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An agentic RAG pipeline combines an authorized internal knowledge base with live web search, then decides which evidence to retrieve, checks whether it is sufficient, and generates an answer with traceable citations. Build it as two connected systems: an offline pipeline that maintains the corpus and an online workflow that routes questions, retrieves evidence, and stops when it has enough—or cannot support an answer.
What makes a RAG pipeline agentic?
Retrieval-augmented generation (RAG) supplies a language model with passages retrieved from an indexed corpus. An agentic system adds a control loop: it can plan what evidence it needs, choose a retrieval tool, inspect the results, and try again when a specific gap remains. Planning, reflection, and tool use are recognized agentic patterns in a 2025 survey of the field.
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That loop is useful when a question may require several searches or sources—for example, comparing a current public announcement with an internal policy. It is unnecessary overhead for a simple question that one authorized corpus search can answer. Start with deterministic routing and a one-shot retrieval path; make iterative search an option for tasks that benefit from it. Agentic behavior can improve evidence gathering, but it does not make retrieved material reliable by itself or guarantee a correct answer.
How do I build an agentic RAG pipeline with real-time web search?
Separate the architecture into an offline document-preparation path and an online question-answering path. The first keeps internal evidence searchable and permission-aware. The second selects internal retrieval, web search, or both, then checks and cites the evidence used.
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- Define the evidence routes. Classify questions by freshness need, internal-corpus scope, and task complexity. Route internal policy questions to authorized corpus retrieval, current public-information questions to web search, and questions needing both to both sources. Keep a direct one-search route for straightforward questions.
- Build and maintain the internal index. Connect approved sources, parse and normalize their content, preserve document structure and provenance, divide content into retrieval units, generate embeddings, and create or update the index. Process source updates and deletions so indexed content reflects the current authorized corpus.
- Retrieve internal evidence with access controls. Search using the user’s question, filter by tenant, user permissions, and corpus scope before any passage reaches generation, and return a compact set of relevant passages with source identifiers, locations, timestamps, and authorization metadata.
- Expose live web search as a separate tool. Call it when the answer needs current public information or internal evidence is insufficient. Retain the actual result URLs and citation metadata returned by the search integration.
- Inspect the evidence and decide whether to continue. Check relevance, authority, recency, and coverage. If a specific gap remains, reformulate a query or call another source within a bounded retry budget. Otherwise, generate the answer; if evidence remains inadequate, report the uncertainty rather than filling the gap with a guess.
- Render claims with their sources. Keep retrieved evidence separate from the generated answer in application state. Attach citations to the material claims they support, and preserve each citation’s source identity through the response pipeline.
- Evaluate, secure, and monitor the system. Test retrieval and answers against representative questions, test authorization boundaries and prompt injection, and monitor quality alongside latency and tool-call cost.
Google Cloud’s RAG reference architecture describes ingestion, embedding generation, and index creation or updating as parts of the RAG subsystem. Online retrieval then supplies context to generation. Treat these as separate operational responsibilities: an agent cannot retrieve a document that was never ingested, and a well-maintained index cannot decide when public information is needed.
How should I prepare the internal corpus?
Keep provenance and permissions attached
For each source document, track its identity, owner or access policy, update time, and original location. When parsing and normalizing content, preserve useful headings and structure. Each retrieval unit should point back to its source document and location, with the authorization context intact. Apply updates and deletions to the index as source material changes.
Chunking is a design choice, not a universal constant. The cited official architecture material does not establish one best chunk size for every corpus. Choose segmentation based on document structure and the kinds of questions users ask, then measure retrieval quality. A passage that loses its source pointer or access metadata can be difficult to cite and may be exposed to an unauthorized user.
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Choose retrieval based on the queries you need to answer
Dense vector search can find passages that are semantically related even when they do not use the query’s exact wording. Token-based search can help with exact phrases, names, and identifiers. Hybrid search combines semantic and token-based signals; rank fusion or a reranking stage can further refine results when evaluations justify the added complexity.
| Retrieval design | Useful when | Trade-off to evaluate |
|---|---|---|
| Dense vector search | Users phrase a question differently from the source text but mean the same thing. | Test whether it finds exact names, phrases, and identifiers reliably. |
| Token-based search | Exact terms, product names, or identifiers are important to the query. | Test whether it also surfaces relevant passages when wording varies. |
| Hybrid search | The corpus needs both semantic matching and exact-term matching. | Measure relevance and coverage against simpler baselines; there is no universal winner. |
| Reranking after retrieval | An initial result set needs another relevance-selection stage. | Measure the quality gain against added latency and operational complexity. |
Google Cloud documents hybrid search that combines token-based and semantic retrieval, as well as a retrieval-then-reranking pattern. Those are available design options, not evidence that one configuration will outperform another on your corpus. Compare them on representative exact-term and semantic queries.
