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.
When component specifications lag behind implementation, the team starts building the system based on assumptions. At Uber, this turned into a systemic, large-scale problem—and was solved through agent-based automation. The problem does not arise at the moment of writing specifications, but later—when the system begins to evolve faster than the documentation. The Uber Base design … Read more
Higress enters the CNCF Sandbox as an API gateway with the aim of consolidating multiple layers of traffic. The key question is whether this reduces complexity or merely shifts it elsewhere. Systems begin to degrade when the traffic management layer becomes fragmented. Ingress operates separately, the gateway for microservices operates separately, and solutions for AI … Read more
The increase in developer productivity has not led to a comparable acceleration of releases. The reason is that the bottleneck has moved higher up the stack: into the area of requirements formalization and result verification. With the advent of AI coding, teams expected a linear acceleration in delivery. In practice, only one stage sped up—the … Read more
As LLM production workloads grow, it becomes clear: classic Kubernetes mechanisms do not understand the nature of inference. llm-d is an attempt to bridge this gap at the platform level. The main limitation becomes apparent when inference goes beyond a “stateless HTTP service.” Requests to LLMs have different costs: prompt length, generation phase, KV-cache hits. … Read more
When LLM becomes part of production infrastructure, traditional monitoring is no longer sufficient. The bottleneck is no longer the application code, but the routing and model selection layer — and that’s exactly where observability is needed. In LLM systems, degradation doesn’t start with HTTP endpoint failures, but with the accumulation of subtle effects: increased latency … Read more
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
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