AI query routing is the layer of logic that decides where each query should go before it runs: which model, which tool, which index, or which underlying data source can answer it best. In a data platform, that decision spans both directions at once. It routes a natural-language question to the right retrieval path and large language model, and it routes the resulting structured query to the right engine and system across a heterogeneous estate. Done well, routing cuts cost and latency without lowering answer quality, because cheap requests stop paying frontier-model prices and queries stop scanning systems that hold none of the relevant data [1].

The reason this matters more in 2026 than it did two years ago is volume. Autonomous agents now query enterprise systems millions of times a day, and Gartner projects that 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from less than 5% in 2025 [28]. Every one of those calls is a routing decision. When the routing is naive, the bill and the tail latency both climb with agent adoption. When the routing is intelligent, the same workload runs against a fraction of the expensive compute, and the platform stays predictable as it scales.

This guide defines AI query routing for data platforms, contrasts the routing approaches you will actually choose between, covers the dynamic optimization techniques that sit underneath them, anchors the performance claims to published benchmarks, and compares the solution categories on the market. It closes with implementation patterns and the architectural decision that determines whether routing helps you or quietly becomes another fragile layer to maintain.

What Is AI Query Routing and Why It Matters for Data Platforms

AI query routing is a decision step that classifies an incoming request and dispatches it to the destination most likely to answer it well, at the lowest acceptable cost and latency [1][2]. The destinations vary by context. In a retrieval-augmented generation system, the destination is a vector index, a SQL database, or a specific tool, and routing the question to the right one is a core technique in advanced RAG [29]. In a multi-model deployment, it is one model out of several at different price and capability points [8]. In a data platform, it is a specific query engine pointed at a specific system, whether that is a cloud warehouse, an on-premises database, or a streaming source.

Retrieval-augmented generation (RAG) is a pattern where a model retrieves relevant documents or records from an external store and uses them as context to generate an answer, rather than relying only on what it learned during training. Routing decides which store to retrieve from, and that single decision shapes both the accuracy and the cost of everything downstream.

The reason routing belongs in the data platform, not bolted onto the application, is that enterprise data does not live in one place. A typical large enterprise runs data across mainframes, on-premises databases, Hadoop clusters, cloud warehouses, streaming systems, and dozens of SaaS tools. An agent that can only reach one of those systems returns confident, incomplete answers. Routing that understands the full estate is what lets a query land on the system that actually holds the answer, under consistent governance, without first copying everything into a central store. This is the same foundational AI data layer that makes governed, cross-estate retrieval possible in the first place.

There is a hard limit worth stating before the benefits: routing adds a decision step, and a decision step can be wrong. A misroute sends a query to the wrong model or the wrong source, which costs a retry and can degrade the answer. The engineering goal is not zero misroutes, which is unrealistic, but a misroute rate low enough that the savings on the correctly routed majority dwarf the cost of the occasional retry. The benchmarks later in this guide show that this trade is favorable in production, but it is a trade, not a free lunch.

Logical vs. Semantic Routing: Choosing the Right Approach

The single most useful distinction in this field is logical routing versus semantic routing, and most ranking explanations of query routing are built on this contrast [2][3]. They differ in how the routing decision gets made, and that difference drives everything about cost, predictability, and the kinds of mistakes each one makes.

Logical routing decides where to send a query using explicit rules: if-else conditions, schema mappings, metadata matches, or pattern checks. It does not interpret meaning. It reads discrete variables (a file type, a table name, a date range, a keyword) and follows a predefined path, much like a switch statement in code [2]. Logical routing is fast, cheap, fully deterministic, and easy to audit, which matters in regulated environments where you must explain why a query went where it did.

Semantic routing decides based on meaning. It encodes the incoming query and each candidate destination into embeddings, numerical vectors that capture intent, then sends the query to the destination with the highest similarity score, usually measured by cosine distance [4][5]. Semantic routing handles fuzzy, natural-language input that no rule could anticipate, which is exactly what users and agents produce. The open-source Aurelio Semantic Router popularized doing this with lightweight vector math rather than a slow model call, and reports cutting routing-decision latency from roughly 5,000 milliseconds for an LLM-based decision to about 100 milliseconds using local embeddings [4][5]. A third option, often grouped with logical routing, is LLM-based routing, where a language model reads the query and picks the destination; it is the most flexible and the most expensive per decision [3].

