Quick summary AI agents — autonomous software that can read email, query your CRM, run searches, and take actions — are no longer just lab experiments. Over the past year major platforms and dozens of startups have made it easier to build agents that connect to enterprise systems, run multi-step workflows, and produce live reports. […]

Summary AI agents — systems that can perform multi-step tasks on their own (think: research, draft emails, update CRM records, and produce reports) — have moved from experiments to everyday business tools. Vendors and open‑source frameworks have matured the tech, and companies are shipping integrations that connect agents to calendars, CRMs, data warehouses, and workflow

Quick summary Autonomous AI agents — small, purpose-built systems that chain together LLM prompts, data lookups, and task actions — are no longer a research novelty. Over the past year we’ve seen platforms and enterprise toolsets make these agents easier to deploy and safer to manage. That means teams can automate multi-step work like sales

Short summary Over the last year the big shift has been clear: AI agents — autonomous, task-oriented systems that can act across apps and data — have moved from experimentation into everyday business use. Vendors and startups are packaging reusable agents and developer frameworks, while companies are deploying them to qualify leads, automate order processing,

Story pick (summary) Over the past year we’ve seen a clear shift: AI agents — conversational, goal-driven systems that can act across apps — are moving out of experimental pilots and into real business workflows. Advances in large language models, better connectors to CRMs, ERPs and BI tools, and tighter RPA (robotic process automation) integrations

Big idea in plain language AI agents — software that can carry out multi-step tasks on its own — have moved from demos into real business use. Major vendors are packaging agent tools and connectors that let companies automate workflows, pull live data, and even run meetings or customer follow-ups without building everything from scratch.

Quick summary AI agents that combine large language models with retrieval-augmented generation (RAG) and vector search are moving from experiments into real business use. Instead of asking a generic chatbot, companies are building agents that pull answers from their own sales data, product docs, CRM notes, and dashboards — then act (route leads, draft proposals,

Quick summary AI agents — software that can perform tasks, make decisions, and carry out multi-step workflows with little human intervention — are no longer just a lab curiosity. Early adopters are using agents to handle things like lead follow-up, invoice reconciliation, automated reporting, and scheduling. The result: faster responses, fewer errors, and lower labor

Quick summary AI agents — autonomous, task-focused systems that can read your data, take actions, and talk to other apps — moved from labs into real business use in 2024–25. Companies now use agents to qualify leads, schedule meetings, draft proposals, update CRMs, and generate routine reports. When set up well, these agents speed response

Short summary AI “agents” — configurable assistants that act on data, apps, and APIs — have moved from research demos into real business deployments. Over the last year vendors and platforms shipped better agent frameworks, easier connectors to CRMs and calendars, and safer function-calling tools. That makes it easier for non‑technical teams to create agents