The utility of coding agents compounds with the quality of their feedback loop. In cloud-native systems, closing that loop involves solving two problems.
Benchmark scores predicted our LLM would succeed. It failed spectacularly. Here's why 92% vs 89% means nothing and what metrics actually matter in production.
Bandwidth — not compute — drives cloud costs. Optimize data movement, compression, and locality, or risk massive bills from hidden data transfer inefficiencies.
Multi-agent AI systems need built-in accountability. With the right logging and observability setup, when agents fail, you can see what happened and why.
This article explains how to turn privacy preference mismatches into a real compliance control using clear matching rules, safe fixes, and full auditability.
After 6 years of Go, always use painter receivers with mutexes, prefer composition over inheritance, and stop writing Java-style getters for everything.
AI-driven infrastructure is non-deterministic. Chaos testing ensures systems maintain intended behavior under stress, improving reliability and safety.
A Kubernetes pod may restart due to an OOMKill when the Java process exceeds the container’s memory limit. JVM memory tuning and correct resource limits prevent crashes.
This article explains a practical design for a LinkedIn-style “People You May Know” system, focusing on real-world tradeoffs, graph embeddings, and low-latency serving.
Autonomous agents fail by persisting: they retry, replan, and chain tools, increasing risk, cost, and potential blast radius without strict safety controls.