AI Post

What happened (short summary) OpenAI’s recent push to make custom GPTs and “Automations” easy to build has changed the game for business AI. Instead of heavy engineering projects, teams can now create AI agents that run multi-step tasks, pull company data, and connect to tools—often with low-code setup. That means AI agents are moving from […]

Quick summary Over the past year major AI platforms and startups have shifted from research demos to production-ready AI agents — software that can act autonomously, access systems, and complete multi-step tasks. These agents are being used for everything from automated customer follow-ups and sales outreach to generating operational reports and orchestrating back-office workflows. Why

Quick summary AI “agents” — autonomous software that can run workflows, pull data, write messages, and trigger actions — moved from research demos into real business pilots over the last year. Companies are using agents to auto-generate sales reports, qualify leads, schedule follow-ups, and run routine finance or HR tasks without needing a human to

Quick recap AI agents — software that uses large language models to carry out multi-step tasks, call APIs, and act like a virtual assistant — shifted from demos to real business use in 2024. Platforms like custom GPTs, agent toolkits (LangChain, RAG frameworks), and built-in tool use in major LLMs made it easier to automate

The story Autonomous AI agents — systems that can plan, act, and follow through on tasks with little human prompting — are accelerating out of research demos and into real-world business use. Vendors and platforms are packaging agent frameworks for teams (think “digital assistants” that can call APIs, read documents, and execute multi-step workflows). Companies

Quick summary AI agents — software that combines large language models with tools, APIs, and workflows — are no longer just research demos. Businesses are deploying them across sales, operations, and finance to qualify leads, automate routine outreach, assemble reports, and orchestrate multistep processes. The result: faster deal cycles, cleaner pipelines, and fewer manual hours

Quick summary In recent months, AI “agents” — autonomous models that can plan, act across apps, and follow up on tasks — have moved from demos into practical business tools. Cloud vendors and startups are shipping agent frameworks and low-code builders so teams can create assistants that fetch data, generate reports, qualify leads, and trigger

Summary AI agents—software that can act on your behalf, complete tasks, and make decisions—are no longer just a research headline. Companies are experimenting with autonomous agents to handle routine workflows: prospecting and follow-up in sales, triaging customer support tickets, compiling monthly reports, and automating procurement approvals. Unlike simple chatbots, these agents can connect to your

Summary AI agents — autonomous, workflow-focused AI that can read data, take actions, and talk to systems — are no longer just research demos. Over the last couple of years we’ve seen companies combine large language models with connectors, retrieval-augmented generation (RAG), and low-code tooling to automate routine work: lead qualification, internal reporting, customer follow-ups,

Short summary AI agents — software that can act autonomously across apps, fetch data, and complete multi-step tasks — are moving fast from lab experiments into real business workflows. Instead of one-off chatbots, companies now build agent-driven processes that do things like generate sales outreach, reconcile data for monthly reports, and automate approvals across systems.