What sits in this layer
The application layer is the majority of the engineering work in a production AI system. The foundation model is one component among many.
Context engineering
Structuring the input — what enters the context window and in what form — is the single largest determinant of output quality. Retrieval, compression, formatting, ordering. Small changes here produce large downstream effects.
Retrieval architecture
Vector search, hybrid search, re-ranking, graph structures over documents, metadata filters. RAG is a category, not a technique. The right retrieval design depends on what the application is asking for, not on which vector database is trending.
Evaluation pipelines
An AI system without evals is a demo. We treat eval infrastructure as equal in importance to application code. Offline evals, online evals, regression evals, per-customer evals.
Tool use and orchestration
Modern applications use foundation models to call tools. Which tools, when, with what arguments, with what fallbacks when the tool fails — these are not prompt-engineering questions, they are systems-design questions.
Agentic workflows
Multi-step, multi-turn systems that plan, act, and recover. The hard problems are not "how to give the agent more capability" — they are "how to constrain the agent so its capability remains correct."
Failure engineering
Prompt injection, jailbreaks, overconfident hallucinations, context contamination. We treat these as engineering requirements with budgets, not as afterthoughts.