Ron Mitchell

Short summary There’s been a clear shift this year from single-query AI tools toward autonomous AI agents — systems that can plan, fetch data, take multiple actions, and close tasks without constant human prompting. These agents are being embedded into CRMs, reporting tools, and workflow platforms so they can do things like enrich leads, run […]

Short summary AI “agents” — software that can act on your behalf (think: read your CRM, draft outreach, run reports, schedule follow-ups) — have moved fast from proof-of-concept demos into tools companies can actually use. New platforms and integrations now let agents connect to internal data, calendars, and apps so they can complete multi-step tasks

Summary AI “agents” — autonomous or semi-autonomous software that completes tasks across apps — have moved from experiments to real-world sales and operations use. Companies are deploying agents that draft personalized outreach, summarize meetings, update CRMs, route leads, and generate routine reports. These agents combine large language models with integrations, retrieval-augmented generation (RAG), and simple

The story (short) In the last year vendors from OpenAI to Microsoft and Google released easy ways for companies to build custom AI agents — think “GPTs” or Copilot templates — that can read your CRM, run reports, draft outreach, and take routine actions. These tools remove much of the heavy engineering needed to deploy

Quick summary AI agents — software that can act autonomously to complete multi-step tasks — have moved from research demos into real enterprise tools. Major cloud providers and startup platforms now let you deploy agents that can read your CRM, pull data, write emails, schedule meetings, and generate reports without constant human prompting. Why this

Summary AI agents — software that can read, decide, and act across apps — are moving from experiments into real business use. Paired with retrieval‑augmented generation (RAG), these agents can pull up the exact customer data, past emails, contracts, and product info they need to generate accurate summaries, suggested next steps, customized outreach, and on‑demand

Short summary Companies are increasingly combining retrieval-augmented generation (RAG) with vector databases and lightweight AI agents to turn messy company data into trustworthy, automated reports and actions. Instead of forcing teams to manual data pulls or one-off scripts, businesses are using embeddings + vector search to let AI “understand” CRM notes, contracts, support tickets, and

AI agents are moving from experiments to real sales impact — what your business should do next Summary — the story in plain language AI “agents” — software that can act on behalf of a user (researching, emailing, updating systems, generating reports) — have crossed a tipping point. What was once a developer experiment is

Short summary AI “agents” — AI systems that can plan multi-step tasks, call tools, fetch company data, and take actions — have moved from research demos to real business pilots. Platforms and developer toolkits (think custom GPTs, agent frameworks, and low-code copilots) make it easy to stitch LLMs to CRMs, BI tools, calendars, and ticketing

Short summary AI agents — software that can act autonomously across apps, data, and workflows — have moved from experiment to practical tool for companies. Today’s agents can draft and send emails, update CRM records, run weekly sales reports, and triage customer issues without constant human prompts. The combination of task-focused models, retrieval-augmented generation (RAG),