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Build an agentic RAG pipeline as two connected systems: an offline process that prepares and secures your internal knowledge, and an online control loop that decides whether to search that corpus, the live web, or both. The loop should check whether its evidence is relevant and sufficient before generating an answer. This improves evidence selection and traceability; it does not guarantee that retrieval or the answer is correct.
What makes a RAG pipeline agentic?
A basic retrieval-augmented generation (RAG) request typically searches a corpus once, then gives the retrieved passages to a model to answer. An agentic pipeline adds a control loop: it can plan what evidence it needs, choose retrieval tools, inspect results, refine a query, and stop when it has enough evidence or reaches a limit.
That flexibility is useful for questions that need multiple sources, a current web check, or a deliberate evidence-quality check. It adds little to a straightforward question that one authorized corpus search can answer. Agentic patterns commonly include planning, tool use, and reflection, but every extra step also adds opportunities for delay, cost, and error.
How do I build an agentic RAG pipeline with real-time web search?
Build and test the ingestion path first, then add online routing and bounded tool use. Keep internal retrieval and web search as separate tools so the system can choose one or both according to the question.
#1 Best Overall
- Define evidence and routing rules. Identify which questions require internal sources, current public information, or both, and when a simple one-shot retrieval is enough.
- Prepare the authorized corpus. Parse and normalize documents, preserve useful structure and provenance, split them into retrieval units, generate embeddings, and create or update the search index.
- Retrieve internal evidence. Search within the user’s authorized scope and return relevant passages with source identifiers, locations, timestamps, and access metadata.
- Expose web search as a tool. Give the orchestrator a clear description of when the tool is appropriate. Preserve the returned URLs and citation metadata.
- Check evidence before answering. Inspect relevance, authority, recency, and coverage. Retry only to address a specific gap, and stop when evidence is adequate or the retry budget is exhausted.
- Generate and validate the answer. Provide selected evidence to the answer model, require claim-level support and appropriate uncertainty, and render citations from the sources actually used.
- Evaluate, secure, and monitor the complete path. Test retrieval, answer quality, citations, authorization, security, latency, and tool use before deployment; monitor them in operation.
How should the pipeline decide what to search?
Classify each request along three dimensions: whether it needs fresh information, which corpus or public sources are in scope, and whether the task needs multiple retrieval steps. The route should follow the evidence requirement, not a blanket rule to call every tool on every request.
| Question type | Starting route | Reason |
|---|---|---|
| Internal policy or procedure | Authorized internal retrieval | The answer should come from the relevant controlled corpus; public search is not a substitute for an internal policy. |
| Current public status or changing information | Live web search | The internal index may not contain information that changed after its last update. |
| A question that combines internal context with current external facts | Internal retrieval and web search | The answer needs both sources, and the response should make their roles traceable. |
| A straightforward question with adequate evidence in one source | One-shot retrieval, then answer | An iterative loop is unnecessary if it cannot improve evidence coverage. |
Live search provides access to web content at query time, but it does not establish that every result is current, authoritative, or complete. The orchestrator should treat freshness as something to assess from the results and their source context, not as a guarantee attached to the tool.
How do I prepare the internal corpus?
Track sources and permissions
Build ingestion around source connectors that record each document’s identity, owner, update time, and access policy. Parse and normalize formats while preserving headings and other structure that helps retrieval. Keep a pointer from each indexed unit back to the original document and its location.
Carry authorization metadata through parsing, chunking, embedding, and indexing. Apply access controls during retrieval, before passages are supplied to the generation model. If a document changes or is deleted, update the index so it reflects the current authorized corpus rather than a stale copy.
Choose segmentation based on the corpus
Divide documents into retrieval units that preserve enough context to answer likely questions. There is no universally established chunk size: select segmentation based on document structure, query types, and measured retrieval quality. A split that separates a passage from its heading, source, or permission context can make results harder to interpret or unsafe to use.
Build and maintain the index
Generate embeddings for the retrieval units and create or update the index as part of the ingestion pipeline. Treat indexing as an ongoing process, not a one-time setup. The online answer path can only retrieve from the material and metadata the ingestion path has successfully maintained.
Rank #3
How should internal retrieval work?
At query time, normalize the request and search within the user’s authorized corpus scope. Dense vector search can find passages that are semantically related even when they do not repeat the query’s wording. Token-based search can help with exact terms such as names, identifiers, or quoted phrases.
| Retrieval approach | Useful when | What to test |
|---|---|---|
| Dense vector search | Relevant passages express the idea differently from the query. | Whether the right evidence appears for semantic questions and whether exact terms are missed. |
| Token-based search | Exact names, phrases, or identifiers matter. | Whether matches are relevant in context rather than merely containing the same token. |
| Hybrid search | The workload includes both semantic questions and exact-term queries. | Whether combining token and semantic signals improves relevance or coverage on the target corpus. |
| Reranking after retrieval | A first-stage search returns candidates that need another relevance ordering. | Whether improved selection justifies the added latency and operational complexity. |
Hybrid retrieval and reranking are options, not automatic upgrades. Benchmark a dense baseline against the alternatives using your own questions and corpus. Return a compact evidence bundle with passage text, source identity, document location or URL, timestamps, and the metadata needed to enforce access and render citations.
