Ron Mitchell

Short summary AI “agents” — autonomous or semi-autonomous AI assistants that can take multi-step actions, talk to your systems, and complete tasks — are moving from labs into real business workflows. Tools and frameworks (think agent orchestration, CRM plug‑ins, and integrations with reporting systems) make it easier for teams to automate outreach, generate roll‑up reports, […]

The story in brief – Over the last year, we’ve seen a clear shift: AI agents — small, goal-oriented AI programs that act on behalf of users — are being used beyond R&D pilots and into everyday sales and operations workflows. – Companies are automating repetitive work like CRM updates, lead qualification, outreach drafts, and

Big idea — quick summary AI agents (autonomous assistants powered by large language models) are no longer lab experiments. Over the past year we’ve seen more robust models, orchestration tools, and enterprise connectors that let these agents log into CRMs, pull data, send emails, and update reports — with far less developer overhead than before.

Quick summary Major AI vendors and startups are moving beyond chatbots to autonomous AI agents — smart software that can take multi-step actions across apps (CRM, calendar, email, reporting). Companies are already using agents to automate sales follow-ups, enrich leads, generate routine reports, and speed decision-making. That shift is making AI less about one-off answers

Short summary AI “agents” — small, goal-focused AI programs that act on your behalf — have moved from experiments to practical tools. Low-code agent builders and plug-and-play integrations now let teams automate tasks like lead qualification, customer triage, and recurring reporting without months of engineering work. Why this matters for business – Faster automation: You

Quick summary AI agents — autonomous, tool-enabled AI that can plan, act, and complete multi-step tasks — are moving out of lab demos and into real business use. Advances in large language models, retrieval-augmented generation (RAG), vector databases, and secure integrations mean agents can now connect to CRMs, internal docs, calendars, and APIs to do

Summary AI agents — autonomous software that can carry out multi-step tasks (think: research, draft, run a report, and follow up) — have moved from demos to real business use cases. Over the past year we’ve seen tools become more reliable, easier to connect to company data, and simpler to orchestrate into workflows. That means

Quick summary A new generation of large language models and low‑code “assistant” platforms has made AI agents practical for real business work. Models like GPT‑4o (and similar multimodal, cheaper, real‑time models) plus tools for building custom assistants let companies create agents that can read documents, summarize meetings, open tickets, update CRMs, generate reports, and even

Quick summary AI agents — small, autonomous software assistants that can read, write, and act across apps — have moved from labs into real business use. Instead of a person manually pulling dashboards, sending follow-ups, and chasing approvals, an AI agent can gather data from your CRM, summarize customer health, draft an outreach sequence, and

AI story summary AI agents — autonomous, multi-step AI that can research, act, and follow up without constant human prompts — have moved from demos into real business use. Over the last year, platforms and frameworks (agent toolkits, integrations with RPA and CRMs, and safer retrieval methods) have made it practical to run these agents