A practical blueprint of safety patterns, architecture, and policies to keep agentic AI reliable, secure, and aligned in production.
A practical blueprint for building scalable, safe AI support chatbots—from NLU and RAG to orchestration, guardrails, and observability.
A practical guide to structured API logging and observability: schemas, tracing, metrics, correlation IDs, pipelines, and cost-efficient practices.
A practical blueprint for deploying autonomous AI agents to production—architecture, safety, reliability, evals, cost control, and ops patterns.
A practical guide to GraphQL error handling: schema design, HTTP codes, partial data, masking, client patterns, observability, and examples.
A practical guide to function calling vs. tool use in LLMs: architectures, trade-offs, design patterns, reliability, security, and evaluation.
