Agentic RAG Architectures
Multi-Agent Retrieval Systems at Terabyte Scale
Context & Problem
Traditional RAG systems struggle with scale, context window limitations, and quality degradation as knowledge bases grow. Enterprise deployments require sophisticated orchestration to maintain response quality across massive, heterogeneous data estates.
Solution & Architecture
Developed multi-agent parallel architectures where specialized agents handle retrieval optimization, context compression, quality validation, and response synthesis. Memory optimization techniques enable efficient processing of terabyte-scale knowledge bases while maintaining sub-second response times.
Key Components
- Multi-layer architecture with clear separation of concerns
- Integration with enterprise systems and data sources
- Scalable infrastructure designed for high availability
- Security and governance built into the core design
Impact
Achieved significant improvements in retrieval precision and response quality at enterprise scale, with real-time hallucination detection and continuous quality feedback loops ensuring production-grade reliability.
What's Next
- Reinforcement learning for dynamic retrieval strategy optimization
- Ontological learning for continuous knowledge graph evolution
- Forward-thinking entity-linking for high-dimensional feature spaces