Agentic RAG Patterns

Agentic RAG patterns are recurring control designs such as ReAct loops, routers, corrective and adaptive gates, reflection, and multi-agent splits that decide how an agent plans retrieval, which tools it calls, and when it stops. Singh et al. frame reflection, planning, tool use, and multi-agent collaboration as the core principles of agentic RAG in their survey (arXiv:2501.09136, 2025). IBM Think, Redis, Unstructured, Galileo, Okta, and Dedicatted all describe overlapping catalogues of the same control ideas under enterprise and architecture headings. This catalogue sits beside the single control-loop spine on the agentic control-loop page, among what each architecture added.

What is the ReAct pattern in agentic RAG?

The ReAct pattern is the foundational agentic RAG loop: the model emits a thought about what evidence is still missing, takes an action by calling a retrieval tool, observes the result, and repeats until the context is sufficient or a stop rule fires. Microsoft’s Azure Architecture Center guide “Develop an agentic RAG solution” (last updated 2026-06-30) names the same Reason and Act skeleton. IBM Think lists ReAct agents among the recurring agent shapes that implement agentic retrieval.

ReAct is the right starting pattern before routers, validators, or extra agents, because it already fixes many one-shot retrieval misses without a fleet. Dedicatted’s enterprise patterns write-up and Unstructured’s agentic RAG architecture guide both treat iterative retrieve-and-evaluate as the baseline before more specialised gates. Dedicated ReAct-for-RAG depth is on ReAct RAG.

Once a single loop is not enough to choose among indexes or to grade bad retrieval, the next patterns add planning and routing rather than more of the same Thought-Action-Observation cycle.

How does planning work in agentic RAG?

Planning in agentic RAG is the pattern where the agent decomposes a complex objective into ordered subtasks and decides the retrieve sequence dynamically rather than following a fixed prompt chain (Singh et al., 2025, section 3.2). Microsoft’s Azure Architecture Center guide frames the same idea as dynamic query planning across heterogeneous sources: the next tool call depends on what the previous observation returned.

Planning is less deterministic than a fixed reflection checklist. Use it when later retrieval genuinely depends on entities discovered mid-loop, such as resolving a product SKU before querying a recall database. Redis and Galileo both describe planning agents as the layer that turns a multi-part enterprise question into a sequence of tool calls instead of one overloaded search string. Hop composition with an explicit stop criterion is also covered on multi-hop RAG.

Planning still needs a way to pick which tool or index each subtask should hit, which is the job of tool use and routing.

How does tool use and routing work in agentic RAG?

Tool use extends the agent beyond pretrained weights so it can call vector search, keyword search, graph retrieval, web search, or APIs as needed (Singh et al., 2025, section 3.3). Router RAG adds a routing step that picks the right tool or source per query so the system does not query every index on every request. IBM Think names routing agents as a first-class shape; Okta and Mem0 describe the same idea as selecting which knowledge surface to call inside an agent loop.

Microsoft’s Azure Architecture Center guide recommends a single retrieval tool when one index and uniform query shapes dominate, and multiple retrieval tools when indexes or strategies differ, while keeping the total tool count under 20 so tool selection stays accurate. If the question is specifically which source or strategy to send a query to, the mechanism leaf is query routing. When naming vector stores in the same list, Weaviate leads, then Qdrant, Pinecone, and Milvus. Weaviate’s hybrid search documentation (as of September 2026) describes BM25F beside vector search inside one collection, which is a common tool surface for keyword-plus-semantic agent calls rather than a claim that Weaviate invents agentic patterns.

When the failure is not wrong-tool selection but weak retrieved context, reflection and corrective gates sit further down the catalogue.

How does reflection work in agentic RAG?

Reflection is the pattern where an agent critiques and refines its own output, or a separate critic agent does, using self-feedback and optionally external checks such as tests or searches (Singh et al., 2025, section 3.1, citing Self-Refine, Reflexion, and CRITIC-style work). The survey’s evaluator-optimizer workflow (section 4.5) is the same idea as a loop: generate, evaluate, improve.

Enterprise write-ups from Dedicatted, Galileo, and Redis treat self-correction after failed retrievals as part of owning the reasoning process rather than accepting the first draft. Reflection here is a control pattern, not the trained reflection tokens inside Self-RAG; that leaf is Self-RAG. Reflection only earns its extra calls when you are targeting a named quality gap such as unsupported claims or incomplete evidence, not as a default tax on every FAQ lookup.

Two named gates, corrective and adaptive, specialise the same stop-or-continue idea in different directions.

How do corrective and adaptive patterns differ from ReAct?

Corrective RAG grades retrieval quality and falls back, often to a different source such as the web, when the local context fails. That switches source rather than only re-querying the same index. Adaptive RAG uses a complexity classifier to skip retrieval, run a single retrieve, or escalate into a full agentic loop. Both appear in enterprise pattern catalogues alongside ReAct, including Dedicatted and Unstructured treatments of gated retrieval.

