Traditional RAG is the better starting point when one predictable retrieval pass gives the model enough evidence. Agentic RAG is useful when the system must decide whether to search, refine a query, choose a tool, or retrieve again after reviewing results. That flexibility adds model and tool calls, so use it only when evaluation on your workload shows enough improvement to justify the added latency, cost, and operational complexity.
What is the difference between agentic RAG and traditional RAG?
These labels do not describe one universally standardized taxonomy. For a practical .NET design decision, distinguish the architectures by who controls retrieval and whether retrieval can repeat.
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- Traditional RAG: the application follows a predetermined path, commonly retrieving context for the user’s query and then asking a model to answer using that context.
- Agentic RAG: an LLM-driven agent can choose retrieval actions through tools or function calls. It may decide whether to search, what to search for, or whether to search again after considering intermediate results.
Agentic RAG is not inherently more accurate. If a single retrieval pass works well, adding an agent decision loop may add overhead without improving the answer. Conversely, fixed retrieval may not be enough when a question requires decomposition or a follow-up search based on what the first search finds.
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| Decision factor | Traditional RAG tends to fit when… | Agentic RAG may fit when… |
|---|---|---|
| Query pattern | One query and retrieval pass usually find the evidence. | Queries need decomposition, conditional search, or follow-up retrieval. |
| Control | The application should own a deterministic retrieval policy. | The agent needs to choose among search tools or decide whether to retrieve again. |
| Latency and cost | The budget favors fewer model and search calls. | Measured quality gains on harder tasks justify more calls and tokens. |
| Debugging | A short, stable pipeline is easier to trace and diagnose. | The team can inspect and govern tool decisions and intermediate results. |
| Failure handling | A simple retrieval fallback is sufficient. | The system has explicit limits and recovery paths for poor tool choices, loops, and unresolved answers. |
The choice is workload-specific. A hybrid is also a reasonable architecture to evaluate: run a normal retrieval pass for common queries, then permit bounded agent-directed follow-up for query classes that need it or when the first result is insufficient. This is an option to test, not a guarantee provided by Semantic Kernel.
How does Semantic Kernel support retrieval in a C# agent?
Semantic Kernel’s documented TextSearchProvider exposes two useful patterns. In BeforeAIInvoke mode, the provider searches before each agent invocation using the message passed to the agent. In OnDemandFunctionCalling mode, the agent can choose a search string and call the search function when needed. The documented on-demand configuration requires UseImmutableKernel = true.
The following sketch shows the mode switch and provider registration, not a standalone program. Constructors and APIs can change; compile against the specific Semantic Kernel package version used by your application and consult the current official sample for initialization and credentials.
var options = new TextSearchProviderOptions
{
SearchTime = TextSearchProviderOptions.RagBehavior.OnDemandFunctionCalling,
};
var provider = new TextSearchProvider(textSearch, options: options);
var agent = new ChatCompletionAgent
{
Kernel = kernel,
UseImmutableKernel = true,
};
agentThread.AIContextProviders.Add(provider);To use the documented before-invocation behavior instead, omit the on-demand configuration and use the provider’s BeforeAIInvoke default. That searches before each agent invocation rather than asking the agent to decide when to call search.
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What the full RAG setup needs
The Semantic Kernel example’s implementation sequence is:
- Configure an embedding generator and a vector store.
- Create a
TextSearchStorewith a collection name and vector dimensions. - Upsert the source text into the store.
- Create the Semantic Kernel agent and an agent thread.
- Add a
TextSearchProviderto the thread’s context providers. - For agent-directed retrieval, configure
SearchTimeasOnDemandFunctionCallingand set the agent’sUseImmutableKernelproperty totrue.
The documented code uses TextSearchStore<string>, an InMemoryVectorStore, and an embedding generator configured for 1536 dimensions. Those are example choices, not universal recommendations. Dimensions must match the selected embedding model and the collection schema; also account for the deployment and any existing indexed data. The cited article’s default Top value is 3 results. Tune that limit against your corpus and measure whether additional results improve evidence coverage or merely add irrelevant context.