The practical answer is that production systems usually combine them. Microsoft's engineering teams documented a semantic router built on Azure AI Search for a banking chatbot, routing across 12 domains defined by 98 example utterances, and benchmarked it against simply cramming all context into the prompt with GPT-3.5, GPT-4o, and GPT-4o-mini [23]. The pattern that holds up is a cheap logical or semantic filter first, with an LLM-based decision reserved only for the genuinely ambiguous cases.

Routing decisions also intersect with where data physically sits. A semantic router can pick the right index, but if that index lives in another cloud or behind a regulatory boundary, the cost of reaching it changes the calculus. For how routing interacts with data locality and movement constraints, see building AI-ready data platforms without moving data.

When to Use Logical Routing vs. Semantic Routing

Use logical routing when the input is structured and the rules are stable: routing by document type, tenant ID, data classification, or a known set of query templates. It is cheaper to run, trivial to test, and its decisions are reproducible, which auditors and on-call engineers both appreciate. Use semantic routing when the input is open-ended natural language and the destinations differ by topic or intent rather than by any field you could match on. Reach for an LLM-based decision only for the residual cases that the first two cannot resolve, because paying for a model call on every request erases much of the savings routing is supposed to deliver [3][8].

Dynamic Query Optimization Strategies for Multi-Database Environments

Routing decides where a query goes. Dynamic query optimization decides how it runs once it gets there, adjusting the execution plan at runtime based on the data it actually encounters rather than committing to a plan chosen in advance. In a multi-database environment, the two work together: the router selects the engines and sources, and dynamic optimization makes each query efficient against systems whose size, skew, and freshness it cannot fully predict beforehand.

The clearest production example is Adaptive Query Execution in Apache Spark, which re-optimizes the query plan mid-flight using statistics gathered during execution, coalescing shuffle partitions, switching join strategies, and handling skew as it appears. On the TPC-DS benchmark, Adaptive Query Execution produced up to an 8x speedup on individual queries, with 32 queries seeing more than a 1.1x improvement [22]. The lesson generalizes beyond Spark: static plans built on stale statistics are slower than plans that adapt to the data in front of them.

Two long-standing query problems become acute in routed, multi-source systems, and naming them is useful because they map directly to secondary concerns architects ask about.

The N+1 query problem occurs when code runs one query to fetch a list of records, then runs an additional query for each record to fetch related data, producing N+1 round trips where one or two would do. It is the single most common performance problem in applications built on object-relational mappers [13][14]. In a federated setting it is worse, because each of those round trips can cross a network boundary to a different system. The fix is the same in spirit as eager loading in an ORM: collapse the per-record calls into batched or pushed-down operations so the work happens close to the data [14].

A second pattern, drawn from how models themselves are built, illustrates why routing the cheapest viable path matters. Multi-query attention and its successor grouped-query attention let multiple attention heads share key and value projections, which shrinks the memory a model must move during inference and speeds up token generation, at a small quality cost for the most aggressive variant [15][16]. The relevance to platform routing is the principle, not the mechanism: the dominant cost in serving is moving data, whether that is keys and values out of GPU memory or rows across a network. Optimization that reduces data movement, at the model layer or the platform layer, is where the real savings live [17].

This is why dynamic optimization and federation belong together. Pushing computation down to each source, joining only the results, and adapting the plan at runtime is what keeps a query across five systems from behaving like five separate slow queries. For how this plays out across distributed hybrid and multi-cloud estates, see our guidance on hybrid and multi-cloud data integration.

Benchmarks and Performance Outcomes: Latency, Cost, and Scale

Intelligent routing reduces cost without proportionally reducing quality, and the published numbers are consistent enough to plan against, with one important caveat about what each number measures. The headline benchmarks come from model routing, where the router chooses between a cheap and an expensive model, and they should not be read as guarantees for data-source routing, which depends on your estate. With that distinction made, the evidence is strong.

The most cited result is RouteLLM, the open-source routing framework from LMSYS. Against a baseline of using GPT-4 for everything, RouteLLM reported cost reductions of over 85% on MT Bench, about 45% on MMLU, and about 35% on GSM8K, while preserving 95% of GPT-4's measured quality [8][9]. The spread across benchmarks is the honest part of that result: savings depend heavily on how many queries genuinely need the expensive model, which is why the same technique yields 85% on one task and 35% on another.

