What is Agentic RAG?
Agentic RAG uses AI agents to plan multi-step retrieval and reasoning, dynamically deciding what to retrieve and when based on intermediate results. Agentic approaches enable complex research-style queries requiring multi-hop reasoning.
This RAG and knowledge systems term is currently being developed. Detailed content covering implementation approaches, best practices, technical considerations, and evaluation methods will be added soon. For immediate guidance on RAG implementation, contact Pertama Partners for advisory services.
Agentic RAG handles the complex multi-hop questions that standard RAG architectures answer incorrectly 40-60% of the time, unlocking enterprise knowledge synthesis across document silos. Companies deploying agentic retrieval for compliance and due diligence workflows reduce research time from hours to minutes by automating cross-reference validation. The capability transforms AI assistants from simple lookup tools into genuine research partners that reason across fragmented information landscapes.
- Agent plans retrieval strategy based on query.
- Multi-hop retrieval and reasoning chains.
- Adaptive to complex informational needs.
- Significantly more complex than standard RAG.
- Higher latency due to planning and iteration.
- Powerful for research and analysis use cases.
- Agent planning overhead adds 2-5 seconds per query; reserve agentic approaches for complex multi-document questions and route simple lookups to faster direct retrieval paths.
- Implement cost guardrails limiting retrieval iterations per query since unconstrained agents can trigger dozens of database queries while pursuing exhaustive answer completeness.
- Monitor agent reasoning traces for retrieval strategy effectiveness, identifying patterns where the agent wastes cycles on unproductive search paths that human-designed pipelines would avoid.
Common Questions
When should we use RAG vs. fine-tuning?
Use RAG for knowledge that changes frequently, needs citations, or is too large for context windows. Fine-tune for style, format, or behavior changes. Many production systems combine both approaches.
What are the main RAG implementation challenges?
Retrieval quality (finding right documents), chunking strategy (preserving context while fitting budgets), and evaluation (measuring end-to-end system performance). Each requires careful tuning for specific use cases.
More Questions
Evaluate retrieval quality (precision/recall), generation faithfulness (answer supported by context), answer relevance (addresses question), and end-to-end accuracy. Use frameworks like RAGAS for systematic evaluation.
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
RAG (Retrieval-Augmented Generation) is a technique that enhances AI model outputs by retrieving relevant information from external knowledge sources before generating a response. RAG allows businesses to ground AI answers in their own data, reducing hallucinations and keeping responses current without retraining the model.
Naive RAG implements basic retrieve-then-generate pattern with simple chunking and single retrieval step, providing baseline RAG functionality without sophisticated optimizations. Naive RAG serves as starting point before adding advanced techniques.
Advanced RAG enhances basic RAG with query rewriting, hybrid retrieval, reranking, and iterative refinement to improve retrieval quality and answer accuracy. Advanced techniques address naive RAG limitations for production deployments.
Modular RAG decomposes RAG pipeline into interchangeable components (retriever, reranker, generator) enabling flexible composition and optimization of each stage independently. Modular design supports experimentation and gradual improvement.
Self-RAG enables models to decide when to retrieve information and critique their own outputs for factuality, improving efficiency and accuracy by avoiding unnecessary retrieval. Self-RAG adds adaptive retrieval and self-correction to standard RAG.
Need help implementing Agentic RAG?
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