Agentic RAG Patterns: When Retrieval Needs a Decision Loop
Standard RAG retrieves once and generates once: a query comes in, chunks come back, an answer goes out, in a single fixed pass. Agentic RAG replaces that fixed pass with a decision loop, letting the model decide whether the retrieved context is sufficient, rewrite the query and retrieve again if it is not, call a different tool entirely if the question is not actually a retrieval question, or decompose a multi-part question into sub-queries answered separately and combined. This matters for the kind of layered questions manufacturing and ERP users actually ask, things like compare this quarter's scrap rate to last quarter and explain what changed, which no single retrieval pass answers well. The cost is real: every additional loop iteration adds an LLM call and a retrieval round-trip, so agentic RAG needs guardrails or it becomes slow and expensive on queries that never needed the loop in the first place.
Query Rewriting and Decomposition
Users rarely phrase questions the way a retriever needs them phrased: a vague follow-up question in a conversation, what about the other one, has no retrievable meaning on its own without context from earlier turns. Query rewriting has the model reformulate the user's question into a retrieval-optimized query before searching, incorporating conversational context and clarifying ambiguous references. Query decomposition goes further for genuinely multi-part questions, splitting compare this quarter's scrap rate to last quarter and explain what changed into two separate retrievals, one per time period, executed independently and then combined during generation, which retrieves more precisely than a single search for the combined question ever would.
Iterative Retrieval and Self-Correction
After an initial retrieval pass, an agentic system can evaluate whether the returned context actually looks sufficient to answer the query, using either a dedicated grading step or the generation model's own confidence signal, and if not, retrieve again with a refined query, a different chunking granularity, or a different source entirely. This self-correction loop is what lets a system recover from a first retrieval pass that returned technically relevant but insufficiently specific results, rather than generating a vague answer from thin context. It needs explicit stopping conditions: a maximum iteration count, typically two to four, and a confidence threshold below which the system stops looping and either answers with its best available context or explicitly tells the user it could not find sufficient information.
- Grade retrieved context for sufficiency before generating, not just retrieve and answer blindly
- Retry with a refined query or different retrieval strategy when context is judged insufficient
- Cap iterations at two to four and define an explicit stopping condition to avoid runaway loops
Tool-Calling Agentic RAG: Retrieval as One Tool Among Several
In many enterprise deployments, retrieval is not the only capability a query might need: a question might require a live ERP query for current inventory, a calculation, or a document search, and the right answer is for the agent to decide which tool or tools apply rather than forcing every query through a fixed retrieval step regardless of relevance. This pattern treats the RAG retriever as one tool in a broader toolset alongside ERP query functions and calculators, letting the model route queries appropriately, which is the same architecture Netray uses when building ERP-aware agents that need to combine document knowledge with live transactional data rather than treating them as separate systems the user has to reconcile manually.
The Cost and Latency Tradeoff of Agentic Loops
Every additional loop iteration adds a full LLM call plus a retrieval round-trip, so a query that resolves in one straightforward pass under standard RAG might take three to four times longer and cost three to four times as much under an agentic architecture that always loops regardless of need. The practical fix is routing: use a lightweight classifier or the model itself to judge upfront whether a query is simple (single retrieval suffices) or complex (looping is likely needed), and only invoke the full agentic loop for the latter. Always cap maximum iterations and set a hard timeout with a graceful fallback to a best-effort answer, so a pathological query cannot loop indefinitely and degrade the user experience or run up an unbounded bill.
How Netray Designs Agentic RAG for Enterprise Use Cases
Netray reserves agentic RAG patterns for the specific query types that genuinely need them, complex multi-part ERP and document questions, rather than applying the loop universally and paying the latency and cost tax on every simple lookup. Our DataRay agentic mode routes queries by complexity, applies query decomposition and iterative retrieval only where the routing decision calls for it, and includes the loop itself in the same evaluation harness used for standard retrieval, measuring iteration count, cost per query, and accuracy so the added complexity is justified by measured results rather than assumed.
Frequently Asked Questions
What is agentic RAG and how does it differ from standard RAG?
Standard RAG retrieves once and generates once in a fixed sequence. Agentic RAG lets the model decide whether to retrieve, rewrite the query, retrieve again, decompose a multi-part question, or call a different tool entirely, in a loop, before producing a final answer. It handles layered and multi-part questions that a single fixed retrieval pass cannot answer well, at the cost of added latency and per-query expense from the extra iterations.
When do you need agentic RAG instead of standard RAG?
When a meaningful share of real queries are multi-part, require comparing information across sources or time periods, or need a mix of document retrieval and live data lookup that a single retrieval pass cannot satisfy. Most straightforward fact-lookup queries are answered faster and more cheaply by standard RAG, so route by query complexity rather than applying agentic loops universally across all traffic.
How many retrieval iterations is reasonable in an agentic RAG loop?
Cap iterations at two to four in most production systems, paired with a confidence threshold or sufficiency check that stops the loop early once retrieved context is judged adequate. Uncapped iteration counts risk runaway latency and cost on queries the system cannot resolve, so always define an explicit stopping condition and a graceful fallback that returns a best-effort answer rather than looping indefinitely.
Does agentic RAG cost significantly more than standard RAG?
Per query that requires looping, yes, often three to four times the cost and latency of a single-pass retrieval, since each iteration adds a full LLM call and retrieval round-trip. The practical mitigation is routing: classify query complexity upfront and only invoke the agentic loop for queries that actually need it, keeping simple lookups on the cheaper standard RAG path.
Key Takeaways
- 1Query Rewriting and Decomposition: Users rarely phrase questions the way a retriever needs them phrased: a vague follow-up question in a conversation, what about the other one, has no retrievable meaning on its own without context from earlier turns. Query rewriting has the model reformulate the user's question into a retrieval-optimized query before searching, incorporating conversational context and clarifying ambiguous references.
- 2Iterative Retrieval and Self-Correction: After an initial retrieval pass, an agentic system can evaluate whether the returned context actually looks sufficient to answer the query, using either a dedicated grading step or the generation model's own confidence signal, and if not, retrieve again with a refined query, a different chunking granularity, or a different source entirely. This self-correction loop is what lets a system recover from a first retrieval pass that returned technically relevant but insufficiently specific results, rather than generating a vague answer from thin context.
- 3Tool-Calling Agentic RAG: Retrieval as One Tool Among Several: In many enterprise deployments, retrieval is not the only capability a query might need: a question might require a live ERP query for current inventory, a calculation, or a document search, and the right answer is for the agent to decide which tool or tools apply rather than forcing every query through a fixed retrieval step regardless of relevance. This pattern treats the RAG retriever as one tool in a broader toolset alongside ERP query functions and calculators, letting the model route queries appropriately, which is the same architecture Netray uses when building ERP-aware agents that need to combine document knowledge with live transactional data rather than treating them as separate systems the user has to reconcile manually..
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Terms used in this article
Have questions your users ask that a single retrieval pass can't answer? Netray builds agentic RAG loops scoped to the queries that actually need them, not applied blindly everywhere.
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