Microsoft’s Semantic Kernel agent RAG article, dated May 22, 2025, described the functionality as experimental and subject to change. That is a dated status statement, not confirmation of its status in 2026. Verify the current package documentation and API status for the version you plan to deploy.
What .NET examples can serve as a baseline?
Semantic Kernel Vector Store RAG Demo
Microsoft’s separate Vector Store RAG demo provides a baseline for a predetermined retrieval path: it ingests PDF text into a vector store and uses retrieved material to supplement the model prompt. Its selectable stores include Azure AI Search, Azure DocumentDB, Cosmos NoSQL, in-memory storage, Qdrant, Redis, and Weaviate; the sample also allows selection of OpenAI or Azure OpenAI chat and embedding services. It is a code sample, not a performance comparison or a ranking of those backends.
Related Agent Framework sample
A separate Microsoft Agent Framework sample uses Qdrant with a custom document schema and says the backend can be replaced with one that implements Microsoft.Extensions.VectorStore. Its stated prerequisites include the .NET 10 SDK or later and Azure OpenAI deployments. Treat this as an Agent Framework example, not a Semantic Kernel sample or evidence that the two frameworks’ APIs are interchangeable.
How should agentic RAG be evaluated in production?
Compare both approaches on the same representative, versioned query set and the same corpus, chunking, embeddings, index, result limit, model, and prompt. For each run, record configuration and software versions. Include expected relevant documents, acceptable answers, and expected tool choices where tool choice matters. A quality score alone will not show whether an agent is using retrieval efficiently or whether its added calls are worth the cost.
- Answer and task quality: correctness or task success, grounding in retrieved evidence, and citation or source correctness when the product presents citations.
- Retrieval quality: whether expected evidence appears in the retrieved set and whether irrelevant context crowds it out.
- Tool-selection accuracy: how often the agent chooses the expected retrieval or other tool for a query.
- Retrieval efficiency: tool calls per request, searches per answered query, and retrievals that add no useful evidence.
- Latency: median and tail end-to-end latency, broken down into model reasoning, search or tool execution, and result processing.
- Cost per request: all model calls and token use plus search and other service calls; compare the added cost with the measured quality change.
- Reliability and operations: timeouts, failed or malformed tool calls, unresolved responses, loop-limit hits, fallback frequency, and trace completeness.
- Security: validated tool parameters, least-privilege access to data and actions, and protection against exposing credentials through tool results.
Microsoft Architecture Center’s agentic RAG guidance calls out tool-selection accuracy, retrieval efficiency, end-to-end latency, and cost per request, along with reliability, observability, and security. It highlights poor tool choice, reasoning loops, and failure to reach an answer as operational concerns, and recommends tracing actions and results, validating parameters, and applying least privilege. The guidance does not prescribe one universal test set or release threshold. Set thresholds according to the consequences of error and the experience your users need.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How much latency can extra tool calls add?
Microsoft Architecture Center’s 2026 guidance gives illustrative design examples of 2–3 seconds for a standard RAG request with one search and one generation, and 8–15 seconds for an agentic RAG request with three to five tool calls. These are examples, not results from a controlled benchmark or a service-level commitment. They should not be treated as predicted latency for a particular .NET deployment.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →The reason for the potential increase is structural: as Microsoft Architecture Center explains, “Each tool call adds a round trip to the search service plus the time for the model to reason about the results.” Actual latency depends on the model, region, search service, concurrency, corpus, and request mix. Measure those conditions in your own environment, including tail latency rather than only an average.
What changes when you choose a vector store?
A vector-store abstraction does not erase backend differences. Microsoft Learn’s Semantic Kernel vector-store samples note that not all databases natively support Skip for vector search; some connectors may fetch Skip + Top results and skip client-side. The samples also describe matching a data model to an existing collection schema for interoperability with systems such as LangChain.
Evaluate candidate backends against the actual corpus and deployment requirements, including query relevance, filtering and metadata behavior, schema compatibility, indexing and update flow, paging support, operational fit, security, deployment geography, and measured latency and cost. The cited examples demonstrate options and implementation patterns; they do not establish a universally best store.
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