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.
Microsoft has identified seven new failure modes in agentic AI systems, in addition to those it identified last year in its first Taxonomy of Failure Modes in Agentic AI Systems.
Four things contributed to the growing list of ways agentic AI can go wrong: the speed at which the technology went mainstream, the growing maturity of the Model Context Protocol (MCP) ecosystem, the rise of computer-use agents, and finally the gathering of more empirical evidence as researchers obtained more real-life findings.
The seven new failure modes it has identified are:
Agentic Supply Chain Compromise —agent behavior can be affected by natural language rather than malicious code;
Goal Hijacking — adversarial instructions appear aligned with legitimate task completion, while silently redirecting the agent’s terminal goal;
Inter-Agent Trust Escalation —a compromised agent asserts false identity or inflates claimed permissions to an orchestrator;
Computer Use Agent (CUA) Visual Attack — agents operating
Projection, much? Microsoft’s head of AI has accused a rival’s AI service of being too pricey, just as the introduction of usage-based pricing for GitHub Copilot begins to hit developers using its own services.
“Anthropic is extremely expensive and I think many people are urgently looking for alternatives,” Mustafa Suleyman, CEO of Microsoft AI, told Bloomberg News.
The spotlight is on the cost of AI services at the moment, with so many different parts of the business using the technology while at the same time many businesses are finding it hard to report any meaningful ROI.
This week, Microsoft at its annual Build conference looked to fight back against this when it announced seven new AI models, emphasizing the lower cost. The company hopes that cheaper AI models will mean more enterprises find that AI projects are viable. In 2025, Gartner reported that many such endeavors would be cancelled by 2027: cheaper implementations could be the way forward.
Microsoft clearly sees its own AI
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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.
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