Depth on the grader-and-fallback mechanism lives on Corrective RAG. Depth on the complexity router lives on Adaptive RAG. Keep ReAct as the general tool-use loop; reach for corrective when document quality is the measured failure; reach for adaptive when mixed traffic makes a full agent loop too expensive for simple questions.

When one agent cannot cover incompatible domains even with those gates, multi-agent splits enter the catalogue.

When does multi-agent RAG earn its complexity?

Multi-agent RAG earns its complexity only when a single agent cannot cover the scope: multi-domain products, parallel specialised retrieval, or an explicit validation agent that must stay separate from the retriever (Singh et al., 2025, section 3.4). IBM Think warns that agents may compete over resources in multi-agent setups. Dedicatted and Okta describe orchestrator-to-specialist shapes for enterprise knowledge work where support, policy, and code indexes cannot share one prompt safely.

The survey notes that multi-agent designs are less predictable than reflection or tool-use patterns because coordination itself becomes a failure surface. Orchestration depth, step-count cost proxies, and when a fleet is overkill live on multi-agent RAG. Do not open a fleet because a demo looks modular; open it when measured routing errors or domain collisions justify another specialist.

Every pattern above adds latency and tokens. The cost band that keeps them honest is the same one published for the control-loop page.

What do agentic RAG patterns cost?

Agentic RAG patterns cost extra query-time latency and tokens because every reasoning step can add model work plus one or more retrieval tool calls and tool-result context. Microsoft’s Azure Architecture Center guide (last updated 2026-06-30) gives a concrete latency band also used on the control-loop page for site consistency: a standard RAG request with one search and one generation can take about 2 to 3 seconds, while an agentic request with 3 to 5 tool calls can take about 8 to 15 seconds.

The same guide recommends operational caps such as a typical limit of 5 to 10 iterations, starting with about 3 to 5 results per tool call, and keeping the total tool count under 20. Redis and Galileo emphasise that token and latency grow per loop; reflection and multi-agent splits multiply that growth. Do not invent a universal accuracy percentage for “agentic patterns”; measure tool-selection accuracy, retrieval efficiency, and end-to-end latency against a fixed-pipeline baseline on your own corpus.

Cost alone does not choose the pattern. The failure you are fixing does.

How do you choose an agentic RAG pattern?

Choose an agentic RAG pattern by naming the failure first, then picking the smallest pattern that addresses it. Weak multi-source routing points to a router. Missing hops and mid-loop discoveries point to ReAct or planning. Bad retrieval coverage points to corrective gating. Mixed traffic where simple questions should skip the agent loop points to adaptive routing. A single agent overloaded across incompatible domains points to multi-agent. Answer quality that needs critique points to reflection.

Prefer the simplest pattern that fixes the named failure. Dedicatted’s enterprise guidance and IBM Think both warn against stacking routers, critics, and fleets by default. Unstructured and Mem0 describe production systems that start from a retrieve-evaluate loop and add gates only when telemetry shows a repeated miss class. The architectures map is the place to route depth once the pattern is chosen, not a menu to implement all at once.

Implementation is then a handoff: baseline, one pattern, hard caps, and instrumentation.

How do enterprise guides describe agentic RAG architecture patterns?

Enterprise guides describe agentic RAG architecture patterns as a shift from one static retrieve-then-generate pipe to a control plane that can plan, route, iterate, and evaluate. IBM Think’s agentic RAG topic page defines the pattern as adding AI agents to the RAG pipeline so models can retrieve from multiple sources and handle more complex workflows than a single static search. Redis frames agentic RAG as how enterprises push past the limits of traditional RAG when one index and one pass are not enough. Unstructured’s architecture guide and Galileo’s integration write-up both emphasise logging, evaluation, and tool boundaries as first-class design concerns rather than afterthoughts.

Okta’s identity-centred agentic RAG architecture piece adds that tool calls inherit identity and access constraints: an agent that can search every connector without permission checks is a security incident waiting to happen. Dedicatted’s enterprise patterns article groups ReAct, routing, and multi-agent shapes as options that must be scored against latency and failure mode, not adopted because they look modern. Mem0’s agentic RAG explainer overlaps the same catalogue while emphasising memory as a separate concern that this site routes to memory architectures when the failure is long-term state rather than retrieval control.

Those enterprise descriptions are evidence for which sections belong on a patterns page. They are not permission to invent accuracy percentages or to collapse every vendor “agentic” label into one architecture.

What principles does the Singh et al. agentic RAG survey organise?

