Ron Mitchell

What’s happening Over the past year we’ve moved past demos and chatbots. AI agents — autonomous, workflow-driven bots that link LLMs, company data, and tools — are being deployed in production across sales, customer service, finance, and operations. Companies are using agents to draft personalized outreach, auto-update CRMs, generate weekly reports, manage low-risk procurement, and […]

Quick summary AI “agents” are software programs that can plan, act, and chain together steps with little human prompting. Over the past 18–24 months they’ve moved out of experiments and into real business use — automating things like lead qualification, customer triage, recurring report creation, and routine procurement tasks. Why this matters for business –

Quick summary AI “agents” — software that can act on behalf of users across apps (email, CRM, calendar, chat) — are moving from experiments into real sales workflows. Instead of just suggesting responses, modern agents can qualify leads, draft and send personalized outreach, update records, schedule meetings, and create routine reports. That combination of autonomy

Story summary A clear trend right now: autonomous AI agents—small, task-focused AI assistants that can act across apps—are moving from demos into real business use. These agents can do things like draft and send outreach, schedule meetings, pull and summarize CRM data, or run weekly performance reports without constant human prompting. Improved large language models,

Short summary AI agents — software that can carry out multi-step tasks on behalf of a user — are no longer just prototypes. Over the last two years we’ve seen a burst of practical, enterprise-ready agent tools and libraries (think connector-enabled assistants that can read your CRM, generate reports, and trigger workflows). That shift means

Summary AI “agents” — software that acts on your behalf to complete tasks, pull insights, and automate workflows — have moved from lab experiments to real business tools. Over the past year we’ve seen dozens of vendors and cloud platforms add agent features that connect to CRMs, document stores, calendars, and reporting systems. The result:

Short summary AI “agents” — language models that can act autonomously across apps and systems — went from experiment to enterprise tool over the last 18 months. With tool-use, connectors, and retrieval-augmented generation (RAG), these agents can read your CRM, draft and send personalized outreach, create consolidated reports, and trigger downstream workflows without constant human

Quick summary AI “agents” — software that can carry out multi-step tasks, talk to your apps, and make decisions — are rapidly moving from tech demos into real business workflows. Big vendors (Copilots, enterprise LLM services) and an ecosystem of agent frameworks (LangChain, AutoGen-style tools) have made it easier to build agents that do things

Quick summary – A new wave of no‑code and low‑code “AI agent” platforms (think Copilot builders, agent marketplaces, and connector ecosystems) is making it fast and cheap to create AI assistants that act across apps — CRM, email, calendars, databases, and reporting tools. – These agents can do things like qualify leads, draft personalized outreach,

Brief summary AI “agents” — autonomous, multi-step AI tools that can act on your systems (email, calendar, CRM, databases) — went from proof-of-concept to real business use in 2024. Big vendors and open-source frameworks made it easier to connect models to workflows, and companies are now using agents for lead qualification, scheduling, automated follow-ups, and