Most RAG (retrieval-augmented generation) systems work the same way: a user asks a question, the system runs one embedding search, pulls the top-k chunks, and hands them to the model. That works fine for simple Q&A. It breaks down the moment your knowledge base is large, evolving, and the questions require multi-step reasoning across many documents. LlamaIndex just shipped a blueprint for what comes next.
The Retrieval Harness
LlamaIndex has published legal-kb, a public reference application on GitHub described as a knowledge base for legal documents, powered by LlamaIndex Index v2 (the LlamaParse Platform). The real payload here is not the legal domain specifically. The project demonstrates a pattern the team calls a Retrieval Harness for agentic retrieval. The approach differs from single-shot retrieval: instead of one embedding search per query, an agent is given filesystem-style tools and can then crawl a large, evolving knowledge base to solve a task.
The harness provides a persistent data pipeline that can connect to a data source, index and update a large knowledge base, and expose a broad set of tools akin to filesystem operations. You can plug this into any of your agents to let them autonomously crawl an arbitrary knowledge base to solve a task with any complexity.
Four Tools, One Agent
The tools mirror operations engineers already know: semantic and keyword search, regex grep, file search, and read. Concretely, the agent gets:
- retrieve: hybrid semantic + keyword search over the index, with optional reranking and metadata filters
- findFiles: locate documents by name or glob pattern
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