Enterprise Knowledge Base Agent: RAG Architecture, Permissions & Citations

An enterprise knowledge-base Agent uses RAG (retrieval-augmented generation) to connect internal docs, wikis, policies, and product materials to LLMs: users ask in natural language; the system retrieves relevant chunks, then answers with citations. Use cases include internal IT/HR/finance assistants, sales playbooks, and support policy lookup. GeonAI delivers bespoke KB Agent design; /agents examples (e.g., knowledge base manager) are reference only. Assessment: [email protected], subject "Enterprise Agent inquiry". Presets are not public full trial.

Layers of a RAG architecture

Permission tiers: who can ask what

Enterprise KBs mix classification levels: public product FAQ, internal HR, manager-only comp policies. RAG must filter collections at retrieval by user identity—not hope the model stays quiet afterward. Map SSO/AD groups to partitions; filter by metadata tags (public/internal/confidential).

• Pre-retrieval filter: user token → allowed collections
• Generation guard: answer only from retrieved chunks; refuse or hand off if empty
• Audit: log hit document IDs and versions for compliance

Traceable answers: citation design

Production KB Agents should return traceable citations (doc name, section, paragraph, updated at). Support and audit need to verify policy sources. Acceptance: sample 20 questions—100% must map to source chunks or count as hallucination risk.

Parsing and chunking practices

1. Formats: prefer Markdown/wiki; structure PDFs; OCR scans with spot checks
2. Chunk size: often 300–800 tokens by heading; index tables separately or summarize
3. Metadata: source URL, owner, effective/expiry dates, language
4. Incremental updates: webhooks or schedules—avoid full rebuilds on every edit

Hybrid retrieval and reranking

Pure vector search misses SKUs, error codes, and internal abbreviations. Vector + BM25 hybrid with cross-encoder or LLM reranking is standard in 2026 enterprise RAG. Model choice (DeepSeek, GPT, Qwen) matters less than retrieval quality for many workloads.

Relation to customer service Agents

KB Agents often power support Agents as the knowledge layer: channels handle dialogue and handoff; KB governs policy and product tone. Internal-only pilots (IT helper, sales playbook) skip public IM. Start narrow—one dept, one doc class—then expand.

Acceptance metrics and launch checklist

• Accuracy: sampled Q&A vs gold answers
• Citation rate: answers with valid sources
• Refusal rate: correct refuse/handoff when no evidence
• Latency: P95 within SLA
• Permissions: 100% block on over-privilege test cases

Delivery follows GeonAI 4-step playbook; pricing drivers in six factors. Start via pricing or Live chat.