Ron Mitchell

Summary AI “agents” — models that can act autonomously, call your apps, and follow multi-step workflows — have moved from research demos into real products and low-code toolkits. Over the last 18 months, major AI platforms and cloud vendors pushed agent frameworks, connectors, and safer guardrails so businesses can build agents that do things like […]

Story summary AI agents — small, task-focused software assistants built on large language models — went mainstream in 2024. Platforms like OpenAI’s custom “GPTs” and enterprise copilots from major cloud providers made it easy for non‑engineers to create agents that can read documents, interact with apps, and complete multi‑step workflows (for example: qualify a lead,

Summary Autonomous AI agents — software that can take multi-step actions on your behalf (think: read CRM records, draft and send emails, update dashboards, and escalate issues) — are moving out of labs and into real business use. Companies are using them to automate routine sales tasks, generate up-to-date reports, and orchestrate handoffs between teams.

Quick summary AI “agents” — autonomous, tool-enabled AI that can complete multi-step tasks — are moving from research demos into real business use. Platforms and plugins now let agents connect to CRMs, ticketing systems, data warehouses and routers, so they can do things like qualify leads, generate and send personalized outreach, triage support tickets, or

Quick summary – This year, major vendors rolled generative AI directly into reporting and workflows: think natural-language copilots in analytics tools and “agents” that can pull data, run queries, and draft actions automatically. – The result: non-technical teams can get answers, summaries, and suggested next steps from their data without waiting for a BI analyst.

AI agents — small, goal-driven AI programs that act across apps and systems — are moving from prototypes into real business use. Sales teams use them to qualify leads and update CRMs. Finance teams automate invoice triage and expense reporting. Operations teams run routine audits and escalate exceptions. The result: faster processes, fewer manual handoffs,

Quick summary AI “agents” — autonomous, connected AI assistants that can act across apps — have moved from experiments to real business deployments. Instead of one-off chatbots, companies now use agents that can qualify leads, update CRMs, run pricing checks, and generate real-time reports without constant human hand-holding. Better connectors, orchestration tools, and enterprise “copilots”

What’s happening A new wave of enterprise-grade AI agents — tools that can act across apps, follow multi-step workflows, and learn from feedback — is making it practical for businesses to automate complex sales and ops processes. These agents can do things like enrich leads, draft customized outreach, book meetings, summarize calls, and update CRMs

Story summary There’s been a clear shift from single-purpose AI tools to autonomous, task-focused AI agents and “copilots” that can run multi-step workflows: pull data, analyze it, write messages, update systems, and follow up — all with little human prompting. Businesses are piloting agents that create weekly sales reports, generate personalized outreach at scale, handle

Quick summary AI “agents” — small, goal-driven systems that can read, act, and coordinate across apps — are moving from labs into real business use. Instead of delivering a single answer, these agents can run multi-step workflows: fetch data from your CRM, draft an email, update records, and generate a follow-up report — all with