From Static Answers to Autonomous Action: The 9 Layers of the Agentic RAG Tech Stack
Most RAG systems just answer questions. They retrieve static context but do not act. Agentic RAG changes this, transforming large language models into autonomous operators that can retrieve, reason, and execute actions autonomously.
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Beyond Static Retrieval: The Evolution to Action
Most Retrieval-Augmented Generation (RAG) systems just answer. They retrieve static context but don't act.
Agentic RAG changes this — transforming large language models into autonomous operators that can retrieve, reason, and execute.
The Paradigm Shift
Traditional RAG systems operate in a simple loop:
- Receive query → 2. Retrieve context → 3. Generate response
Agentic RAG introduces a fundamentally different approach:
- Understand intent → 2. Plan actions → 3. Execute workflows → 4. Learn from outcomes
This evolution represents the shift from conversational AI to execution-driven AI.
The 9-Layer Agentic RAG Tech Stack
Here's the comprehensive tech stack powering autonomous AI operations:
Level 0 – Deployment
Infrastructure Foundation
- Groq — Specialized AI inference hardware
- AWS — Cloud computing and AI services
- Google Cloud — Scalable AI infrastructure
- Together AI — Collaborative AI deployment
Infrastructure to run and scale models on specialized hardware and cloud platforms.
Level 1 – Evaluation
Quality Assurance Layer
- LangSmith — LLM application development and monitoring
- Phoenix — AI observability and evaluation
- DeepEval — Comprehensive AI testing framework
- Ragas — RAG system evaluation metrics
Test and validate AI outputs for quality and accuracy.
Level 2 – LLMs
The Intelligence Core
- Llama 4 — Meta's open-source language model
- Gemini 2.5 Pro — Google's multimodal AI
- Claude 4 — Anthropic's constitutional AI
- GPT-4o — OpenAI's flagship model
The "brains" that interpret, reason, and generate responses.
Level 3 – Framework
Orchestration Engine
- LangChain — LLM application framework
- LlamaIndex — Data-aware LLM applications
- Haystack — End-to-end NLP framework
- DSPy — Programming framework for LM pipelines
Orchestrate LLMs, tools, and data sources into workflows.
Level 4 – Vector DB
Semantic Storage
- Pinecone — Managed vector database
- Chroma — Open-source embedding database
- Milvus — Scalable vector similarity search
- Weaviate — Vector search with GraphQL
Store embeddings for fast, scalable similarity search.
Level 5 – Embedding
Semantic Understanding
- Nomic — Open-source embedding models
- Ollama — Local LLM and embedding deployment
- Voyage AI — Specialized embedding models
- OpenAI — Industry-standard embeddings
Convert data into vectors for semantic search and retrieval.
Level 6 – Data Extraction
Information Ingestion
- Firecrawl — Web scraping for AI
- Scrapy — Python web crawling framework
- Docling — Document processing pipeline
- LlamaParse — Document parsing for LLMs
Pull in and clean data from the web or documents for AI use.
Level 7 – Memory
Persistent Context
- Zep — Long-term memory for AI assistants
- Mem0 — Personalized AI memory layer
- Cognee — Knowledge graphs for AI memory
- Letta — Persistent memory management
Store context so agents remember past interactions and learn from experience.
Level 8 – Alignment
Governance & Oversight
- Guardrails AI — AI safety and alignment framework
- Arize — ML observability and monitoring
- Langfuse — LLM engineering platform
- Helicone — LLM observability and proxy
Enforce rules, monitor behavior, and trace decisions.
The Open Code Mission Perspective
At Open Code Mission, we see Agentic RAG as part of a larger evolution toward sovereign AI systems. While the tech stack above provides the foundation, true autonomy requires additional considerations:
Data Sovereignty
Unlike traditional RAG systems that may compromise data ownership, our Lumen-based architecture ensures that data remains under user control throughout the agentic workflow.
Verifiable Actions
Through Verum Sphere integration, every autonomous action taken by an AI agent is cryptographically verified and auditable — preventing the "black box" problem that plagues many agentic systems.
Explainable Autonomy
OS Mission provides sub-60ms explainability for all autonomous decisions, ensuring that users understand not just what the AI did, but why it chose that specific action.
The Enterprise Impact
By 2026, Agentic RAG will move AI from static conversation to fully autonomous execution, reshaping:
Enterprise Operations
- Automated workflow orchestration
- Intelligent resource allocation
- Predictive maintenance and optimization
- Real-time decision-making systems
Product Development
- Autonomous testing and validation
- Intelligent feature prioritization
- Automated documentation generation
- Dynamic user experience optimization
Workflow Design
- Self-organizing task management
- Intelligent delegation systems
- Adaptive process optimization
- Context-aware collaboration tools
The Challenges Ahead
While the technology stack is maturing rapidly, several challenges remain:
Trust and Verification
How do we ensure autonomous actions align with user intent and organizational values?
Resource Management
What happens when multiple agents compete for the same computational or data resources?
Error Handling
How do systems recover when autonomous actions produce unintended consequences?
Human Oversight
Where should humans remain in the loop, and where can full autonomy be safely deployed?
Building Responsibly
The shift to Agentic RAG represents tremendous opportunity, but also significant responsibility. At Open Code Mission, we believe the future belongs to systems that enhance human agency rather than replace it.
Autonomous AI should be:
- Transparent in its decision-making
- Accountable for its actions
- Controllable by its users
- Aligned with human values
The Road to 2026
The tech stack is evolving rapidly, but the fundamental shift is already underway. Organizations that begin experimenting with Agentic RAG today will have significant advantages as the technology matures.
The question isn't whether AI will become more autonomous — it's whether that autonomy will serve human flourishing or replace human agency.
References:
Horn, A. (2025) The future of RAG is agentic, and this is the tech stack that will power it. [LinkedIn post] 13 August. Available at: https://linkedin.com (Accessed: 13 August 2025).
Gohel, R. (2025) Agentic RAG Tech Stack [Video graphic]. Referenced in: Horn, A. (2025) The future of RAG is agentic, and this is the tech stack that will power it.
The evolution from static answers to autonomous action represents more than technological progress — it's a fundamental shift in how AI systems interact with the world. The stack is ready. The question is: are we?
