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 […]
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GitHub is expanding Copilot beyond the IDE with a new desktop application and a new collaborative work surface called canvas as part of its broader efforts to pitch the AI-assisted coding tool as the control center for agent-native software development.
The desktop application announced at Microsoft’s annual Build conference this week is designed to give developers a dedicated environment for working with AI agents throughout the software development lifecycle, rather than limiting those interactions to code-generation tasks inside an editor, the company wrote in a blog post.
The application includes a collaborative workspace called canvas where developers can brainstorm ideas, refine requirements, generate plans, and iterate on projects alongside AI, it said.
It also has new Agent Merge and code review features that enable developers to automate Copilot to combine tasks of different agents to complete a specific goal or conduct autonomous code reviews according to set standards, it sa
"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.
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
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.
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.
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.
