Autonomous RAG & Code Intelligence Agent
Agentic retrieval-augmented generation engine with LangChain and LangGraph for autonomous code analysis, semantic search, and multi-step reasoning.
System Overview
Modern development stacks produce vast codebases requiring contextual understanding beyond static search. This autonomous agent coordinates multi-step retrieval and code reasoning over private repositories.
Generic LLM assistants hallucinate API boundaries, lack awareness of custom microservices architectures, and struggle with multi-repository context retrieval.
Constructed a ReAct agentic workflow with LangGraph, featuring semantic AST chunking, vector embedding indexing, and iterative tool verification loops to ground answers in source code.
Key Technical Capabilities
- ✓Dynamic tool execution using LangGraph ReAct loop for multi-step reasoning.
- ✓High-dimensional vector embeddings and cosine reranking for precision retrieval.
- ✓Zero-hallucination guardrails checking synthesized answers against ground-truth references.
- ✓Streaming response generation with sub-300ms time-to-first-token.
- ✓LangSmith tracing for end-to-end prompt observability and evaluation.
Hard Problems Tackled
Preserving context windows during multi-file repository exploration without losing critical import statements.
Designing deterministic fallback tools when initial retrieval similarity scores fall below confidence thresholds.
Achieved 94% retrieval accuracy across proprietary codebases and reduced developer debug search time by over 60%.