Claude Solution Architecture Patterns - Hedge Funds

COMMON SOLUTION ARCHITECTURES

This short course/presentation showcases some general patterns, for production architectures, extracted from enterprise implementations, with clients, of Claude-based solutions.

We'll cover production-grade solution patterns, with Claude, for some key use cases:

• A;

For each use case, we will analyze five key components:

• The overview. The use case, required goals and success criteria, general architecture snapshot (with agentic and/or local workflows, tools, skills, key contextual information, validation methods, and telemetry metics), as well as the top failure modes;

• Agentic orchestration patterns, OR local workflows. For agentic-based workflows, we'll cover sub-agents, specific orchestration, tools to use, which steps are sequential/parallel/multi-pass, pre-tool use and post-tool use hooks, step prerequisites and structural validation, as well as alternative pathways with errors. For local workflows, we'll cover CLAUDE .md guidance (project-level and user-level), specific skills that can be used (with required arguments, permissions and whether to fork context or not), and specific workflow modalities (Plan Mode vs. Direct Execution, Batch API, etc);

• Context management patterns. We'll cover which information can be summarized and which should be kept in persistent storage, how frequently to summarize the context and what may be lost, key outputs to validate and the specific shapes that hallucinations take, and more;

• RAG and knowledge patterns. We'll cover key materials to include with RAG, whether to use dense/sparse/hybrid representation and its consequences, the recommended level of approximation in indexing (Approximate Nearest Neighbors or ANN), key chunk fields and "chunk roles" to include, and how to optimize reranking based on metadata fields;

• Observability and cost patterns. Key structural indicators to save, both in terms of workflows (workflows started, completed, with/without errors, etc) and RAG/context ones (when did we retrieve correctly, what type of bias may we have, blind spots, etc), optimizing inputs with few-shot examples and their characteristics, optimizing outputs by trimming unneeded tool outputs and using context forking, as well as what workflow steps to use low-power/medium-power models for, and how frequently to run the workflow;