AI on ThecoreGrid focuses on production-grade engineering for machine learning and LLM systems in highload environments.
We cover how to design scalable AI architectures, build reliable data and feature pipelines, and choose infrastructure for training and inference with predictable latency, cost, and resilience. The content is curated from real BigTech practices: incident post-mortems, MLOps and DevOps patterns, observability, security, and governance for AI-powered products. Instead of hype or beginner tutorials, you get deep technical analysis of real-world implementation: LLM integration into existing services, RAG architecture decisions, orchestration strategies, vector databases, caching, CI/CD for ML, and model quality control in production. The AI tag is built for architects, ML engineers, backend/platform teams, and SREs who deploy AI in critical systems and need robust, maintainable, and scalable solutions.
AI agents in development have become more autonomous, but this has been accompanied by increased costs of errors and control complexity. The primary tension has shifted from model quality to system behavior management. The problem does not manifest immediately, but rather the moment the agent steps outside a simple scenario. Early approaches like “vibe coding” … Read more
Slowdowns in QA processes often become a hidden limit for the entire engineering team. In this case, optimizing the testing pipeline has a disproportionately strong effect on delivery speed. The problem does not manifest immediately—only when the release cycle begins to depend on verification rather than development. Manual E2E (end-to-end) tests and limited parallelism create … Read more
The coregrid Radar is a weekly column where we curate key architectural insights and major releases. No need to search across multiple sources — everything in one place.
AI agents complicate observability: the same request can lead to different chains of actions. Without tracing, the system becomes opaque. The problem manifests when generative systems transition from simple LLM calls to agents. An agent plans steps, invokes tools, and makes decisions dynamically. Behavior becomes non-deterministic: the same prompt can result in different call sequences … Read more
Stripe has advanced LLM agents to the point of generating production-ready pull requests without human involvement in the code. The key question is how to maintain reliability as autonomy increases. The problem manifests at the intersection of scale and responsibility. The system generates code changes that serve payment infrastructure with high demands for correctness and … Read more
Agent-based systems are not limited by prompts, but rather by the economics and infrastructure of inference. Cloudflare is attempting to bridge this gap by integrating large open-source models directly into its edge platform.
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