AI Post

Short summary AI “agents” — autonomous assistants that can research, draft, and act across apps — went from lab experiments to real business pilots over the last 18–24 months. Teams are using them to run prospect research, draft outreach, reconcile invoices, and generate executive reports without manual handoffs. At the same time, customers and regulators […]

Quick summary AI “agents” — small, goal-driven AI programs that can act (send emails, pull reports, update CRMs) with minimal human prompts — moved from demos into real business pilots in 2023–24. No-code builders, agent frameworks, and better integrations with CRMs and BI tools mean companies can now deploy agents for everyday work: lead qualification,

Story summary There’s a clear, recent surge in enterprise adoption of AI agents — autonomous, task-focused AI that can research leads, draft emails, run reports, and even trigger actions across systems. Big vendors and a growing number of startups are offering “Agent-as-a-Service” or agent toolkits that make it easier to connect LLMs to CRMs, data

The story (short) – Over the past year, “AI agents” — autonomous, workflow-focused AI assistants that can connect to apps, fetch data, and take multi-step actions — have moved from labs into real business tools. – Low-code builders, stronger connectors to CRMs and databases, and improvements in retrieval-augmented generation (RAG) make these agents useful for

The story in brief – Lately, businesses are moving beyond one-off AI experiments and building AI agents: lightweight systems that combine LLMs, data retrieval, connectors, and simple automation to run real tasks (sales outreach, report generation, ticket triage). – These agents don’t replace people — they handle repetitive, multistep work and feed humans with clean,

Summary Over the last year we’ve moved from “chatbots” to autonomous AI agents and built-in workplace copilots that can perform multi-step tasks: qualify leads, pull and summarize data from multiple systems, generate reports, and even trigger actions in your CRM or ERP. These agents combine large language models, retrieval (vector) databases, and workflow connectors so

Short summary AI “agents” — autonomous, task-focused AI programs that can research, act, and follow up without constant human prompting — are moving from demos into real business use. Over the past year we’ve seen platforms and integrations that make it easier to deploy agents for sales outreach, customer follow-ups, scheduling, and automated reporting. That

Quick summary AI “agents” — autonomous, task-focused AI tools that can read your systems, take actions, and learn from outcomes — went from lab experiments to practical business tools in the past 12–18 months. Companies are using them to enrich leads, automate outreach, prepare sales playbooks, generate near-real-time reports, and trigger follow-up actions in CRMs

Quick story Over the last 18 months, “AI agents” — autonomous workflows that can read data, act across apps, and carry out multi-step tasks — have moved from demos into real business use. Vendors and startups now offer agent frameworks that connect to CRMs, BI tools, calendars, and email systems so a single agent can

Summary Over the past 18 months we’ve moved beyond demos and “one-off” generative features. The real trend now is autonomous AI agents — workflows that connect LLMs, retrieval (vector databases), APIs, and business systems to execute multi-step tasks. Companies are using these agents to automate sales outreach, generate and distribute regular reporting, and handle routine