Practical, opinionated recipes for building real things with generative AI — agent harnesses, spec-driven development, memory, and prompts that hold up in production.https://appliedgenai.io/https://appliedgenai.io/recipes/ai-native-product-architecture/https://appliedgenai.io/recipes/ai-native-product-architecture/Most 'AI features' are a chatbot bolted onto a CRUD app. An AI-native product is built the other way around — capabilities first, with the model in the critical path and the UI, data, and org reshaped to match. Here's the reference architecture.Tue, 09 Jun 2026 00:00:00 GMTenterprisearchitectureproductpatternshttps://appliedgenai.io/recipes/effective-agent-harness/https://appliedgenai.io/recipes/effective-agent-harness/An agent is a loop around a model, and the loop is the easy part. This is a deep dive into the part that actually matters: the guardrails, tracing, and control flow that let you leave it running.Tue, 09 Jun 2026 00:00:00 GMTagentsharnesspatternsreliabilityhttps://appliedgenai.io/recipes/evaluation-harness-for-trust/https://appliedgenai.io/recipes/evaluation-harness-for-trust/You can't improve — or safely ship — what you can't measure. This is how enterprises turn 'the demo looked good' into a regression-gated eval suite that tells you, before deploy, whether a change made the product better or worse.Tue, 09 Jun 2026 00:00:00 GMTenterpriseevalsreliabilitypatternshttps://appliedgenai.io/recipes/spec-driven-development/https://appliedgenai.io/recipes/spec-driven-development/Code is cheap now; intent is the bottleneck. Write a tight spec, let the agent implement against it, and gate the work with executable acceptance checks — so you review intent instead of archaeology.Tue, 09 Jun 2026 00:00:00 GMTsddworkflowpatternsreview