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Code execution with MCP: How sandboxed Python replaces tool schema bloat in AI agents
As the number of tools connected to an AI agent grows, JSON Schema definitions become a massive scaling bottleneck. Every tool carries a full schema that gets loaded into the LLM’s context window on every turn. Our tests show that replacing these schemas with a...
PyTorch Call Stack Deep Dive: Tracing Tensor Operations from Python to C++ Kernels
Eliminating the ‘Rego tax’: How AI orchestrators automate Kubernetes compliance
Manually writing OPA Rego policies is a significant bottleneck for many platform teams, creating a 'Rego tax' that can slow down development and introduce risk. This article introduces a new approach: a Dynamic Kubernetes Policy Generator that uses a large language...
Zero trust AI agents on Kubernetes: What I learned deploying multi-agent systems on Kagenti
AI agent content focuses on prompt engineering and framework selection. But very little addresses what happens when those agents run in production: Who they are, what they're allowed to call, and whether anyone can tell what they did. I spent 2 weeks (January 2026)...
Zero Trust for autonomous agentic AI systems: Building more secure foundations
AI systems are no longer just single-purpose models. With the rise of agentic AI, software systems designed to carry out complex tasks and solve problems with limited human supervision. It's a step beyond generative AI, which creates content, to an AI that does...