InfoWorld AI·
Agentic AI has moved from conference hype to a budget line item. This is where the conversation gets more interesting and more uncomfortable. Unlike traditional AI systems that respond to a single prompt, classify a document, recommend an action, or generate a summary, agentic AI systems are designed to pursue goals. They plan, call tools, inspect results, retry failed steps, consult memory, hand off tasks to other agents, and sometimes critique their own work before producing an answer or taking an action. That extra autonomy is the value proposition. It also introduces the cost problem. A single chatbot interaction may consume a few thousand tokens. A useful agentic workflow can consume hundreds of thousands or millions of tokens per day because it does more than answer a question. It decomposes the problem, retrieves context, reasons through options, invokes APIs, checks the output, and often runs multiple passes before reaching a result. Therefore, the economics need to be understo
Read full article"Agent" is the most overused word in AI right now. But strip away the hype and what are you actually working with? Adobe principal scientist Deepak Pai breaks down the real building blocks of agentic systems and when they're worth reaching for.
Shell will use agents from C3 AI to shift from basic anomaly detection towards fully-automated predictive maintenance. The global energy giant is building on their current use of the C3 AI Reliability Suite, which already keeps tabs on more than 30,000 crucial pieces of equipment across upstream and downstream operations. Shell now intends to lean […] The post How C3 AI agents will automate predictive maintenance for Shell appeared first on AI News.
Microsoft Build 2026 was about far more than new AI models – it revealed the company’s blueprint for a unified intelligence platform that connects agents, enterprise data, governance, and continuous learning into a single ecosystem. From MAI models and Frontier Tuning to Microsoft Scout and Azure Foundry, discover the key announcements shaping the future of enterprise AI.
Venice's two-front bet to rival OpenAI and Claude: win consumers with private, uncensored, aggregated AI, then sell that inference to agents.
Meta has launched Business Agent to automate conversational commerce workflows directly inside its messaging applications. The software allows global retail brands to execute transactions and field support tickets without human intervention. Deploying this architecture places agentic AI directly at the core of social commerce. Meta integrated these workflows natively into Instagram, Messenger, and soon WhatsApp. […] The post Meta Business Agent drives AI-powered conversational commerce appeared first on AI News.
Stanford researchers released OpenJarvis, an open-source framework that runs inference, agents, memory, and learning entirely on-device. It decomposes a personal AI system into five composable primitives — Intelligence, Engine, Agents, Tools & Memory, and Learning — and lands within 3.2 points of the best cloud model at roughly 800× lower marginal API cost. The post Meet OpenJarvis: A Local-First Framework for On-Device Personal AI Agents with Tools, Memory, and Learning appeared first on MarkTechPost.
Once an adversary gets inside the network, the problem is no longer prevention; it’s limiting lateral movement and the scope of disruption.
A true agentic enterprise requires a fabric that connects goals to workflows, workflows to agents, agents to data and systems, and every action to governance.
