Generative AI agents — autonomous, multimodal models that can read, plan, and act across apps — are moving from labs into business teams. Over the past year, more organizations have started piloting agents for customer service, sales outreach, IT ticket triage, and automated reporting. These agents combine large language models (LLMs) with retrieval-augmented generation (RAG), […]
Short summary (for LinkedIn and business channels) Companies are rapidly adopting Retrieval-Augmented Generation (RAG) — pairing large language models (LLMs) with vector databases — to build private, accurate knowledge assistants. Instead of asking a generic LLM to guess answers, RAG systems search your own documents, embed the most relevant facts, and feed them to the
AI trend snapshot AI “agents” — autonomous workflows that combine large language models with tools, connectors, and retrieval systems — are moving rapidly from pilot projects into real business operations. Instead of single-use chatbots, modern AI agents can read your documents, pull data from CRMs and ERPs, run scheduled reports, open tickets, and even trigger
AI is moving beyond chat. Over the past year, “AI agents” and enterprise copilots — systems that combine large language models, retrieval-augmented generation (RAG), and automated task runners — have gone from proofs-of-concept to production use. Companies are using these agents to search internal knowledge, draft emails and reports, automate routine workflows, and trigger actions
Quick summary The EU AI Act is driving a new era of AI regulation. Regulators are focusing on transparency, safety, and accountability for AI systems — especially generative AI and any tools that make decisions affecting people. That means companies using LLMs, copilots, or automated decision tools will soon need formal risk assessments, documentation, human
AI agents — small, goal-directed AI programs that can research, draft, act, and follow up — are moving fast from proof-of-concept to real business use. Over the last year we’ve seen major platforms (from cloud providers to open-source frameworks) make agents easier to build, connect, and manage. That means teams can automate routine sales and
AI agents — small, goal-directed AI programs that can research, draft, act, and follow up — are moving fast from proof-of-concept to real business use. Over the last year we’ve seen major platforms (from cloud providers to open-source frameworks) make agents easier to build, connect, and manage. That means teams can automate routine sales and
Quick summary Generative AI (large language models and multimodal agents) is being embedded into robotic process automation (RPA) and workflow platforms. Instead of rigid, rule-based bots, organizations can now build flexible “AI-driven” automations that understand unstructured text, extract data from emails and documents, summarize exceptions, and follow natural-language instructions. The result: faster automation development, broader
How Generative AI Is Supercharging RPA — What Business Leaders Need to Know Read More »
Big news: enterprise AI agents — sometimes called copilots — are moving fast from lab demos into real business use. Major cloud vendors and platforms now let companies build custom AI agents that connect to internal data, CRMs, and workflows. That means generative AI can do more than write text: it can pull live data,
AI news snapshot Autonomous AI agents—software that can plan, act, and follow up with little human input—have moved from experiments to real business pilots. Over the past year, more companies have tested agents that handle tasks like sales outreach, customer triage, invoice processing, and monitoring systems. Frameworks such as LangChain, agent templates from major cloud
