Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG

2026-04-16 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

Corpus2Skill is a novel approach that transforms a document corpus into a hierarchical skill directory, enabling LLM agents to navigate enterprise knowledge more effectively than traditional Retrieval-Augmented Generation (RAG) systems. Unlike passive RAG, Corpus2Skill allows agents to understand corpus organization, backtrack from unproductive search paths, and combine scattered evidence. Its offline compilation pipeline iteratively clusters documents, generates LLM-written summaries at each hierarchical level, and materializes the structure as a tree of navigable skill files. At serve time, the agent gains a bird's-eye view, drills into topic branches via progressively finer summaries, and retrieves full documents by ID. This method significantly outperforms dense retrieval, RAPTOR, and agentic RAG baselines on the WixQA enterprise customer-support benchmark across all quality metrics.

Key takeaway

For AI Architects designing enterprise RAG systems, consider adopting a navigation-based approach like Corpus2Skill to overcome the limitations of passive retrieval. Your systems can achieve superior performance on complex QA tasks by providing agents with an explicit, navigable knowledge hierarchy, allowing for more intelligent evidence combination and path correction. This shift enhances accuracy and efficiency in enterprise customer support and similar applications.

Key insights

Corpus2Skill distills document corpora into navigable hierarchical skills, enabling LLM agents to actively explore and combine evidence.

Principles

• Explicit hierarchy improves agent reasoning.
• Summarization aids navigation at multiple granularities.
• Offline compilation enhances serve-time efficiency.

Method

Corpus2Skill iteratively clusters documents, generates LLM-written summaries for each level, and materializes a tree of navigable skill files for agent exploration and retrieval.

In practice

• Implement hierarchical knowledge structures.
• Use LLMs for multi-level summarization.
• Enable agents to backtrack search paths.

Topics

• Corpus2Skill
• Retrieval-Augmented Generation
• LLM Agents
• Enterprise Knowledge
• Hierarchical Skill Directory

Best for: AI Architect, AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.