real business use. Companies are now deploying agents that read emails, qualify leads, update CRMs, schedule meetings, generate reports, and trigger downstream workflows without constant human prompts. Why this matters for business – Faster response: