Agentic RAG: Autonomous AI Retrieval and Generation
AI agents need a warehouse, not a database. Agentic RAG uses AI agents that reason about how to retrieve information -- dynamically selecting tools, decomposing complex queries, and iterating until they find the right context. Built on Mixpeek's multimodal data warehouse infrastructure.
What is Agentic RAG?
Agentic RAG replaces the fixed retrieval pipeline with an AI agent that reasons about how to find information. The agent plans, selects tools, evaluates results, and iterates -- treating retrieval as a dynamic decision process rather than a static workflow.
Reasoning Over Retrieval
Instead of blindly embedding a query and searching, the agent first analyzes what information is needed, which data sources are relevant, and what retrieval strategy will yield the best results. This planning step prevents the blind-search failures that plague standard RAG.
Retrieval as Tool Use
Mixpeek retriever pipelines become tools the agent can invoke. Each pipeline is configured for specific data types, namespaces, and search strategies. The agent selects the right tool for each sub-question, just as a researcher would choose different databases for different types of information.
Self-Correcting Loops
When retrieval results are insufficient or irrelevant, the agent does not give up. It reformulates the query, adjusts filters, tries different retrievers, or broadens the search scope. This iterative refinement is the key difference between standard and agentic RAG.
Standard RAG vs. Agentic RAG
The fundamental difference: fixed pipelines vs. autonomous retrieval agents.
Standard RAG
A static pipeline: embed the query, search the vector database, retrieve top-K results, pass them to the LLM. The retrieval strategy is hardcoded. If the first search does not find good results, the system returns poor answers rather than adapting.
Query --> Embed --> Vector Search (fixed) --> Top-K --> LLM --> Answer
- Cannot reformulate queries when results are poor
- Cannot search multiple indexes or data sources
- No reasoning about which retrieval strategy to use
- Fails silently on ambiguous or multi-faceted queries
Agentic RAG
An AI agent decides how to retrieve information. It can reformulate queries, select which retrievers to call, chain multiple searches, filter and refine results, and iteratively improve until it has sufficient context to generate an accurate answer.
Query --> Agent Reasoning --> Tool Selection --> Retriever(s) --> Evaluate --> Iterate or Answer
- Query decomposition and reformulation
- Dynamic retriever and tool selection
- Multi-step iterative retrieval
- Self-evaluation and result quality checking
Agentic RAG Architecture
A four-phase cycle: plan, select tools, retrieve iteratively, and synthesize. The agent loops through retrieval phases until it has sufficient context.
Planning
The agent analyzes the query and creates a retrieval plan. For complex questions, it decomposes the query into sub-questions that can be answered independently. For ambiguous queries, it identifies clarification strategies.
Tool Selection
The agent selects which tools to use -- specific retriever pipelines, namespace searches, metadata filters, or external APIs. Different sub-questions may route to different retrievers optimized for that data type.
Multi-Step Retrieval
The agent executes retrieval calls, evaluates the results, and decides whether to refine the query, search additional sources, or apply different filters. This iterative loop continues until the agent has sufficient context.
Synthesis
With all retrieved context assembled, the agent synthesizes a final answer. It can cite specific sources, explain its reasoning chain, and flag low-confidence segments where retrieval quality was insufficient.
The agent evaluates retrieved results after each retrieval step. If results are insufficient, it loops back to planning with refined strategies. This self-correcting behavior is what separates agentic RAG from static pipelines.
Agentic RAG Capabilities
Dynamic tool selection, iterative refinement, query decomposition, and result evaluation -- the building blocks of autonomous retrieval.
Dynamic Retriever Selection
Agents choose which Mixpeek retriever pipelines to call based on query analysis. A question about video content routes to video-optimized retrievers while a document question routes to text retrievers -- automatically.
- Query-driven retriever routing
- Namespace-aware tool selection
- Modality-specific pipeline optimization
Iterative Query Refinement
When initial retrieval results are insufficient, the agent reformulates the query, adjusts filters, broadens or narrows scope, and retries. This self-correcting loop dramatically improves answer quality for complex queries.
