We Built an Alternative to Vector RAG for AI Agent Memory
4 hours ago
- Agentic RAG treats retrieval as a tool the agent can invoke, evaluate, and repeat, moving away from fixed retrieve-then-generate pipelines.
- Traditional RAG uses a fixed sequence: embed query, search vector DB, retrieve chunks, generate answer; agentic RAG lets the agent decide when and how to retrieve.
- Limitations of fixed vector RAG include chunking destroying document structure, embeddings measuring similarity not business relevance, and insufficient single retrieval calls.
- Vector databases and embeddings are not obsolete but become one tool among many, selected for specific tasks rather than mandatory infrastructure.
- Alternatives to vector-based RAG include structured extraction, direct document querying, multi-document processing, SQL, knowledge graphs, and APIs.
- A practical architecture for document agents has layers: format-aware extraction, document operations, agent reasoning, and business actions.
- Agentic RAG is becoming the default pattern for complex workflows requiring dynamic context, multi-step reasoning, and tool orchestration.
- Common mistakes include treating every document problem as semantic search, building vector databases prematurely, and ignoring document relationships.
- The future is a tool-driven information layer combining extraction, processing, search, SQL, APIs, graphs, and human review, with vectors used only when appropriate.