AI Post

Summary OpenAI and other providers recently made it much easier for non‑technical teams to build custom AI agents (sometimes called “GPTs” or no‑code agents). These tools let you create a tailored assistant by adding instructions, uploading documents, and connecting simple APIs — without writing a lot of code. Why this matters for businesses – Faster […]

Story summary AI “agents” — autonomous or semi-autonomous software that can read your systems, take actions, and follow multi-step workflows — have moved past pilots into real business use. In practice you’re seeing agents that: – Draft and send follow-up emails from your CRM – Schedule meetings, summarize calls, and update records automatically – Run

Summary Autonomous AI agents — small AI programs that can take actions, run workflows, and talk to apps — are moving from labs into real business use. Vendors and startups are now offering agent platforms that connect to CRMs, calendars, email, and data warehouses so agents can qualify leads, draft outreach, schedule meetings, and generate

Short summary AI agents — software that uses large language models to run multi-step tasks on its own — have moved out of demos and into real business pilots. Better connectors (to CRMs, databases, email), improved retrieval techniques (so agents use the right data), and clearer guardrails mean these agents can now handle repeated, structured

Summary AI agents — software that can plan, act, and carry out multi-step tasks — are moving from lab demos into real business use. Companies are already using agents to research leads, draft personalized outreach, automate routine customer tasks, and produce near-real-time reports. Improvements in large models, retrieval-augmented generation (RAG — i.e., using your own

Short summary There’s been a clear surge in business-grade “AI agents” — autonomous or semi-autonomous AI workflows that can read your data, take actions (send emails, update CRMs, generate reports), and learn from results. Tools and frameworks now make it easier to build agents that connect to your systems, follow rules, and keep humans in

Short summary Autonomous AI agents — software that can take actions, make decisions, and carry out multi-step tasks with little human hand-holding — have moved from lab experiments to real business pilots. Big vendors have embedded LLMs into products (think sales copilots, AI assistants in CRMs, and RPA platforms using generative models), and more mid-market

Quick story Autonomous AI agents — systems that can plan, act, and follow up on tasks with little human supervision — have moved from experiments into real business use. Over the last year we’ve seen frameworks, orchestration tools, and integrations that let these agents connect to CRMs, databases, calendars, and reporting stacks. That means AI

Quick summary Major AI platforms (OpenAI, Microsoft Azure, Google Cloud and others) are making it much easier to create task-specific AI agents — small, focused AI assistants that can run workflows, pull data, and answer questions on behalf of employees. These agents are moving from pilot projects into real business workflows for sales, operations, and

Short summary AI agents — software that can act autonomously to complete multi-step tasks — have moved from labs into real business work. Modern agent platforms can pull data from your CRM, generate proposals, run outreach, update inventories, and create regular sales and performance reports without someone doing every step manually. That means faster workflows,