- Automatic query reformulation
- Adaptive filter adjustment
- Confidence-based iteration control
Query Decomposition
Break complex multi-faceted questions into simpler sub-queries that can be answered independently. The agent runs parallel retrievals for each sub-query and combines results for a comprehensive answer.
- Multi-hop question decomposition
- Parallel sub-query execution
- Cross-source answer aggregation
Result Evaluation
The agent evaluates retrieved results for relevance and sufficiency before generating an answer. If results do not meet quality thresholds, the agent iterates with refined strategies rather than producing a poor response.
- Relevance scoring and threshold checking
- Coverage analysis for multi-part queries
- Fallback strategy execution
Agentic RAG Use Cases
Where standard RAG fails, agentic RAG adapts. These use cases require the dynamic retrieval strategies that only autonomous agents provide.
Complex Research Queries
Answer multi-faceted research questions that require information from different document types, time periods, and data sources. The agent decomposes the query, searches relevant namespaces, and synthesizes findings from across your knowledge base.
Multimodal Question Answering
Handle questions that span modalities -- 'show me the video clip where the CEO discussed Q4 results and the related financial slides.' The agent routes sub-queries to video, audio, and document retrievers and assembles a multimodal response.
Customer Support Escalation
When standard RAG fails to find a resolution, agentic RAG iterates -- searching knowledge bases, past tickets, product documentation, and internal wikis with progressively refined queries until it finds relevant context for the support agent.
Compliance and Audit
Answer regulatory queries that require cross-referencing multiple document sources -- policies, procedures, audit logs, and communications. The agent systematically retrieves from each source and identifies gaps in compliance documentation.
Standard RAG vs. Agentic RAG vs. Fine-Tuning
Each approach has trade-offs. Agentic RAG offers the best balance of accuracy, freshness, and adaptability for complex retrieval tasks.
| Aspect | Standard RAG | Agentic RAG | Fine-Tuning |
|---|---|---|---|
| Retrieval Strategy | Fixed pipeline (embed, search, retrieve, generate) | Dynamic (agent selects strategy per query) | No retrieval (knowledge baked into model weights) |
| Query Handling | Single query, single search | Query decomposition, reformulation, multi-step | Direct generation from internal knowledge |
| Multi-Source Search | Single index or namespace | Multiple retrievers, namespaces, and tools | No external search |
| Error Recovery | No recovery (returns poor results as-is) | Self-correcting (refines query and retries) | No recovery (hallucination risk) |
| Freshness | Up-to-date (retrieves from live index) | Up-to-date (retrieves from live index) | Stale (limited to training data cutoff) |
| Cost | Low (single retrieval + generation call) | Medium (multiple retrieval + reasoning calls) | High (training cost) + low (inference cost) |
Build Agentic RAG in Minutes
Define retriever tools, configure agent behavior, and let the agent autonomously manage retrieval for complex queries.
from mixpeek import Mixpeek
client = Mixpeek(api_key="YOUR_API_KEY")
# Define retriever tools the agent can use
video_retriever = {
"name": "video_search",
"namespace": "corporate-videos",
"stages": [
{
"type": "feature_search",
"method": "hybrid",
"query": {"modalities": ["video", "text"]},
"limit": 15
},
{"type": "rerank", "model": "cross-encoder", "limit": 5}
]
}
document_retriever = {
"name": "document_search",
"namespace": "company-docs",
"stages": [
{
"type": "feature_search",
"method": "hybrid",
"query": {"modalities": ["text", "image"]},
"limit": 20
},
{
"type": "filter",
"conditions": {
"metadata.status": "published"
}
},
{"type": "rerank", "model": "cross-encoder", "limit": 5}
]
}
# Execute agentic RAG with multiple retriever tools
response = client.agents.query(
query="What were the key decisions made in last month's board meeting and how do they relate to the Q4 financial targets?",
tools=[video_retriever, document_retriever],
config={
"max_iterations": 5,
"decompose_query": True,
"evaluate_results": True,
"min_confidence": 0.7
}
)
# The agent autonomously:
# 1. Decomposed into sub-queries (board meeting + Q4 targets)
# 2. Searched video namespace for board meeting recordings
# 3. Searched document namespace for financial reports
# 4. Evaluated results and refined queries as needed
# 5. Synthesized a comprehensive answer
print(f"Answer: {response.answer}")
print(f"Iterations: {response.iterations}")
print(f"Sources used: {len(response.sources)}")
for source in response.sources:
print(f" - {source.namespace}: {source.metadata['filename']}")
print(f" Relevance: {source.score}")Frequently Asked Questions
What is agentic RAG?