Production reports land in a similar band. Guild.ai's documentation puts real-world routing cost reductions at 27% to 85% depending on traffic patterns and model selection, and estimates that organizations processing more than 100 million tokens a month typically save $50,000 to $80,000 a year against their prior bill [1]. Commercial routers describe a tunable trade rather than a fixed number: Orq.ai frames it as roughly 25% savings at 99.5% quality retention up to about 70% savings at 95% quality, letting teams choose their operating point [10]. Independent practitioner write-ups describe similar bands for matching the right model to each task [12].

Latency is where routing earns the most trust, because the decision overhead is tiny against the thing it is deciding about. A semantic router using local vector math decides in the low hundreds of milliseconds or less, and Guild.ai measures high-performance routers adding 10 to 50 microseconds, against model inference times of 500 to 2,000 milliseconds [1][4]. Routing overhead under those conditions is well below 5% of total response time, and a correct route that avoids a slow or oversized model can cut end-to-end latency outright [11].

The caveat worth repeating: these figures assume a workload with a real mix of easy and hard requests. A workload where every query genuinely needs the strongest model or the largest scan will save little, because there is nothing cheaper to route it to. Routing pays in proportion to how skewed your workload is, and most enterprise workloads are heavily skewed toward the easy end. For how to evaluate these outcomes against broader readiness criteria, see the 2026 enterprise guide to AI-ready data.

Top AI Query Routing Solutions for Enterprise Data Platforms in 2026

There is no single best AI query routing solution, because the category spans four different layers of the stack that solve different problems. The right choice depends on whether you are routing between models, routing to data sources inside an application, routing SQL across an estate, or governing all of it as one system. The honest comparison is by category, with the understanding that serious data platforms end up using more than one.

Model routers choose between large language models at different price and capability points. RouteLLM (open source), along with commercial services such as Not Diamond, Martian, Orq.ai, and Morph, sit in front of model APIs and send each request to the cheapest model that will answer it acceptably [8][10][25]. Dynamic model-selection strategies in this category continue to mature through 2026 [30]. They are the fastest way to cut an inference bill and do nothing about where your data lives.

Semantic routing libraries and frameworks route a query to the right tool, index, or data source inside an application. The Aurelio Semantic Router, the router constructs in LangChain and LlamaIndex, and Azure AI Search's semantic routing pattern all fit here [4][7][23]. The same approach increasingly anchors agentic workflows, where a router picks the right tool or sub-agent at each step [24][31]. They are embedded by application developers and are excellent for RAG, but they route within whatever sources the application already has connected.

Federated query engines route SQL across heterogeneous systems without moving the data. Trino is the reference implementation, connecting to more than 30 source types and joining across them in a single query, and it is the foundation Starburst builds on commercially [18][19][21]. Apache Kyuubi adds multi-tenant SQL gateway capabilities on top of engines like Spark and Trino, and Apache Gravitino provides the federated metadata catalog that tells the engine what exists and where [20]. This category is where routing meets the data platform directly, because the routing target is a real system holding real enterprise data.

Composable data platforms with governed routing combine the federation layer with unified identity, policy enforcement, and agent orchestration so that every routed query, whether issued by a human or an agent, runs under the same controls. This is the category that matters when "the data platform" means a sprawling estate rather than a single connected application, and it is the lane NexusOne occupies. The 2026 shift toward autonomous agents makes this layer urgent: only about 30% of organizations have reached a mature stage of agentic governance, leaving the other 70% routing agent queries on foundations built for human-paced access [27][28].

If you are building a vendor shortlist rather than a proof of concept, the evaluation criteria that separate real capability from marketing are worth treating rigorously; our 2026 AI data buyer's guide lays out that framework.

Why Not Just Use Your Cloud Provider's Routing?

The strongest objection to a dedicated routing layer is that the major cloud platforms already route within their ecosystems, so why add anything. The answer is scope. Snowflake, BigQuery, Redshift, and Microsoft Fabric each route and optimize beautifully inside their own boundary, but their governance, identity, and query reach stop at the edge of that ecosystem. The moment a query needs to span a cloud warehouse, an on-premises Oracle database, and a mainframe under one consistent policy, single-vendor routing cannot reach across the gap without middleware and custom integration. For an enterprise whose data is genuinely consolidated in one platform, the cloud provider's routing is the right answer. For the far more common enterprise running 15 or more systems, the routing layer has to be estate-wide or it routes around the hardest, most valuable data [26][27].

