TinyFish Details Production-Grade Agentic Search Architecture for Deep Research Agents

TinyFish has published a technical breakdown of the agentic search architecture powering its deep research agents, covering how it structures search loops, evidence aggregation, and source credibility weighting for production AI agent deployments. The architecture addresses a common failure mode in naive agentic search implementations: retrieving lots of text without a principled method for synthesizing, ranking, or grounding the agent's conclusions in high-quality sources. Key components described include iterative query reformulation, confidence-weighted evidence accumulation, and termination criteria that prevent agents from searching indefinitely without convergence. For developers building research or retrieval-augmented agents, this is a practical reference architecture that goes beyond basic RAG patterns and addresses the harder problems of multi-step information gathering. The publication continues a useful trend of AI product companies sharing architectural details that accelerate the broader developer community's ability to build reliable agentic systems.