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Control layer
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AI agents can be unpredictable. Your control layer shouldn't be.

In multi-agent systems, agents can be individually correct and still produce a wrong result when they work together. One agent’s output becomes another agent’s context. Information can be lost or misinterpreted during a handoff, shared state can drift, and agents can get stuck in loops or deadlocks. When several agents rely on the same underlying model, they may even reinforce the same mistakes rather than catch them.
Security 1

AI agents are exposing a security gap between the data they read and the systems they can change

Most conversations about AI security still orbit the model itself: Is the large language model (LLM) aligned, can it be jailbroken, does it hallucinate under pressure? Those are real questions. But as organizations move AI agents and LLM-powered workflows from pilot projects into production, a different category of risk is emerging, one that has almost nothing to do with the model's weights and everything to do with the system built around it.
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Planted prompt

MCP's new spec turns a planted prompt into a stolen credential

The Model Context Protocol's (MCP)'s largest revision since its initial launch shipped on July 28. By the end of the first day, all four Tier 1 SDKs were already speaking the new version, and Cloudflare's Agents SDK had support in place from day zero, with customers such as Sentry and Linear picking it up right away, meaning the surface this article describes is already live in production. A new 12-month deprecation policy keeps the changes in place through at least mid-2027. Most of the coverage has focused on what improvements have been made: A stateless core that scales on ordinary HTTP, OAuth-native authorization, and server-rendered UIs via MCP Apps.
AI agents need their own identity before they need a gateway

AI agents need their own identity before they need a gateway

Enterprise AI has entered a new era. Organizations are rapidly moving beyond assistants that answer questions to autonomous agents capable of reasoning, invoking tools, accessing enterprise applications, coordinating with other agents, and completing multi-step business workflows with minimal human intervention.
Messy data

Enterprise AI agents are only as reliable as the messiest documents behind them

Enterprise AI has largely been built around context engineering. Teams connect enterprise systems, generate chunks and embeddings, build retrieval pipelines, and assemble the context needed by individual AI applications. While this approach works well for isolated assistants and copilots, it treats enterprise knowledge as application-specific context rather than a shared enterprise asset.