Claude Code Agent: LLM Architect — Design AI Systems

What it is

The LLM Architect agent is a Claude Code agent template specialized in designing LLM-powered application architectures. It helps with model selection, prompt pipeline design, RAG system architecture, and cost optimization. Once installed, it activates automatically when Claude Code detects architecture-related tasks in your project.

AI engineers and technical leads planning new LLM applications or refactoring existing ones benefit from having a structured approach to architecture decisions. The agent provides opinionated guidance on model selection trade-offs, embedding strategies, retrieval patterns, and token cost estimation.

How it saves time or tokens

Architectural decisions for LLM applications involve evaluating model capabilities, estimating token costs, designing retrieval pipelines, and planning for scaling. This agent encodes common patterns and trade-offs, so you get structured recommendations instead of starting from scratch. It reduces the research time needed to compare model providers, embedding dimensions, and retrieval strategies.

How to use

1. Install the agent template:

npx claude-code-templates@latest --agent ai-specialists/llm-architect --yes

1. Start Claude Code in your project directory. The agent activates when it detects architecture tasks.
2. Ask architecture questions:

> Design a RAG system for our customer support knowledge base
> Compare GPT-4o vs Claude Sonnet for our code review pipeline
> Estimate monthly token costs for 10K daily queries

Example

# Install the agent
npx claude-code-templates@latest --agent ai-specialists/llm-architect --yes

# In a Claude Code session:
> I need to build a document QA system that handles 50K PDFs.
  What architecture do you recommend?

# The agent will outline:
# - Document ingestion pipeline (chunking strategy, embedding model)
# - Vector store selection (based on scale and query patterns)
# - Retrieval strategy (hybrid search, reranking)
# - LLM selection for generation (cost vs quality trade-offs)
# - Caching layer for repeated queries

Related on TokRepo

• AI Agent Tools -- explore agent frameworks and templates for AI development
• RAG Tools -- discover retrieval-augmented generation tools and patterns

Common pitfalls

• The agent provides architectural guidance, not runnable code. Use it for planning and decision-making, then implement with appropriate libraries.
• Model pricing and capabilities change frequently. Verify the agent recommendations against current provider pricing pages before committing to a budget.
• RAG architecture depends heavily on your data characteristics. The agent asks clarifying questions about document types, query patterns, and latency requirements to give relevant recommendations.