Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Design robust health check and readiness probe endpoints for AI agent services that verify dependencies, enable graceful startup and shutdown, and integrate with container orchestrators.
Build comprehensive health monitoring for AI agent systems that checks LLM providers, databases, and tool integrations. Learn health check patterns, dependency graphs, degraded state detection, and alerting.
Build Helm charts for AI agent deployments — including chart structure, values files, Go templates, dependencies, and chart repositories for reusable, parameterized Kubernetes manifests.
Learn how to build hierarchical multi-agent systems where orchestrators manage sub-orchestrators, each leading specialized teams, enabling recursive task decomposition for large-scale workflows.
Learn how to design a three-tier memory architecture for AI agents with working memory, short-term buffers, and long-term stores, including promotion rules, eviction policies, and retrieval priority.
Understand how high-bandwidth interconnects enable multi-GPU and multi-node AI training, the differences between interconnect technologies, and why network topology determines training efficiency.
Achieve up to 5x throughput improvements for agentic AI workloads with proven inference optimization patterns including batching, caching, and parallel execution.
A comprehensive guide to building HIPAA-compliant AI agents covering encryption, access controls, audit logging, Business Associate Agreements, data retention policies, and breach notification procedures.
Configure Kubernetes Horizontal Pod Autoscaler for AI agent workloads using CPU, memory, and custom metrics. Learn KEDA integration and scale-to-zero patterns for cost optimization.
