Agentic AI - RAG Tech Stack Layered Blueprint

Building Scalable, Explainable, and Production-Ready AI Systems

The rise of Agentic AI and Retrieval-Augmented Generation (RAG) is transforming how we build intelligent, context-aware applications. But with so many tools and frameworks available, structuring a scalable, production-ready stack can be overwhelming.

To simplify this, I’ve broken down the Agentic AI - RAG Tech Stack into 8 distinct layers, each serving a critical role in the AI pipeline. Whether you're an AI engineer, architect, or tech leader, this modular approach ensures flexibility, explainability, and performance optimization at every stage.

🔹 The 8-Layer RAG Tech Stack

1️⃣ Level-0: Deployment

Where AI meets real-world infrastructure
Tools: Groq (ultra-fast LLM inference), AWS Bedrock, GCP Vertex AI
Role: Scalable hosting, serverless AI, and edge deployment.

2️⃣ Level-1: Evaluation

Measure, optimize, and debug
Tools: LangSmith (tracing), Phoenix (monitoring), DeepEval, Ragas
Role: Track retrieval accuracy, LLM responses, and end-to-end pipeline performance.

3️⃣ Level-2: LLM (Reasoning Engine)

The brain behind the operation
Models: Llama-4, Gemini 2.5 Pro, Claude 4, GPT-5
Role: Choose based on cost, reasoning ability, and task requirements.

4️⃣ Level-3: Framework (Orchestration)

The glue that binds AI workflows
Tools: LangChain (chaining), LlamaIndex (RAG optimization), Haystack
Role: Simplify complex pipelines with pre-built retrievers, agents, and logic flows.

5️⃣ Level-4: VectorDB (Retrieval Layer)

Where knowledge lives and scales
Databases: Pinecone, Chroma, Milvus, Weaviate
Role: High-speed semantic search for dynamic context retrieval.

6️⃣ Level-5: Embedding (Semantic Understanding)

Turning data into meaning
Models: BAAI, Nomic, Ollama, Voyage AI, OpenAI embeddings
Role: Define retrieval accuracy with the right embedding space.

7️⃣ Level-6: Data Extraction (Ingestion Layer)

From raw data to structured knowledge
Tools: Firecrawl, Scrapy (web scraping), Docling, LlamaParse (document parsing)
Role: Pre-process text, PDFs, and web data for RAG pipelines.

8️⃣ Level-7: Memory (Context Persistence)

Retain and recall intelligently
Tools: Zep, Meo, Cognee, Letta
Role: Maintain conversation history and long-term context for agents.

9️⃣ Level-8: Alignment (Safety & Control)

Keeping AI in check
Tools: Guardrails AI, Arize, LangFuse, Helicon
Role: Ensure outputs are safe, ethical, and aligned with user intent.

🔍 Why This Layered Approach?

✔ Modularity – Swap components without breaking the system.
✔ Explainability – Debug failures at each layer.
✔ Scalability – Optimize bottlenecks independently.
✔ Future-Proofing – Adapt to new models and frameworks seamlessly.

🚀 What’s Next?

This stack is just the beginning. As Agentic AI evolves, we’ll see tighter integrations between layers, smarter evaluation tools, and more efficient retrieval methods.

What would you add or change? Let’s discuss how we can refine this blueprint for real-world AI applications!

🔗 About BhuviAI

At BhuviAI Solutions, we specialize in building scalable, open-source-driven AI toolchains and agent-based solutions. This visualization is part of our effort to make AI more explainable, composable, and usable across industries.