Agentic RAG is an evolution of retrieval-augmented generation where an AI agent autonomously manages the retrieval process. Instead of a fixed pipeline (embed query, search, retrieve, generate), the agent reasons about which retrieval strategies to use, decomposes complex queries into sub-questions, selects appropriate tools and data sources, evaluates result quality, and iterates until it has sufficient context. The agent uses retrieval as a tool within a larger reasoning loop.
How is agentic RAG different from standard RAG?
Standard RAG uses a fixed retrieval pipeline -- every query goes through the same embed-search-retrieve-generate flow. Agentic RAG adds a reasoning layer where an AI agent dynamically decides how to retrieve information. The agent can reformulate queries that return poor results, search multiple data sources, decompose complex questions into simpler sub-queries, and evaluate whether retrieved context is sufficient before generating an answer.
When should I use agentic RAG instead of standard RAG?
Use agentic RAG when your queries are complex, multi-faceted, or span multiple data sources. Standard RAG works well for simple factual lookups where a single search usually finds the answer. Agentic RAG excels when users ask compound questions, when relevant information is spread across different document types or namespaces, or when query intent is ambiguous and requires refinement.
How does Mixpeek support agentic RAG?
Mixpeek provides the retrieval infrastructure that agentic RAG agents use as tools. You define retriever pipelines as tools the agent can call -- each with its own namespace, search method, filters, and reranking stages. The agent dynamically selects which retrievers to call based on query analysis, passing different sub-queries to different pipelines optimized for specific data types or topics.
Does agentic RAG increase latency compared to standard RAG?
Yes, agentic RAG typically has higher latency because the agent may execute multiple retrieval calls and reasoning steps. However, the trade-off is significantly better answer quality for complex queries. You can control this trade-off with configuration parameters like max_iterations and min_confidence thresholds. For simple queries, the agent often resolves in a single iteration, adding minimal overhead.
Can agentic RAG search across different data modalities?
Yes. This is a key strength of combining agentic RAG with Mixpeek's multimodal infrastructure. An agent can route different parts of a query to different modality-specific retrievers -- video content to video namespaces, documents to text namespaces, and images to visual search namespaces. The agent then aggregates and synthesizes results across all modalities into a unified answer.
How does query decomposition work in agentic RAG?
When the agent receives a complex query like 'Compare our Q3 and Q4 sales performance and identify the top-performing products', it decomposes this into sub-queries: (1) Q3 sales data, (2) Q4 sales data, (3) product performance metrics. Each sub-query is routed to the appropriate retriever pipeline, potentially searching different namespaces or applying different filters. Results are aggregated and the agent synthesizes a comparative answer.
Is agentic RAG better than fine-tuning for domain-specific applications?
Agentic RAG and fine-tuning serve different purposes. Fine-tuning bakes knowledge into model weights -- good for consistent domain terminology and style, but stale immediately after training. Agentic RAG retrieves from live data sources, ensuring answers reflect current information. For most enterprise applications, agentic RAG is preferred because it stays current, provides source citations, and does not require expensive retraining when data changes.