Quick summary Major momentum in 2024–25 pushed AI agents from experiments into real business work. Cloud and enterprise vendors rolled out managed agent platforms and toolkits; startups delivered vertical agents for sales, finance, and support; and organizations began using agents to run multi-step tasks — from drafting outreach to compiling month-end reports — without coding […]

Summary AI “agents” — autonomous, goal-driven assistants built on large language models and connectors to your systems — moved rapidly from research demos into real business use in 2023–2024. Cloud and AI vendors and many startups launched agent frameworks and pre-built connectors that let these agents perform tasks: pull reports, update CRMs, draft outreach, triage

Quick summary AI “agents” — autonomous, pipeline-style AI that can research, act, and report across apps — are no longer just R&D experiments. In the past year we’ve seen low-code agent builders, tighter integrations with CRMs and BI tools, and improvements in multimodal models that let agents read dashboards, draft emails, and update records automatically.

Quick summary Autonomous AI agents — tools that can act on your behalf (send outreach, book meetings, update records, generate reports) — moved from experiments to practical tools in 2024–25. They’re being built into CRMs, scheduling apps, and reporting systems so teams can automate repetitive work end-to-end instead of only getting “suggestions” from a copilot.

What’s happening There’s been a rapid shift from “AI as a helper” to “AI as an autonomous worker.” Production-ready AI agents and orchestration platforms now let models take multi-step actions across apps (CRM, ERP, helpdesk, spreadsheets) — not just answer questions. In practice that means AI agents can open tickets, enrich records, run reconciliation checks,

Quick summary AI agents — autonomous, task-focused AI that can read your data, take actions, and loop in people when needed — have moved from research demos into real business use. Over the last 18–24 months we’ve seen tooling, integrations, and safety features mature (think agent orchestration libraries, retrieval-augmented workflows, and enterprise connectors to CRMs

Quick summary AI “agents” — models that can act on your behalf to complete multi-step tasks (think: qualifying leads, triaging support tickets, or assembling weekly sales reports) — moved from experiments to real-world use in 2024–25. Businesses are combining large language models with retrieval systems, connectors to CRMs and analytics, and simple automation so these

Quick summary AI agents — software that combines generative AI, connectors to your systems, and simple decision rules — are no longer just experiments. Over the past year we’ve seen more businesses put autonomous agents into production to handle repeatable sales and operations work: lead qualification, personalized outreach, order follow-up, and live reporting dashboards. Better

The story AI agents—software that can carry out multi-step tasks, make decisions, and interact with systems—have moved from labs into everyday business use. Over the last year we’ve seen more enterprise tooling that lets these agents connect to CRMs, finance systems, and reporting platforms. That means companies can automate complex workflows (lead follow-up, deal qualification,

Quick summary AI “agents” — systems that plan and carry out multi-step tasks (think: research, decide, act) using large language models and connected tools — are no longer just experiments. Over the past year, more companies have put these agents into production to automate sales outreach, triage customer support, and generate business reports that once