Agentic AI Reference Architecture

Knowledge & Data · Software component

Answer Synthesizer

Software componentKnowledge & DataKnowledge & DataVariation point (abstract)arc:AnswerSynthesizer

A software component that prompts an LLM to turn a question plus retrieved or queried results into a grounded natural-language answer.

Responsibility. Generates a natural-language answer grounded in retrieved results.

Also known as: Response Agent, Response Generator, QA Answer Generation, Response Generation Agent, Response generator service, Generation layer, Answer generation

is evaluated by; is triggered byreceives data from; is routed to byis configured by; is constrained byinvokes; receives data frominvokes; receives data fromis guarded by; is triggered byinvokesinvokesemits telemetry todeployed ondeployed onis invoked byis orchestrated byis orchestrated byis orchestrated byinvokesis monitored byinvokesLLM Judge: is evaluated by; is triggered byLLM JudgeIntent Router: receives data from; is routed to byIntent RouterSystem Prompt Template: is configured by; is constrained bySystem Prompt TemplateContext Assembler: invokes; receives data fromContext AssemblerKnowledge Retrieval Agent: invokes; receives data fromKnowledge Retrieval AgentDual-Agent Critic: is guarded by; is triggered byDual-Agent CriticLLM Inference Service: invokesLLM Inference ServiceInference Server: invokesInference ServerTrace Collector: emits telemetry toTrace CollectorGPU Node: deployed onGPU NodeContainer Orchestrator: deployed onContainer OrchestratorReAct Agent Controller: is invoked byReAct Agent ControllerSupervisor Agent: is orchestrated bySupervisor AgentState-Graph Orchestrator: is orchestrated byState-Graph OrchestratorRAG Query Orchestrator: is orchestrated byRAG Query OrchestratorRetry Handler: invokesRetry HandlerOnline Evaluator: is monitored byOnline EvaluatorOpenAI-Compatible Inference API: invokesOpenAI-Compatible Infere…+24 more (see relationships)
Direct neighbourhood (hover for relationship types)

Variants

VariantWhen to choose
Multi-Hop Answer SynthesizerChoose when answers must combine sub-answers from multiple documents and cite the supporting sources for verification.
Multimodal Answer Synthesizer—

Relationships

deployed on structural

is configured by structural

invokes dependency

is cached by dependency

is invoked by dependency

emits telemetry to dynamic

is routed to by dynamic

is triggered by dynamic

receives data from dynamic

sends data to dynamic

fails over to control

is constrained by control

is guarded by control

is orchestrated by control

is scaled by control

is evaluated by assurance

is monitored by assurance

Design guidance

Quantitative guidance

As stated by the sources; verify before use.

Classification

Patterns
Citation of sourcesResults-to-prose generationCitation generation from source_doc/chunk_index metadata
Technologies
LangChain GraphCypherQAChain
Quality attributes
Transparency and accountability (NIST AI RMF: accountable and transparent)Interaction capability (ISO/IEC 25010)Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)

Sources

  1. Ch1.3: T. Nguyen, "Multi-Agent Systems," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 1.3. ISBN: 9798244538229.
  2. Ch1.7A: T. Nguyen, "Relational Reasoning with Knowledge Graphs - The Fundamentals, Integration, and Extraction," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 1.7A. ISBN: 9798244538229.
  3. Ch1.7B: T. Nguyen, "Relational Reasoning with Knowledge Graphs - Hybrid RAG+KG Integration," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 1.7B. ISBN: 9798244538229.
  4. Ch2.1: T. Nguyen, "Framework Landscape and Selection," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 2.1. ISBN: 9798244538229.
  5. Ch2.7: T. Nguyen, "Multimodal RAG Approaches," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 2.7. ISBN: 9798244538229.
  6. Ch2.9: T. Nguyen, "Streaming and Real-Time Responses," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 2.9. ISBN: 9798244538229.
  7. Ch3.10: T. Nguyen, "Efficiency Metrics," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.10. ISBN: 9798244538229.
  8. Ch4.2: T. Nguyen, "Deployment and Scaling," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.2. ISBN: 9798244538229.
  9. Ch5.8: T. Nguyen, "Semantic Memory," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.8. ISBN: 9798244538229.
  10. Ch5.13: T. Nguyen, "Hybrid Decision Systems Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.13. ISBN: 9798244538229.
  11. Ch6.2B: T. Nguyen, "Production Vector Database Deployment," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.2B. ISBN: 9798244538229.
  12. Ch6.4: T. Nguyen, "Data Quality Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.4. ISBN: 9798244538229.
  13. Ch6.5: T. Nguyen, "Production RAG Systems," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.5. ISBN: 9798244538229.
  14. Ch8.1: T. Nguyen, "Latency Metrics," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 8.1. ISBN: 9798244538229.
  15. Ref2.07: NVIDIA Developer, "Building multimodal AI RAG with LlamaIndex, NVIDIA NIM, and Milvus | LLM app development," YouTube. Accessed: Sep. 26, 2026. [Online Video]. Available: https://www.youtube.com/watch?v=NaT5Eo97_I0
  16. Ref6.01: S. Schürch, "How to Make Your LLM More Accurate with RAG & Fine-Tuning," Towards Data Science, Mar. 11, 2025. [Online]. Available: https://towardsdatascience.com/how-to-make-your-llm-more-accurate-with-rag-fine-tuning/
  17. Ref7.07: E. Li, V. Bellotti, R. Kraus, and R. Kao, "Build a retrieval-augmented generation (RAG) agent with NVIDIA Nemotron," NVIDIA Technical Blog, Sep. 23, 2025. [Online]. Available: https://developer.nvidia.com/blog/build-a-rag-agent-with-nvidia-nemotron/