When should the agent call web search?
Use web search when an answer depends on current public information, or when internal retrieval cannot support the requested claim. Use internal retrieval for questions scoped to authorized material. A question spanning internal and public facts can use both, but keep the origins of the evidence distinct so the answer does not imply that an external page is internal policy or vice versa.
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Expose web search as a tool with a clear description of when it should be called. OpenAI documents hosted web search in the Responses API and Agents SDK, with URL citation annotations in its response format. Anthropic’s API documentation describes live web content and citation-bearing results. These examples establish implementation options, not a neutral ranking. Provider controls, output formats, availability, data handling, latency, and costs differ and should be checked in the selected provider’s current documentation.
A provider-managed search tool can reduce integration work. A custom search integration may provide more control over query parameters, filtering, or retrieval behavior. Preserve raw source identifiers and adapt your citation renderer to the selected API’s actual response format instead of assuming providers return interchangeable results. Keep search calls, result volume, elapsed time, and agent retries bounded.
How should the orchestration loop check evidence?
Make evidence sufficiency an explicit decision rather than asking the model to search indefinitely. A practical loop is:
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- Plan: identify which facts the answer needs and whether each requires internal evidence, current public evidence, or both.
- Retrieve: call only the authorized internal search and web-search tools relevant to the question.
- Assess: inspect whether the results are relevant, authoritative enough for the claim, recent enough for the question, and broad enough to cover it.
- Target a gap: if a material fact is missing, issue a more focused query or consult another permitted source. Do not repeat a search without a defined gap.
- Stop: answer when evidence is adequate, or state what cannot be established when the retry budget is exhausted.
Pass selected evidence to the answer model with instructions to distinguish supported claims from uncertainty, cite each material claim, and treat text found in retrieved documents or web pages as untrusted content—not as instructions that override the system’s rules. Keep evidence records separate from answer text so citations can be checked and rendered without relying on the model to recreate source details.
OpenSearch’s RAG processor, introduced in version 2.12, illustrates a system that can select context fields and return original search results alongside a generated response. The general design lesson is to retain the result records that support an answer, not just the answer string.
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Documents and web pages can contain adversarial text. RAG does not make their contents trustworthy just because they came from retrieval. OWASP’s RAG security guidance covers risks and controls across ingestion, embedding, vector storage, retrieval, generation, output validation, and downstream agent integration.
- At ingestion: review or screen incoming material for adversarial content, and record its origin and handling policy.
- At indexing: preserve ownership and access-control metadata with every retrieval unit; do not let chunking discard it.
- At retrieval: enforce tenant and user authorization before selected evidence is passed to a model.
- At generation: distinguish system instructions from retrieved content, and do not allow page text to grant the agent new capabilities.
- At tool use: expose only the capabilities the workflow needs, bound calls and results, and validate structured output or any downstream action before execution.
- At testing: attempt prompt injection through both documents and web results, and test whether one user can retrieve another user’s material.
How should I evaluate and operate the pipeline?
Build a representative test set from real questions, recording expected source documents or answer facts. Evaluate retrieval, generation, and the combined system separately: a correct-sounding answer can still hide a retrieval failure, while relevant passages can still be misrepresented during generation.
| Evaluation area | What to check |
|---|---|
| Retrieval | Relevance and coverage of expected evidence, including exact-term and semantic queries. |
| Answer grounding | Whether material claims are supported by the selected evidence. |
| Citations | Whether each citation points to the actual source used and supports the nearby claim. |
| Abstention | Whether the system says evidence is insufficient when it is weak, missing, stale, or conflicting. |
| Security | Whether prompt injection is contained and unauthorized cross-user retrieval is prevented. |
| Operations | Latency, search and model calls, cost, and the effects of added retrieval or reranking stages. |
Compare a dense-only baseline with hybrid retrieval and, where useful, reranking. Include stale and conflicting sources in tests so the system’s recency and uncertainty behavior is visible. The cited architecture and security guidance describes approaches and risks, but does not establish a universal quality threshold or prove a best configuration for this application. Set acceptance criteria from your own use case and risk level, then monitor the same measures after deployment.
What should I decide before choosing a search provider?
Compare provider-hosted tools and custom search integrations against the requirements of your application rather than assuming a single option is best. Check citation metadata quality, filtering controls, freshness, result coverage, latency, cost, data handling, geographic availability, and integration burden. The cited provider documentation describes particular tools and output formats; it does not provide a complete neutral feature or price ranking. Pricing, rate limits, regional availability, retention, and service terms can change, so verify them in current official documentation before implementation.
Similarly, choose between dense, hybrid, and reranked retrieval using measurements on your own corpus. A more elaborate retrieval chain is worthwhile only if its improvement in evidence relevance or coverage justifies its additional complexity and latency.
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