Implementation Patterns for Platform Architects and Scaling Teams

The implementation that works is layered, and it routes cheap before it routes smart. Put a fast deterministic filter at the front to handle the structured, high-volume cases, add a semantic layer for natural-language intent, and reserve an LLM-based decision for the genuine edge cases. Underneath, run a federated query engine so that a routed query reaches the actual source instead of a stale copy, and enforce one identity and policy model across every destination so that routing never becomes a way to slip past governance. The capabilities that make this concrete map directly to platform features; see how they fit together in the NexusOne capabilities overview.

For scaling teams under resource and time constraints, the lighter path is real. Start with an open-source semantic router or a model router in front of your existing model calls, which can cut inference cost in weeks with little architectural commitment [8][10]. Add federated query through Trino when you have more than two systems worth joining, and layer governance in as the number of agents and users grows. The mistake to avoid is the reverse order: building elaborate routing logic before the underlying data is reachable and governed, which front-loads complexity onto a foundation that is not ready. Teams wrestling with fragmented, ungoverned data usually need to fix the data foundation before routing can deliver its full return.

A short checklist for putting routing into production:

• Classify your workload by difficulty first; routing pays in proportion to how many requests can safely take a cheaper path [1][8].

• Default to a logical or semantic filter; call a model to decide only on the residual ambiguous cases [3].

• Push computation down to each source and batch per-record calls to avoid the N+1 trap across network boundaries [14].

• Enforce one identity and policy model across every routing destination, so an agent route is governed exactly like a human route [27].

• Measure misroute rate and tail latency, not just average cost; the savings are real only if the correctly routed majority stays large [1].

• Instrument every routing decision for audit: what was routed, where, on whose behalf, and under what policy [27][28].

The Biggest Challenges in Routing at Scale

The hardest problems are not the routing algorithm. They are misroute handling, governance consistency, and observability. A misroute degrades an answer quietly, so you need retries and a measured error rate, not blind trust in the classifier. In multi-agent systems the failure mode is ambiguity in agent selection, which is why a common best practice is to give each agent a narrow, well-defined role so routing has clean boundaries to choose between [6]. Governance has to hold identically across every destination, because routing multiplies the number of paths a query can take and each path is a potential gap. And because agents route millions of times a day, you need to capture not just what happened but why, on whose behalf, and under what policy, or you lose the ability to debug and to pass an audit [27][28]. These are tractable problems, but they are platform problems, which is why routing tends to migrate from the application into the data layer as a system matures.

Build Your Intelligent Query Routing Architecture with NexusOne

NexusOne is built for the case the other categories only partially cover: routing every query, human or agent, across an entire heterogeneous estate under one governance model. It is an AI-native data layer, a universal control plane that lays horizontally across mainframes, on-premises databases, cloud warehouses, Hadoop clusters, and streaming systems, connecting them through one identity, policy, and metadata model rather than a separate integration for each [32]. Routing on top of that layer reaches the real source, in place, under the same controls whether the query comes from an analyst or an agent.

The routing substrate is open and recognizable. Federated query runs on Trino, Apache Kyuubi, and Apache Gravitino, so a single query can span systems without copying data. Identity is unified through Keycloak, fine-grained access is enforced by Apache Ranger, the catalog is DataHub, and agent workflows are orchestrated with CrewAI, all built on a foundation of more than 85 integrated open-source projects rather than a proprietary format that locks you in [32]. Because the governance is one model across the estate, an agent's routed query is governed exactly like a human's, which is the gap most single-ecosystem routing leaves open.

The proof is in deployment speed and outcomes. At Wells Fargo, NexusOne connected 30 applications to the cross-estate layer in under four weeks and eliminated more than $130 million in license and hardware cost, measured against a Cloudera bill, while running production AI workloads on the modernized infrastructure [32]. At SBFe, one governed layer now spans 160 financial institutions and supports 166 audits a year [32]. These are delivered through Embedded Builders, engineers who wire a specific environment into the layer, and the 5-5-5 model: provisioned in hours, source systems connected in days, a production semantic layer live in weeks. There is hardening and deeper integration still ahead for any estate this size, but the early deployments give us confidence that estate-wide governed routing is practical, not theoretical.

If your data lives in more than a handful of systems and you are about to point agents at all of them, the routing decision and the governance decision are the same decision. Talk to our architects about what intelligent query routing looks like in your own environment.

