10decoders
What Static RAG Actually Fails On
Static RAG works by retrieving a fixed number of chunks from a vector index in a single retrieval step, then passing them to the LLM. For questions with a clear, single-document answer ("What is our refund policy?" or "What does section 4.2 of the contract say?"), static RAG handles the task reliably. The failure mode appears on questions that require assembling evidence across multiple documents, following a reference chain, or reasoning conditionally about what to retrieve next.
A question like "Which of our suppliers had a quality non-conformance in the last quarter and also had a late delivery in the same period?" requires two separate retrievals with a logical join between them. Static RAG retrieves chunks most similar to the full query string and stops. The answer requires synthesizing results from two distinct retrieval paths. Standard vector similarity does not build that join. The LLM receives incomplete evidence and either fabricates the missing part or declines to answer.
On multi-hop question answering benchmarks, static RAG achieves around 34% accuracy. Agentic RAG, which embeds retrieval inside an iterative reasoning loop that decomposes the query, retrieves evidence incrementally, and refines subsequent retrievals based on what was found, reaches 89% on the same benchmark. That gap shows up in the use cases that actually matter for enterprise teams: legal research, clinical decision support, financial analysis, and compliance review, where the correct answer requires connecting information scattered across a corpus.
"Static RAG retrieves chunks most similar to the query and stops. Agentic RAG decides what to retrieve based on what was just found. The accuracy gap on multi-hop questions: 34% vs 89%."
Architecture Comparison: Which Retrieval Pattern Fits Your Query Distribution
Before moving to agentic retrieval, measure the proportion of multi-hop queries in your production logs. If fewer than 15% of queries require evidence from more than one document, static RAG with good hybrid search handles most use cases at significantly lower cost. The upgrade is justified by your query distribution, not by the sophistication of the architecture.
| Architecture | Query type handled | Multi-hop accuracy | Cost vs static RAG | When to use |
|---|---|---|---|---|
| Static RAG | Single-hop, direct lookup | ~34% on complex | Baseline | Standard Q&A on uniform corpora. Queries with well-contained answers in a single document or adjacent chunks. Under 15% multi-hop in production logs. |
| Agentic RAG (iterative, single-agent) | Multi-hop, conditional, ambiguous | ~89% on complex | 3–6x per query | Legal research, financial analysis, policy queries requiring 2 to 4 retrieval steps. When users ask comparative or conditional questions across document sets. |
| Multi-step planning RAG | Cross-domain, decomposed, high-stakes | Up to 94.5% | 8–15x per query | Compliance review, clinical decision support, multi-system research. Use cases where every retrieval step must be traceable and auditable. |
Not sure if your RAG pipeline needs agentic retrieval?
10decoders runs two-week RAG architecture assessments that classify your query distribution, measure multi-hop failure rates on your actual corpus, and determine whether agentic retrieval is justified before any architectural rebuild begins.
Book a Free AI Assessment →How to Make the Decision Without Rebuilding First
The correct decision path starts with measuring your current failure rate, not building a new architecture. Pull 50 to 100 recent production queries that users rated as unhelpful or wrong. Classify each failed query: single-hop failures, where the answer exists in one document but was not retrieved, versus multi-hop failures, where the answer requires combining evidence from multiple sources. Single-hop failures are fixed by improving vector search, chunking, or hybrid retrieval. Only multi-hop failures justify agentic retrieval.
Agentic retrieval carries costs that static RAG doesn't. Per-query spend climbs because each reasoning step calls the LLM independently: a 4-hop query costs roughly 4x the generation budget of a single-pass query. Latency grows too, typically adding 2 to 5 seconds per retrieval step at standard LLM response times. The third cost is harder to put a number on: an agent that selects its own retrieval steps creates a non-deterministic decision path, which makes audit trails genuinely difficult in regulated industries. Financial and medical teams often need a constrained agentic pattern with explicit step budgets and full step logging rather than a fully autonomous retrieval loop.
The practical upgrade path is incremental. Build a routing layer that classifies queries and sends multi-hop queries to a constrained agentic retrieval loop with a fixed step budget, typically 3 to 4 hops. Route single-hop queries to your existing static RAG pipeline. Measure answer quality separately for each route. Expand the agentic route only as the quality improvement justifies the additional cost. Routing all queries through agentic retrieval produces 3 to 15x cost-per-query multipliers without proportional gains on simple queries.
The 3-Stage Path to Production Agentic Retrieval
Measure and classify failures
Pull 50 to 100 failed or low-rated queries. Classify each as single-hop or multi-hop. Under 15% multi-hop: fix retrieval quality on static RAG first. Over 30%: agentic retrieval is likely justified. Build 10 annotated multi-hop test queries as your evaluation set.
Build a routing layer and constrained loop
Implement query classification to route multi-hop queries to an agentic retrieval loop with a fixed step budget of 3 to 4 hops. Run your annotated multi-hop query set through both architectures. Record accuracy, latency, and cost per query before deciding on broader rollout.
Instrument steps and set cost guardrails
Log every retrieval step: the query issued, chunks retrieved, and what the reasoning step concluded. Set cost-per-query alerts. For regulated industries, constrain the agent to a deterministic retrieval plan that satisfies audit requirements. Expand agentic coverage only as cost-quality metrics justify it.
"Agentic retrieval is justified by your query distribution, not by the architecture's sophistication. Measure multi-hop failure rates first. Build second."
What to Do This Week
01Pull 50 failed queries from your production logs
Find queries that users marked unhelpful, triggered a feedback submission, or where answer quality was manually flagged. Classify each failure: single-hop (answer exists in one document, wasn't retrieved) or multi-hop (answer requires combining evidence from multiple sources). This classification takes a day. It determines whether your problem is retrieval quality or retrieval architecture, and that distinction drives every subsequent decision.
02Build 10 representative multi-hop test queries
Write 10 questions that your users actually submit, where the correct answer requires following a chain of evidence across multiple documents. Annotate the correct source chunks for each. Run these through your current static RAG pipeline and record accuracy. This is your baseline for any agentic retrieval comparison. Without a domain-specific annotated test set, any quality comparison between architectures is anecdotal.
03Calculate the cost implication before building
Look up the per-query cost for your current RAG stack: embedding, retrieval, and LLM generation combined. Multiply by 3 to estimate agentic retrieval cost at 3 hops per query, and by 8 to 15 for a planning-based multi-step architecture. Apply these multipliers to your current query volume. That number should appear in the architecture decision document before any engineering begins, not after the first cost invoice from the agentic prototype.
04Design your query routing layer before writing agentic code
Before any agentic retrieval code is written, define the routing logic: what signals classify a query as multi-hop (query length, presence of "and both," "compare," "which also," conditional operators)? A keyword-plus-classifier approach is a practical starting point. Route flagged queries to a constrained 3-hop agentic loop and standard queries to your existing pipeline. Measure quality separately for each route from day one. The routing layer is the decision point the architecture depends on.
Let 10decoders assess whether your RAG pipeline needs agentic retrieval
10decoders runs two-week RAG architecture assessments that classify your query distribution, measure multi-hop failure rates against your annotated corpus, design a hybrid routing architecture, and determine whether agentic retrieval is cost-justified before any rebuild begins.