Structured Retrieval Orchestration: Why Multi-Agent Systems Need More Than RAG

The Limitation of RAG as an Isolated System

Retrieval-augmented generation (RAG) is now a standard component of knowledge-intensive AI. By integrating external retrieval with large language models (LLMs), it extends their usefulness beyond static pretraining data. However, RAG alone is often insufficient for complex real-world applications.

The assumption behind most RAG implementations is that improving retrieval alone — through better embedding models, indexing strategies, or reranking mechanisms — will solve the problem. In reality, the entire retrieval workflow is the issue.

Where RAG Systems Fail in Real Deployment

• Incomplete or ambiguous queries → Users rarely phrase questions optimally for retrieval.
• Multi-turn context loss → Retrieval is usually unaware of past exchanges, leading to fragmented results.
• Heterogeneous data sources → Most implementations assume a single knowledge base, while real-world applications require mixing vector search, API lookups, and structured databases.
• Inefficient execution → RAG pipelines often run retrieval, reranking, and generation in sequence, leading to latency bottlenecks.

At scale, these limitations make retrieval quality highly variable and response latency unpredictable. The core problem is that retrieval is treated as a single step rather than a structured, orchestrated process.

Photo by Amélie Mourichon on Unsplash

Retrieval is Not Just a Search Problem — It’s an Orchestration Problem

Most retrieval pipelines assume that once a query is issued, the only goal is to retrieve the most relevant documents. This assumes that:

1. The user’s query is already well-formed.

2. The best information is stored in a single retrieval source.

3. The retrieval process itself is the primary bottleneck.

None of these assumptions hold in practice. Instead, retrieval should be treated as a structured reasoning task, where multiple agents collaborate to:

• Reconstruct queries dynamically based on conversation history and task-specific context.
• Select optimal knowledge sources rather than querying everything indiscriminately.
• Optimize execution paths for response time and resource constraints.

This is the core shift that KIMAs (Knowledge Integrated Multi-Agent System) introduces.

What KIMAs Does Differently

KIMAs, introduced by Li et al. (2025) rethinks retrieval as a multi-agent process, where specialized agents handle different aspects of the workflow:

1. Query Refinement as a First-Class Component

Poor retrieval often starts with incomplete or ambiguous queries. KIMAs mitigates this by introducing context-aware query rewriting before retrieval.

• Extracts missing details from previous conversation history.
• Adjusts queries dynamically depending on the retrieval method (vector search vs. API lookup).
• Rewrites based on retrieval feedback, iterating until a useful response is found.

Most RAG pipelines assume retrieval happens once. In contrast, KIMAs treats retrieval as an adaptive process where queries are rewritten based on intermediate results.

2. Multi-Source Retrieval Routing

Rather than issuing the same query to every source, KIMAs introduces embedding-based knowledge routing.

• Structured vs. unstructured retrieval → Queries are directed toward databases, APIs, or search engines based on intent.
• Adaptive retrieval selection → Only the most relevant sources are activated for each request.
• Cross-source query handling → Queries may be rewritten to align with specific retrieval mechanisms.

This allows KIMAs to reduce noise from irrelevant sources while maintaining coverage across heterogeneous knowledge bases.

3. Parallelized Execution for Lower Latency

Most RAG implementations run in sequence:

1. Query is issued.

2. Documents are retrieved.

3. Reranking occurs.

4. Response is generated.

This serial execution introduces unnecessary delays, especially when retrieving from multiple sources.

KIMAs restructures this process by:

• Running retrieval agents in parallel, reducing wait time for slower sources.
• Prefetching and caching frequently accessed information.
• Filtering retrieved content before passing it to the LLM, optimizing token efficiency.

This ensures that knowledge-intensive applications can scale without latency degradation.

4. Automated Reference and Citation Tracking

LLMs struggle with verifiability — most responses do not contain clear citations, and hallucinations are difficult to detect.

KIMAs introduces a look-back citation mechanism:

1. The initial response is generated based on retrieved knowledge.

2. A secondary pass extracts and verifies references.

3. References are appended to the response dynamically, ensuring traceability.

This is particularly relevant for enterprise applications, research tools, and compliance-driven workflows, where answers must be both accurate and auditable.

KIMAs is not simply a better retrieval system — it is a structured approach to retrieval orchestration, ensuring that responses are context-aware, efficiently retrieved, and dynamically optimized.

The Future of Multi-Agent RAG Systems

The real bottleneck in retrieval systems is not retrieval itself but how retrieval is structured and executed.

KIMAs moves the focus from:

❌ “How do we retrieve the best documents?”

✅ “How do we optimize retrieval, reasoning, and generation as a structured process?”

Key Takeaways

• Retrieval needs orchestration, not just better indexing.
• Query rewriting is essential for real-world retrieval accuracy.
• Multi-source knowledge integration must be dynamic.
• Parallelized execution reduces bottlenecks in response time.

These principles are central to KIMAs, positioning it as a potential example approach for scalable, adaptive knowledge retrieval.