Ron Mitchell

Quick summary AI agents — autonomous, goal-driven programs that can plan, act, and use tools — moved from research demos into real business pilots in 2024. Tools built on frameworks like LangChain and vendor “copilots” let these agents handle multi-step work: triaging customer messages, generating and sending sales outreach, gathering data across systems, and producing […]

Short summary AI “agents” — autonomous, multi-step AI assistants you can build and connect to your apps — are moving from labs into business teams. Major vendors (OpenAI GPTs, Microsoft Copilot Studio, Anthropic/Claude agent features) and a wave of low-code builders now let non‑engineers create agents that run repeatable workflows: qualify leads, send follow-ups, generate

Summary AI “agents” — autonomous AI programs that can fetch data, execute steps, and act across apps — moved from research demos to real business tools in 2024. Frameworks and platforms (agent SDKs, low-code connectors, and cloud integrations) now let companies create agents that qualify leads, update CRMs, generate reports, and follow up with customers

Quick summary AI “agents” — autonomous assistants that read your data, take actions, and follow up — have crossed a practical threshold. Instead of being lab experiments, they’re now routinely used to do specific tasks: follow up on leads, update CRMs, generate regular reports, and automate routine approvals. Advances in retrieval-augmented generation (RAG), secure data

AI agents are finally moving from experiments to real business value — sales, automation, and reporting Quick summary – What happened: Over the last year we’ve seen a wave of enterprise-ready “AI agents” — autonomous workflows powered by modern large models and tool integrations. These agents can do things like qualify leads, update CRMs, run

Short summary AI “agents” — autonomous assistants that can run tasks, talk to systems, and make decisions — are no longer just experiments. Over the last year vendors and integrators have packaged these agents for real business use: connecting to CRMs, calendars, help desks, and data warehouses so an agent can qualify leads, generate tailored

Big picture: Over the last year we’ve seen AI agents — autonomous systems that can read, act, and coordinate across apps — move from lab demos into real business workflows. Vendors and startups are shipping connectors, low-code agent builders, and pre-built vertical agents (sales, support, finance). At the same time, AI-powered reporting tools that combine

Quick story summary Over the past year we’ve seen a wave of low‑code and no‑code platforms for building autonomous AI agents — programmable assistants that carry out multi‑step tasks (research, updates, outreach, reporting) with minimal human direction. Those platforms combine large language models, connectors to CRMs and databases, and retrieval‑augmented generation (RAG) so agents can

Short summary AI “agents” — autonomous or semi-autonomous software that can act on your behalf (e.g., qualify leads, send follow-ups, pull and summarize reports) — moved from experiment to enterprise-ready in 2024. Better models, retrieval-augmented workflows, prebuilt connectors to CRMs and data warehouses, and clearer guardrails mean companies can now deploy agents that safely automate

Summary AI agents — the “smart workers” that can run tasks, pull data, and act on your behalf — moved from experiments to real business pilots in 2024. Companies are already using agents to qualify leads, auto-generate sales reports, route orders, and automate repetitive back-office work. The result: faster response times, fewer manual steps, and