Agentic Search Models
8 hours ago
- #Agentic Search
- #Domain-Specific Models
- #Retrieval Systems
- Traditional retrieval stacks involve manual, programmatic integration of components like embeddings, rerankers, query understanding, and BM25, each operating independently without a holistic view.
- Agentic search models, such as SID-1 and Waldo, are emerging as specialized LLMs trained to orchestrate the entire search process, using tools and context to solve user queries more intelligently.
- Unlike frontier models like GPT-5, which are trained on general web search, agentic search models can be tailored to specific domains (e.g., e-commerce, fashion) to address niche user needs and improve relevance in the 'last 20%' of challenging cases.
- These models enable a shift from monolithic, complex search pipelines to simpler retrieval primitives (e.g., keyword search, embeddings with filters), with the agent managing orchestration for scalability and efficiency.
- The future may see a proliferation of domain-specific agentic search models, similar to the variety of embedding models available, potentially transforming search infrastructure by integrating query understanding, hybrid search, and domain expertise.