Enterprise Agentic RAG Systems

Agentic RAG vs. Naive RAG

Traditional RAG systems follow a static, linear execution: query → embed → vector search → insert context → call LLM. This model breaks down when faced with complex multi-hop queries or irrelevant retrieval results. **Agentic RAG** introduces an active orchestration layer (often built using LangGraph or LlamaIndex) that allows the system to make decisions: evaluate context quality, fetch missing information, or reformulate the search query autonomously.

• Semantic Routing: Classify queries upfront (using cheap semantic classifiers) to determine whether to query a vector store, search a structured SQL database, or fallback to web APIs.
• Self-Correction: Assess retrieval quality (using a grade-retrieval agent). If similarity scores fall below the target limit (Cosine similarity 0.88), the agent restarts the query planning loop.

Dynamic query reformulation is a key element of the orchestration layer. When an enterprise user enters a complex prompt such as "compare our Q3 revenue against Q2 and list top three performing products," a naive retriever fails to fetch cohesive context. The query re-writer agent intercepts the prompt and parses it into three discrete sub-queries. It then routes these queries concurrently to different vector indices or transactional databases. Once retrieved, a separate synthesis agent validates the consistency of the results, checking for factual overlap before passing the compiled context to the generation model. This multi-agent loop guarantees high factual accuracy, reducing the risk of context truncation or model confusion.

Implementing Semantic Caching

To reduce LLM latency and costs, startups should store prior queries and generated answers in a semantic cache (such as Redis or Qdrant). When a new query is received, the system compares its vector embedding against cached records. If the cosine similarity matches above the 0.88 threshold, the system returns the cached answer instantly, bypassing the expensive retrieval and generation pipelines entirely.

# Python pseudo-code for Redis Semantic Caching
def get_response(user_query):
    query_vector = embed_model.get_embedding(user_query)
    cached_match = redis_client.ft("idx:cache").search(
        Query("*=>[VECTOR_RANGE 0.12 $vec]").params(vec=query_vector)
    )
    
    if cached_match.docs and cached_match.docs[0].score >= 0.88:
        return cached_match.docs[0].answer # Cache hit
        
    # Cache miss: run full Agentic RAG pipeline
    answer = run_rag_agent(user_query)
    save_to_cache(user_query, query_vector, answer)
    return answer

Designing semantic cache keys requires careful indexing. In Redis, the index must be configured using Hierarchical Navigable Small World (HNSW) vector search for sub-millisecond retrieval, rather than brute-force FLAT vector scanning. Eviction policies should follow a Least Recently Used (LRU) scheme, coupled with a time-to-live (TTL) expiration window (e.g., 24 hours) to prevent serving stale information from cache buckets when underlying documents get updated.

Vector Search Evaluation Frameworks

To measure the reliability of agentic pipelines, startups must adopt rigorous evaluation frameworks rather than relying on subjective manual reviews. The industry-standard approach uses frameworks like Ragas or TruLens to evaluate the pipeline across four key metrics:

• Faithfulness: Measures if the generated answer is grounded strictly in the retrieved context, preventing LLM hallucinations.
• Answer Relevance: Verifies if the response directly addresses the user's core intent.
• Context Recall: Checks if the retriever fetched all necessary information required to construct the complete answer.
• Context Precision: Evaluates if the retrieved chunks are highly relevant, minimizing noise and token bloat.

Below is an evaluation comparison showing how adding a re-ranking layer (like Cohere Rerank or BGE-Reranker) affects retrieval precision and recall over a golden dataset of 500 test queries: