AutoRAG — Architect AI
AutoRAG (Architect AI) is an agentic system for designing, configuring, and evaluating RAG pipelines. It helps engineers compare chunking strategies, embedding models, retrieval approaches, and reranking configurations — using a defined evaluation harness to support evidence-based architecture decisions.
LangGraph agent
Approach
RAGAS framework
Evaluation
Designed & demonstrable
Status
Problem & use case
Enterprise teams building RAG systems face a combinatorial explosion of design choices — chunking, embedding, retrieval strategy, reranking, and LLM routing — with no principled way to evaluate tradeoffs before investing in implementation.
Solution & engineering approach
A LangGraph-based architect agent that interviews stakeholders, analyzes document corpus characteristics, generates a ranked architecture recommendation, scaffolds the pipeline code, and runs an automated evaluation suite before handoff.
Architecture & data flow
- 01
Agent interviews team on corpus type, query patterns, latency SLAs
- 02
Analyzes sample documents to recommend chunking + embedding strategy
- 03
Generates ranked architecture options with tradeoff explanations
- 04
Scaffolds retrieval pipeline code and evaluation harness
- 05
Runs automated RAG eval suite (RAGAS) and produces readiness report
Technology stack
Evaluation methodology
- Evaluation plan: compare chunking, embedding, retrieval, and reranking configurations against a defined evaluation dataset
- Measure retrieval relevance, answer faithfulness, citation correctness, and end-to-end latency
- Performance benchmarks will be published after reproducible evaluation on a documented dataset
Implementation status
Designed and demonstrable — implementation-specific benchmarks pending