
AI tool poisoning exposes a major flaw in enterprise agent security
AI agents choose tools from shared registries by matching natural-language descriptions. But no human is verifying whether those descriptions are true.

AI agents choose tools from shared registries by matching natural-language descriptions. But no human is verifying whether those descriptions are true.
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Just a few weeks after announcing Claude Managed Agents, Anthropic has updated the platform with three new capabilities that collapse infrastructure layers like memory, evaluation, and multi-agent orchestration, into a single runtime.

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The company also moved two previously experimental features — outcomes and multi-agent orchestration — from research preview into public beta, making them broadly available to developers building on the Claude platform. Together, the three features address what Anthropic says are the hardest problems in running AI agents at scale: keeping them accurate, helping them learn, and preventing them from becoming bottlenecks on complex, multi-step work.



A little-known Miami-based startup called Subquadratic emerged from stealth on Tuesday with a sweeping claim: that it has built the first large language model to fully escape the mathematical constraint that has defined — and limited — every major AI system since 2017.




"We had over 8,000 people express interest in just 24 hours, and while we wish our office was big enough to welcome everyone, we weren't able to make space for every person who applied," the company wrote in the email, which VentureBeat obtained. "As a small token of appreciation, we've 10x'ed your Codex rate limits until June 5th on your personal ChatGPT account."

The vector database category is undergoing a shift in response to the needs of agentic AI.





The product, first announced at Microsoft's Ignite conference in November, positions itself as a unified control plane that lets enterprise IT and security teams observe, govern, and secure AI agents wherever they run: inside Microsoft's own ecosystem, on third-party cloud platforms like AWS Bedrock and Google Cloud, on employee endpoints, and increasingly across a sprawling ecosystem of SaaS agents built by partner software companies.

The company also moved two previously experimental features — outcomes and multi-agent orchestration — from research preview into public beta, making them broadly available to developers building on the Claude platform. Together, the three features address what Anthropic says are the hardest problems in running AI agents at scale: keeping them accurate, helping them learn, and preventing them from becoming bottlenecks on complex, multi-step work.



A little-known Miami-based startup called Subquadratic emerged from stealth on Tuesday with a sweeping claim: that it has built the first large language model to fully escape the mathematical constraint that has defined — and limited — every major AI system since 2017.

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OpenAI updated the default model for ChatGPT to its new GPT-5.5 Instant, along with a new memory capability that finally shows which context shaped responses — at least some of them.


American Express (Amex) is building a system that lets AI agents shop and pay on behalf of users — but right now it’s only within its own payment network, and still involves a black box that could hinder trust and auditability.