Key Terms

Frequently Asked Questions

What is AI query routing and how does it work?

AI query routing is a decision step that inspects an incoming query and sends it to the destination most likely to answer it well at the lowest acceptable cost and latency [1]. It works by classifying the query, by explicit rules, by embedding similarity, or by a model's judgment, and then dispatching it to the chosen model, tool, index, or data source. In a data platform, routing operates in both directions: it picks the retrieval path and model for a natural-language question, and it routes the resulting structured query to the right engine and underlying system [2][8].

What are the main types of AI query routing (logical, semantic, and rule-based)?

There are three primary types. Logical routing (also called rule-based routing) uses explicit conditions, schema, and metadata to make deterministic decisions [2]. Semantic routing uses embeddings to route by the meaning and intent of a query, measuring similarity between the query and each destination [4]. LLM-based routing uses a language model to read the query and decide, offering the most flexibility at the highest cost per decision [3]. Production systems typically layer them, using cheap logical or semantic filters first and reserving model-based decisions for ambiguous cases.

How does AI query routing reduce LLM inference costs and latency?

Routing reduces cost by sending requests that do not need a frontier model to a cheaper one, and by avoiding scans of systems that hold no relevant data. RouteLLM reported cost reductions of over 85% on MT Bench while keeping 95% of GPT-4's quality, and production deployments report reductions of 27% to 85% depending on workload [8][1]. Latency improves because the routing decision is cheap relative to inference: optimized routers add 10 to 50 microseconds and semantic routers decide in roughly 100 milliseconds, against model inference of 500 to 2,000 milliseconds, so a correct route that avoids an oversized model cuts total response time [1][4][11].

How does AI query routing improve RAG and multi-database data platforms?

In RAG, routing sends each question to the index or source that actually contains the answer, which improves both accuracy and cost by avoiding irrelevant retrieval [3][23]. In a multi-database platform, routing pairs with federated query so a single request reaches the right engine and system across cloud warehouses, on-premises databases, and streaming sources without copying data [21]. Combined with dynamic query optimization, which adapts the execution plan at runtime, this keeps a query spanning several systems from degrading into many slow, separate queries [22].

When should you use logical routing versus semantic routing for your data platform?

Use logical routing when inputs are structured and rules are stable, such as routing by document type, tenant, data classification, or known query templates; it is cheaper, deterministic, and easy to audit [2]. Use semantic routing when inputs are open-ended natural language and destinations differ by topic or intent rather than by any matchable field [4]. Most production platforms combine the two and add an LLM-based decision only for the residual ambiguous cases, because paying for a model call on every request erases much of routing's savings [3].

What are the biggest challenges in implementing AI query routing at scale?

The hardest challenges are not the routing algorithm but misroute handling, governance consistency, and observability. A misroute quietly degrades an answer, so production systems need retries and a measured error rate rather than blind trust in the classifier [1]. Governance must hold identically across every destination, since routing multiplies the paths a query can take and each is a potential gap [27]. And because agents route millions of times a day, every decision must be logged with what was routed, where, on whose behalf, and under what policy, both to debug and to pass audits [28].

How is AI query routing different from traditional query optimization?

Traditional query optimization, the work of a database's query processor and optimizer, decides how to execute a single query efficiently within one system: choosing join orders, indexes, and execution plans [22]. AI query routing decides where a query should go in the first place, across multiple models, tools, or data sources, before any one system optimizes it. The two are complementary layers. Routing selects the destination and engine; dynamic query optimization, such as Spark Adaptive Query Execution, then adapts the plan at runtime once the query is running against the chosen source [22].

References

[1] Guild.ai. "Query Routing (AI)." https://www.guild.ai/glossary/query-routing-ai

[2] Maddewad, Ankur. "Understanding Query Routing: Logical vs Semantic." Medium. https://medium.com/@ankur0x/understanding-query-routing-logical-vs-semantic-6d0d14fbf5e9

[3] Towards Data Science. "How to Build Helpful RAGs with Query Routing." https://towardsdatascience.com/rags-with-query-routing-5552e4e41c54/

[4] Giskard. "Semantic Router: Efficient Semantic Query Routing for AI." https://www.giskard.ai/glossary/semantic-router

[5] Deepchecks. "What is Semantic Router? Key Uses & How It Works." https://www.deepchecks.com/glossary/semantic-router/

