Ron Mitchell

Quick summary AI agents — autonomous software that can read your data, take actions (send emails, update CRMs, schedule demos), and generate human-readable reports — have moved from lab experiments to real business tools. Modern agent platforms pair large language models with connectors (CRMs, calendars, BI tools) and retrieval systems (vector databases) so an agent […]

Step 1 — The story in one line There’s a clear surge in autonomous AI agents moving out of labs and into real business workflows — cloud vendors and startups are shipping agent frameworks that let models act across calendars, CRMs, ticketing systems and reporting tools. Step 2 — What this means for business (short

Quick summary AI “agents” — autonomous models that can use tools, search your files, and follow multi-step instructions — have moved fast from research demos into real business pilots. Tooling like LangChain, vector databases, and retrieval-augmented generation (RAG) make it practical to build agents that understand company documents, act inside CRMs, and generate ready-to-use reports.

Quick story Over the past year, AI agents have moved from proofs-of-concept into real business apps. Major vendors (Microsoft Copilot, Google Duet, Salesforce Einstein) and a wave of startups launched agent frameworks and connectors that let AI act on company data — scheduling meetings, drafting sales outreach, triaging support tickets, and generating on-demand reports. The

Quick summary – What happened: In the last year we’ve seen AI agents move from research demos to practical tools that can connect to company systems (CRMs, BI, ERP) and run multi-step workflows. Vendors and open-source frameworks now offer connectors, authentication patterns, and better retrieval (RAG) so agents can use your data instead of guessing.

Quick summary AI “agents” — autonomous, task-focused systems that can read emails, pull CRM data, run reports, and take follow-up actions — have moved from lab demos into real business use. Companies are now building agents that qualify leads, update pipelines, generate weekly sales reports in plain English, and automate repetitive workflows across tools. Why

Quick summary AI “agents” — models that can take multi-step actions across apps, pull in company data, and carry out tasks without constant human prompts — moved from demos into real business use in 2023–24. Tools and frameworks (think copilots in office apps, connector-driven agents, and orchestration platforms like LangChain-style stacks) made it practical to

Quick summary AI “agents” — autonomous, goal-driven AI that can act across apps and data — are moving from demos into real business use. Instead of just suggesting text or insights, modern agents can qualify leads, update CRMs, book meetings, pull live numbers, and produce routine reports without constant human prompts. Why this matters for

Quick summary OpenAI and other vendors have made it much easier this year to build custom AI agents — think “little AIs” you can tailor to your business, connect to your CRM, internal docs, or reporting tools, and deploy quickly. These new tools (custom GPTs, agent builders, and app plugins) let non‑engineers create agents that

Quick summary AI agents — autonomous software that can read your data, run workflows, and take actions — are moving from research demos into real business use. Paired with retrieval-augmented generation (RAG), secure connectors, and real‑time APIs, these agents can pull CRM and ERP data, generate actionable reports, triage leads, update records, and even trigger