Ron Mitchell

Quick summary AI agents — autonomous, task-focused AI programs that can act, decide, and connect systems — are no longer just R&D demos. Companies are increasingly using them for real business work: qualifying leads, routing customer requests, generating recurring reports, and automating follow-ups. Instead of one-off chat interactions, agents can orchestrate multiple systems (CRM, calendar, […]

Why this matters right now AI “agents” — autonomous or semi-autonomous assistants built on large language models — have moved beyond proofs-of-concept. Over the past year we’ve seen more companies put agents into real workflows: sales assistants that triage leads, ops bots that open tickets and follow up, and reporting agents that generate insights and

Quick summary AI agents — purpose-built AI assistants that can read data, call APIs, and complete multi-step tasks — have moved from experimental demos into real business workflows. Modern agent platforms and low-code orchestration tools make it fast to connect agents to CRMs, databases, and reporting systems. That means teams can automate recurring tasks (lead

Quick summary AI agents — autonomous, task-focused AI that can read, act, and interact across systems — are no longer just developer experiments. Over the last 18–24 months we’ve seen businesses pilot agents for things like lead qualification, automated customer responses, and report generation. Low-code agent frameworks and improved retrieval-augmented models make it easier to

Story pick Major vendors and startups have shifted from “LLMs as chat” to “LLMs as agents” — autonomous software that uses language models plus connectors (APIs, RPA, databases) to take actions: update CRMs, generate reports, triage support, and automate routine decisions. That shift is moving agent technology out of labs and into real business processes.

Recent story (short summary) Over the last 12–18 months, a wave of tools — custom GPTs, low-code “agent” builders, and mature retriever-augmented-generation (RAG) pipelines — has made it much easier for companies to build task-specific AI agents. These agents can autonomously draft sales outreach, pull and summarize data for reports, triage support tickets, and trigger

Summary AI agents — autonomous software that can read your data, take actions, and talk to other systems — have become practical for real business work. Instead of one-off demos, companies are rolling out agents for lead qualification, scheduling, automated reporting, invoice processing, and routine customer follow-ups. That shift is driven by better models, easier

Quick summary Autonomous AI agents—models that can access tools, connect to systems, remember context, and carry out multi‑step tasks—have moved out of labs and into real business pilots. Over the last year we’ve seen platforms make it easier to plug agents into CRMs, calendars, and BI tools so they can qualify leads, generate reports, schedule

Short summary AI agents — software that can autonomously run tasks, pull data from systems, and take actions — are moving fast from demos to real business use. Over the last year we’ve seen more purpose-built agents (sales copilots, automated reporting agents, and process bots) integrated with CRMs, ERPs, and business intelligence tools. Instead of

Quick summary AI “agents” — small, goal-directed systems that can run multi-step tasks, interact with apps, and make decisions — moved from demos to real business pilots in 2024. Platform support from big vendors and open-source tools made it easy to string together data access, automation, and natural-language reporting. That means AI can now do