[6] Patronus AI. "AI Agent Routing: Tutorial & Best Practices." https://www.patronus.ai/ai-agent-development/ai-agent-routing

[7] Red Hat Developer. "LLM Semantic Router: Intelligent request routing for large language models." https://developers.redhat.com/articles/2025/05/20/llm-semantic-router-intelligent-request-routing

[8] LMSYS Org. "RouteLLM: An Open-Source Framework for Cost-Effective LLM Routing." https://www.lmsys.org/blog/2024-07-01-routellm/

[9] Ong, Isaac, et al. "RouteLLM: Learning to Route LLMs with Preference Data." arXiv:2406.18665. https://arxiv.org/abs/2406.18665

[10] Orq.ai. "Intelligent LLM Routing: Cut Costs by 25-70%." https://router.orq.ai/blog/auto-router-intelligent-llm-routing

[11] GMI Cloud. "Cutting LLM Inference Costs in 2026: Where Caching, Batching, and Smart Routing Actually Pay Off." https://www.gmicloud.ai/en/blog/llm-inference-cost-optimization-caching-batching-routing

[12] Pondhouse Data. "Saving costs with LLM Routing: The art of using the right model for the right task." https://www.pondhouse-data.com/blog/saving-costs-with-llm-routing

[13] Scout Monitoring. "Understanding N+1 Database Queries: Rails, Django, and Elixir." https://www.scoutapm.com/blog/understanding-n1-database-queries

[14] PingCAP. "How to Efficiently Solve the N+1 Query Problem." https://www.pingcap.com/article/how-to-efficiently-solve-the-n1-query-problem/

[15] Ainslie, Joshua, et al. "GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints." arXiv:2305.13245. https://arxiv.org/abs/2305.13245

[16] Klu. "What is Grouped Query Attention (GQA)?" https://klu.ai/glossary/grouped-query-attention

[17] Friendli.ai. "Grouped Query Attention (GQA) vs. Multi Head Attention (MHA): LLM Inference Serving Acceleration." https://friendli.ai/blog/gqa-vs-mha

[18] Trino. "Distributed SQL query engine for big data." https://trino.io/

[19] Cloudera. "Trino: The Federation Engine Powering Your Unified Data Fabric." https://www.cloudera.com/blog/business/trino-the-federation-engine-powering-your-unified-data-fabric.html

[20] Gravitino. "Using Apache Gravitino with Trino for Query Federation." DEV Community. https://dev.to/gravitino/using-apache-gravitino-with-trino-for-query-federation-4doi

[21] Starburst. "How does data federation work." https://www.starburst.io/blog/how-does-data-federation-work/

[22] Databricks. "Adaptive Query Execution: Speeding Up Spark SQL at Runtime." https://www.databricks.com/blog/2020/05/29/adaptive-query-execution-speeding-up-spark-sql-at-runtime.html

[23] Microsoft ISE Developer Blog. "Semantic Router using Azure AI Search." https://devblogs.microsoft.com/ise/semantic-routing-using-azure-ai-search/

[24] The New Stack. "Semantic Router and Its Role in Designing Agentic Workflows." https://thenewstack.io/semantic-router-and-its-role-in-designing-agentic-workflows/

[25] Morph. "What Is an LLM Router? Automatic Model Routing for Cost and Quality." https://www.morphllm.com/llm-router

[26] K2view. "What are data agents? The bridge between agentic AI and enterprise data." https://www.k2view.com/what-are-data-agents/

[27] Promethium. "AI Agent Data Governance: The Enterprise Playbook for 2026." https://promethium.ai/guides/ai-agent-data-governance-enterprise-playbook-2026/

[28] TechTarget. "How agentic AI governance tackles data, security challenges." https://www.techtarget.com/searchdatamanagement/feature/How-agentic-AI-governance-tackles-data-security-challenges

[29] Epsilla. "Let the Answers Come to You with Query Routing." https://blog.epsilla.com/advanced-rag-optimization

[30] Zylos Research. "AI Agent Model Routing and Dynamic Model Selection Strategies." https://zylos.ai/research/2026-03-02-ai-agent-model-routing/

[31] arXiv. "When to Reason: Semantic Router for vLLM." arXiv:2510.08731. https://arxiv.org/abs/2510.08731

[32] NexusOne. "Platform architecture, customer outcomes, and capabilities." https://www.nx1.io/