Successfully getting an AI model up and running in a closed lab environment is one thing. Trying to get that same model to run reliably on remote edge devices is a whole other problem entirely. Any MLOps workflow follows the same cycle: develop a model, package it,...
AI models no longer just read and write text. The latest generation of open source models—Qwen3-Omni, Hunyuan-Image3, MiMo-Audio—can reason across text, images, audio, and video simultaneously, generating rich multimodal outputs from a single prompt. But the...
Key takeaways: The last mile problem is translating a verified zero trust delegation token into credentials that external resources will accept while preserving the permission intersection. This is solved by a credential gateway that validates the token, computes the...
This is the second article in our series on Triton kernel profiling. In our first post, Triton kernel profiling with NVIDIA Nsight tools, we introduced how to profile and optimize custom Triton GPU kernels on NVIDIA hardware. In this post, we focus specifically on...
Key takeaways: The identity plumbing for zero trust delegation is accomplished by wiring three technologies together: SPIFFE for service-to-service cryptographic workload identity (mTLS), AuthBridge via RFC 8693 token exchange to pass user delegation context (JWTs),...
Have you ever felt that every conversation you have with an LLM across sessions feels like starting over from scratch? LLMs have a problem: they have the memory of a goldfish (no disrespect to goldfish intended). This article explores the solution: Agent memory. Agent...
As enterprises look to optimize the total cost of ownership (TCO) of Large Language Model deployment, utilizing existing enterprise CPU infrastructure alongside GPU resources for specific inference workloads has become a strategic initiative. However, infrastructure...
When an AI agent acts on your behalf, how much of "you" should it become? In AI systems, agent impersonation creates security risks by granting overly broad permissions. This post introduces a delegation model using a permission intersection' pattern, ensuring agents...
The gap between what an agent claims and what the platform can verify is a real attack surface, and it grows with every new agent you onboard. As agents increasingly discover and call each other at runtime, protocols like Agent2Agent (A2A) have introduced a useful...
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...
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...
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)...
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...
In the world of Large Language Models (LLMs), speed is very important. Much of this speed comes from highly specialized functions called GPU kernels which are small, focused routines that instruct the GPU how to perform calculations with the maximum efficiency....
Triton is a domain-specific language and compiler for writing high-performance GPU kernels (snippets of compiled GPU code) using a Python-like syntax. It offers fine-grained control over memory and parallelism, making it ideal for custom, architecture-optimized...
Triton is a domain-specific language and compiler for writing high-performance GPU kernels in Python. It offers fine-grained control over memory and parallelism, making it ideal for custom, architecture-optimized compute in machine language and high-performance...
Moving AI from interactive chatbots to autonomous "ambient" agents requires a fundamental shift in system architecture. This article examines the technical implementation of agents that operate asynchronously within an enterprise environment. We detail a practical...
As the ecosystem and economy around AI continues to grow and the Internet of Things (IoT) grows smarter and more prolific, a new paradigm of computing is emerging: edge AI. That is, the application of AI technologies to advanced IoT systems. This has all sorts of...
As AI agents become more common in the enterprise, the sheer number of available tools can overwhelm them. This article explores a practical approach based on the `Tool2Vec` methodology to create a smarter tool retrieval system, allowing even small language models to...