Ron Mitchell

Short summary AI agents — autonomous, goal-driven systems that can read, act, and coordinate across tools — are no longer just developer experiments. Over the last year businesses have started deploying agent-based workflows for lead qualification, customer follow-ups, inventory checks, and automated reporting. These agents can pull CRM data, draft emails, trigger calendar invites, and […]

Quick summary Major vendors and startups are now shipping task-focused AI agents you can connect to your data and tools. These agents — think automated assistants that can read your CRM, generate reports, draft outreach, or run simple workflows — are no longer just demos. Better language models, retrieval-to-generation (RAG) techniques, and easier integrations mean

Quick summary AI is shifting from single-turn chatbots to autonomous, multi-step “agents” that can connect to your tools, run workflows, and make decisions. Instead of answering a single question, these agents can research prospects, update CRMs, generate and distribute reports, and trigger follow-up actions — all with minimal human oversight. Why this matters for businesses

Short summary AI “agents” — autonomous assistants that combine large language models with task-specific tools and integrations — have moved from lab demos into real business use. Thanks to better models, off-the-shelf agent frameworks, and deeper integrations with CRMs and reporting tools, companies are using agents today for lead enrichment, follow-ups, automated reporting, and routine

Quick summary AI agents — autonomous or semi‑autonomous software that can carry out multi‑step tasks (think: qualify leads, schedule demos, compile weekly sales reports, or triage invoices) — moved from proof‑of‑concepts to real production pilots across 2023–24. Companies are now using agents to run repetitive, rules‑based workflows end‑to‑end, connect to CRMs and ERPs, and generate

Quick summary AI “agents” — small, goal-directed AI programs that chain LLM capabilities with APIs, data stores, and business systems — are moving from labs into real company workflows. New low-code agent builders and integration tools make it easier for non‑engineers to create assistants that fetch data, run checks, draft messages, and trigger actions across

How AI agents are moving from hype to real business impact — and how to start Summary — the story in plain language Autonomous AI agents — small programs that use large language models, company data, and app connectors to complete tasks end-to-end — are rapidly shifting from proof-of-concept to day-to-day business use. Over the

Quick summary AI agents — autonomous, goal-oriented AI that can act across apps and data sources — are moving from tech experiments into real business use. Over the past year we’ve seen more off-the-shelf agent platforms and low-code orchestration tools that let companies automate cross-system workflows: update CRM records, pull finance reports, triage support tickets,

Short summary There’s been a big push this year toward using AI agents — software that can act on your behalf to gather info, run workflows, and even carry out sales tasks. New tools and frameworks (think agent orchestration, retrieval-augmented generation or RAG, and secure data connectors) make it much easier for teams to build

Why this story matters – AI agents — autonomous, task-focused AI that can use tools, access data, and act on behalf of people — have moved from experiments to practical business use. Advances in memory, tool integrations, and safer prompts make them reliable enough for routine work. – For business leaders, that means you can