Agentic RAG Workflow Design for Knowledge-Based AI Systems

You are an expert AI system architect specializing in knowledge-based AI assistants, agentic workflows, and retrieval-augmented generation systems.

Your task is to design a practical agentic Retrieval-Augmented Generation workflow for the project described below.

Context:

* Project context: [Project context]
* AI assistant or system purpose: [AI assistant or system purpose]
* Target users: [Target users]
* Knowledge sources: [Knowledge sources]
* Relevant files or documents: [Relevant files or documents]
* Preferred AI model or tool stack: [Preferred AI model or tool stack]
* Citation requirements: [Citation requirements]
* Risk level or compliance concerns: [Risk level or compliance concerns]
* Human review requirements: [Human review requirements]
* Definition of done: [Definition of done]

Instructions:

1. Analyze the project requirements and explain what type of RAG workflow is most suitable.
2. Compare basic RAG, enhanced RAG, and agentic RAG for this use case. Highlight the strengths, weaknesses, cost implications, complexity, and best-fit scenarios for each approach.
3. Design the agentic RAG workflow as a clear step-by-step system flow, including:

   * user query intake
   * query classification
   * retrieval planning
   * source selection
   * document retrieval
   * chunk ranking
   * context assembly
   * answer generation
   * citation verification
   * confidence scoring
   * hallucination checks
   * fallback handling
   * human review or escalation
   * final response delivery

4. Propose a retrieval strategy that includes:

   * source prioritization
   * metadata filtering
   * hybrid search, if useful
   * semantic search, if useful
   * keyword search, if useful
   * reranking
   * freshness checks
   * source quality checks

5. Recommend a chunking strategy, including:

   * chunk size
   * overlap
   * metadata fields
   * document hierarchy
   * handling tables, FAQs, PDFs, policy documents, and long-form content

6. Define how tools, APIs, databases, or internal systems should be used to improve accuracy and reduce hallucination.

7. Establish a citation protocol that explains:

   * when citations are required
   * how citations should be selected
   * how unsupported claims should be handled
   * how conflicting sources should be handled
   * how missing information should be reported

8. Design confidence and hallucination controls, including:

   * answerability checks
   * source support checks
   * contradiction checks
   * uncertainty handling
   * refusal or fallback rules

9. Specify fallback behaviors for cases where:

   * no relevant documents are found
   * retrieved sources conflict
   * the user asks for unsupported information
   * confidence is low
   * the request is sensitive or high-risk
   * tool or retrieval systems fail

10. Place human review gates at the right points in the workflow, especially for high-risk, customer-facing, legal, medical, financial, compliance, or brand-sensitive outputs.

11. Define evaluation metrics for:

   * retrieval precision
   * retrieval recall
   * citation accuracy
   * answer faithfulness
   * hallucination rate
   * fallback quality
   * latency
   * user satisfaction
   * human review accuracy
   * overall system reliability

12. Provide an implementation roadmap with phases, recommended priorities, and testing steps.

Constraints:
* Design for modularity, scalability, and maintainability.
* Avoid over-reliance on a single knowledge source.
* Clearly separate facts supported by sources from assumptions or recommendations.
* Include fallback paths for low-confidence or unsupported answers.
* Keep the workflow practical enough for a real product team to implement.
* Do not invent project details that were not provided.
* Where information is missing, state the assumption and explain how it affects the design.

Output format:
Provide a structured design document with the following sections:

1. Executive summary
2. Recommended RAG approach
3. Comparison of basic RAG, enhanced RAG, and agentic RAG
4. Agentic RAG workflow diagram in text form
5. Retrieval strategy
6. Chunking and indexing strategy
7. Tool and API integration plan
8. Citation protocol
9. Confidence and hallucination controls
10. Fallback behavior design
11. Human review gate placement
12. Evaluation metrics
13. Implementation roadmap
14. Risks and mitigations
15. Final recommendations

Verification steps:

* Confirm that every workflow stage has a clear purpose.
* Confirm that retrieval, generation, verification, and fallback are separated.
* Check that citation rules are practical and enforceable.
* Confirm that confidence checks reduce unsupported answers.
* Confirm that human review gates are placed only where they add value.
* Confirm that the implementation roadmap is realistic.

Return the complete structured design document.