19 posts
Run untrusted agent-generated code in Kubernetes with a layered sandbox: gVisor runtime isolation, restricted Pod Security, default-deny networking, quotas and admission policy that enforces all of it.
There's an awkward moment in every agentic data project. The agent works. It writes decent SQL, it reasons about schema, it proposes a migration that looks righ...
Instrument LLM agents with OpenTelemetry GenAI conventions, send traces and metrics to Grafana, and build dashboards and alerts for cost, latency, guardrail blocks and tool failures across AWS Bedrock, GCP Vertex AI and Microsoft Foundry.
In the current landscape of software development, the integration of Artificial Intelligence (AI) and Machine Learning (ML) is no longer a luxury.
The shift toward reasoning-heavy Large Language Models (LLMs) marks a pivotal moment in cloud-native AI. While traditional generative models excel at pattern matching and rapid text synthesis, reasoni...
As Generative AI transitions from experimental prototypes to mission-critical production systems, the primary challenge for cloud architects has shifted from model performance to model governance. In ...
The transition from experimental generative AI (GenAI) prototypes to production-grade enterprise applications represents one of the most significant hurdles for modern cloud architects. While the indu...
The evolution of Generative AI has fundamentally shifted the requirements for modern database architectures. While dedicated vector databases initially filled the gap for storing and querying high-dim...
The transition from "chatting with a PDF" prototypes to production-grade Retrieval-Augmented Generation (RAG) involves a significant shift in architectural complexity. At scale, the challenges shift f...
How to schedule and scale GPU-backed LLM inference on Kubernetes today with device plugins, vLLM and sensible autoscaling, and what Dynamic Resource Allocation, now GA, changes for AI workloads across EKS, GKE and AKS.
Run open models locally with Docker Model Runner, call them through an OpenAI-compatible API from your Compose services, and switch to a cloud provider in production by changing two environment variables.
The transition from experimental generative AI to production-grade applications requires a shift from simple stateless interactions to complex, stateful orchestration. While the initial wave of LLM ad...
The shift from traditional application development to AI-native design marks a fundamental change in how we architect cloud systems. In the Google Cloud Platform (GCP) ecosystem, this evolution is cen...
The shift toward Generative AI has forced cloud architects to move beyond traditional CRUD applications and grapple with a fundamental "Buy vs. Build" dilemma: should we leverage a managed service lik...
As enterprises transition from generative AI experimentation to production-scale deployments, the conversation has shifted from "what is possible" to "how do we sustain this economically." In the Micr...
Building a production-grade system for Large Language Model (LLM) inference at scale represents a fundamental shift in distributed systems design. Unlike traditional microservices at companies like Ub...
In the landscape of Generative AI, the "brain" of the application—the Large Language Model (LLM)—is only as effective as the context it can access. While LLMs possess vast general knowledge, they lack...
Retrieval-Augmented Generation (RAG) has transitioned from an experimental pattern to the standard architecture for deploying Generative AI in the enterprise. While large language models (LLMs) posses...
The rapid transition from generative AI experimentation to production-grade deployment represents one of the most significant shifts in enterprise computing history. While the capabilities of Large La...