Ron Mitchell

Summary AI “agents” — autonomous workflows powered by large language models — have moved from demos into real business pilots. Instead of a human typing prompts, agents can monitor data, run multi-step processes, and act on your behalf: scheduling follow-ups, creating tailored sales outreach, reconciling expense reports, and assembling regular KPI dashboards. Why this matters […]

Quick summary – What’s happening: Autonomous AI agents — systems that can take multi-step actions, talk to apps, and make decisions with minimal human prompting — are moving out of demos and into real sales and operations workflows. Platform providers and open-source toolkits now make it easier to connect agents to CRMs, email, calendars, and

Summary There’s a clear shift happening: autonomous AI agents — systems that can take multi-step actions, connect to apps, and carry out tasks with little human oversight — are moving out of labs and into real business workflows. Vendors and platform builders now offer agent-capable connectors for CRMs, calendars, BI tools, and ERPs, so these

Why this matters – Over the last 18–24 months we’ve seen a rapid shift: AI agents — software that can read, decide, and act across apps — are moving from lab demos to real work. Platforms from major cloud vendors, LLM toolkits, and low-code automation tools now make it practical to build agents that run

Summary AI “agents” — autonomous software that can plan, act, and complete multi-step tasks — are moving from experiments into everyday business use. Tools that started as hobby projects (Auto-GPT, BabyAGI) have inspired enterprise versions from major cloud and SaaS vendors. Companies are already using agents to triage support tickets, enrich CRM records, generate first-draft

Quick summary Major cloud vendors and open‑source frameworks have made it much easier to build AI agents — low‑code/no‑code tools now let non‑engineers assemble assistants that connect to CRMs, calendars, databases, and reporting systems. That means businesses can automate recurring tasks (lead qualification, follow‑up, proposal drafting), generate real‑time reports, and run intelligent workflows without long

Quick summary AI “agents” are autonomous, task-focused AI programs that can act across apps and data sources — not just answer chat prompts. Think of an agent that qualifies leads in your CRM, drafts personalized outreach, schedules follow-ups, and updates pipeline stages — all with little human hand-holding. Why this matters for business – Faster,

Quick summary AI “agents” — autonomous workflows that use large models, retrieval systems, and APIs to complete multi-step tasks — moved from demos to real pilots in 2023–24. Tools and frameworks (e.g., LangChain-style agent patterns, RAG databases, and commercial copilots) made it practical to automate end-to-end processes like sales outreach, contract drafting, and operational reporting.

Quick summary AI “agents” — autonomous workflows powered by large language models — moved from demos into real business use in 2023–2024. These agents can research, draft outreach, pull data from your systems, and complete multi-step tasks without constant human direction. That makes them useful for sales, operations, customer service, and reporting. Why this matters

Short summary AI “agents” — software that can carry out multi-step tasks, pull from your data, and act with minimal human prompts — are no longer just lab experiments. Improvements in large language models, retrieval-augmented generation (RAG), and orchestration frameworks have made it practical for companies to deploy agents for real work: qualifying leads, auto-generating