How do I add web search to an AI agent?
Expose search as a distinct tool with a narrow purpose: retrieve public web evidence when a question depends on current information or internal retrieval leaves a specific evidence gap. A provider-managed search tool can reduce integration work; a custom integration may offer different controls over queries, filtering, or retrieval behavior. The right choice depends on the selected provider and application requirements; there is no neutral universal winner.
Rank #4
Preserve each result’s original source identifier, URL, and citation metadata as it moves through the orchestration and answer stages. Search APIs can return citation-bearing results, but formats and controls differ. Adapt the citation renderer to the chosen API’s documented response rather than assuming all providers return the same fields. Check the provider’s current documentation for its API schema, limits, pricing, data handling, and regional availability before implementation.
Bound the tool’s use. Set limits for the number of searches, iterations, result volume, and execution time. The workflow needs a defined stop condition and a way to say that available evidence is insufficient; otherwise, retries can continue without materially improving the answer.
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Use a bounded evidence loop
- Plan. Identify what facts the answer needs and which sources can establish them.
- Retrieve. Call internal search, web search, or both according to the route.
- Inspect. Assess each result for relevance, authority, recency, and coverage of the question.
- Address a concrete gap. Reformulate a query or search another source only when the evidence shows what is missing.
- Stop. Answer when the evidence is adequate, or report uncertainty when it is not or the retry budget is exhausted.
Keep evidence separate from the answer
Store retrieved evidence separately from generated prose. Give the answer model the selected passages and instructions to distinguish supported claims from uncertainty, cite material claims close to the supporting evidence, and treat retrieved text as content rather than as instructions. Render citations from the sources actually used, not from search results that were returned but not relied on.
Best Value
For questions that combine internal and external information, preserve enough provenance to show which claims rest on which source. If sources conflict or their dates make one less applicable, surface that issue instead of silently blending the claims.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I secure the retrieval and tool path?
Documents and web pages are untrusted inputs. A relevant-looking passage can still contain adversarial instructions, misleading claims, or material the user is not permitted to see. Security controls should cover the full path from ingestion through any downstream action.
- Ingestion: screen or review content for adversarial patterns and preserve source identity and ownership.
- Indexing: retain permission and provenance metadata on every retrieval unit rather than dropping it during transformation.
- Retrieval: enforce tenant, user, and corpus-scope authorization before evidence reaches generation.
- Generation: tell the model that retrieved material cannot override system instructions, and require claims to be supported by evidence.
- Tools and outputs: limit tools to necessary capabilities and validate structured output or downstream actions before execution.
- Testing: attempt prompt-injection scenarios and unauthorized cross-user retrieval as part of security evaluation.
These safeguards address distinct failure modes. Filtering for relevance does not enforce authorization, and an answer citation does not make untrusted content safe.
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How should I evaluate the pipeline?
Create a representative test set of real questions with expected source documents or answer facts. Assess retrieval and generation separately, then test the complete experience: a good answer depends both on finding the right evidence and on using it correctly.
| Evaluation area | What to inspect |
|---|---|
| Retrieval | Relevance and coverage of expected evidence; compare dense-only search with hybrid retrieval and reranking on semantic and exact-term questions. |
| Answer grounding | Whether material claims are supported by retrieved evidence and uncertainty is handled when the evidence is weak. |
| Citations | Whether citations identify the actual sources used and support the claims they accompany. |
| Abstention | Whether the system can decline to make unsupported claims when evidence is missing, stale, or conflicting. |
| Security | Whether adversarial content is treated as untrusted and unauthorized material stays out of results and answers. |
| Operations | Latency, search and tool-call volume, and cost alongside quality. |
There is no established universal score threshold or configuration that is best for every corpus and workload. Use your test results to decide whether extra retrieval stages improve quality enough to justify their operating cost.
What should I verify before choosing search services?
Compare alternatives against the requirements of your application rather than assuming one search integration or retrieval setup is best. For web search, assess citation metadata, filtering controls, result coverage, freshness, latency, cost, data handling, geographic availability, and integration burden. For internal retrieval, test evidence relevance and coverage, exact-phrase behavior, citation support, latency, and operational complexity. Confirm current service terms and API details with the selected provider, since these can change.
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