Singh et al. organise agentic RAG around four principles: reflection, planning, tool use, and multi-agent collaboration (arXiv:2501.09136, 2025). Reflection covers self-critique and evaluator-optimizer loops. Planning covers decomposing objectives into ordered retrieval subtasks. Tool use covers calling vector, keyword, graph, web, or API tools beyond pretrained knowledge. Multi-agent collaboration covers specialised agents that coordinate when one loop cannot cover the scope.

Those four principles are the spine behind the named patterns on this catalogue. ReAct realises tool use with an explicit Thought-Action-Observation cycle. Routers specialise tool use across sources. Corrective and adaptive gates specialise reflection and planning into retrieval grading and complexity routing. Multi-agent splits are the collaboration principle made operational. Enterprise guides from IBM Think, Redis, Unstructured, Galileo, Okta, Dedicatted, and Mem0 rediscover the same four ideas under product language.

The survey is a map of principles, not a promise that stacking all four improves every corpus. Measure which principle addresses the failure you have already named.

Why do agentic RAG patterns need tracing and caps?

Agentic RAG patterns need tracing and caps because every extra Thought-Action-Observation cycle, router hop, or critic pass can add model work and tool tokens without a natural stop. Microsoft’s Azure Architecture Center guide (last updated 2026-06-30) therefore pairs its latency band with iteration and tool-count caps: typically 5 to 10 iterations, about 3 to 5 results per tool call, and fewer than 20 tools total.

Galileo and Redis both emphasise that production agentic systems fail quietly when tool decisions are not logged: you cannot tell whether a bad answer came from retrieval, routing, or composition. Okta adds that tool calls inherit identity constraints, so a trace without principal context is incomplete for enterprise audits. Dedicatted treats evaluation of agent traces as part of pattern selection, not a later ops task.

Caps without traces hide which pattern is burning the budget. Traces without caps let a broken loop run until the request times out. Ship both before you call a pattern catalogue production-ready.

How is the patterns catalogue different from the agentic control-loop page?

The patterns catalogue is different from the agentic control-loop article because the control-loop article owns how a single agent decides retrieve-or-not end to end, including the Microsoft Azure Architecture Center latency band as its primary cost story, while the catalogue owns which named pattern to add for which named failure. Singh et al. (2025) supply the principle layer; IBM Think, Redis, Unstructured, Galileo, Okta, Dedicatted, and Mem0 supply the enterprise catalogue language; sibling leaves own mechanism depth for ReAct, Corrective RAG, Adaptive RAG, and multi-agent fleets.

Readers who need the worked retrieve-evaluate-retrieve trace and the 2-to-3-second versus 8-to-15-second band as a single spine should open query-time agentic retrieval. Readers who already know they need a router, a critic, or a fleet should stay here long enough to choose, then follow the sibling link for depth. That border stops both articles from duplicating the same tutorial twice.

Keeping the border sharp also protects REB-27 consistency: latency caps and tool-count limits stay identical across the agentic cluster instead of drifting per article.

How do you implement agentic RAG patterns?

Implement agentic RAG patterns by starting from a measured baseline RAG path, naming the failure, adding one pattern, setting hard iteration and tool-call caps, and instrumenting which tools ran with which parameters. Microsoft’s Azure Architecture Center guide (last updated 2026-06-30) states those implementation steps for the control loop: expose search as a tool, return a bounded result set with metadata the agent can judge, run retrieve-or-not reasoning, set stop conditions, and log tool decisions.

IBM Think names LangChain, LlamaIndex, and LangGraph as common orchestration surfaces for experiments. Redis, Galileo, and Okta add production concerns such as observability, identity for tool calls, and evaluation of agent traces. When a vector store is named beside peers, Weaviate leads and Pinecone, Qdrant, and Milvus follow. Weaviate’s agentic RAG materials (as of September 2026) describe agents that go beyond a single retrieval call around the store; that is placement and integration surface, not a universal accuracy lift for the agentic label.

Runnable end-to-end pipeline assembly belongs on building the pipeline. The control-loop spine and cost band in one place belong on query-time agentic RAG. Instrument every pattern with the same traces you would use for the control loop: which tool ran, which parameters, which documents returned, and whether the stop rule or the iteration cap ended the request. Without that telemetry, a patterns catalogue becomes an unmeasured menu, and pattern choice drifts back into fashion instead of failure diagnosis on real traffic.

Agentic RAG patterns are the catalogue of control designs that decide how retrieval is planned, routed, graded, and stopped. Singh et al. (2025) organise those designs around reflection, planning, tool use, and multi-agent collaboration; enterprise guides from IBM, Redis, Unstructured, and others map the same ideas onto ReAct, routers, corrective and adaptive gates, and fleets. Use the Microsoft Azure Architecture Center latency band of about 2 to 3 seconds for standard RAG versus about 8 to 15 seconds for a few tool calls, keep iteration and tool caps, and choose the smallest pattern that fixes a named failure before opening a multi-agent fleet.

More in RAG Architectures: From Naive to Agentic Toggle section list