TinyFish Details Production AI Agent Search Architecture

The guides define a production agentic search architecture and share lessons learned from building a deep research system.

The architecture examines how an autonomous search agent can plan retrieval, evaluate evidence, handle failures, and manage the boundary between model reasoning and deterministic execution. TinyFish's system is built on four web primitives: TinySearch, TinyFetch, TinyBrowser, and TinyAgent, operating via a seven-phase control loop around a base model.

According to OpenBenchmarks, TinyFish achieved a 79.0% completion rate on coding tasks, with the lowest median task cost of $0.050 and token footprint. This performance highlights the importance of rigid control loops, failure-specific retries, and unified reasoning over standalone planners.

Agentic search architecture allows an AI agent to plan retrieval, access sources, evaluate evidence, and adapt its approach as new information becomes available. The model handles reasoning, while the surrounding application manages state, tool execution, evidence handling, runtime policies, and output validation.

TinyFish's architecture incorporates foundational concepts from a February 2026 architecture preprint that decouples reasoning from execution through typed tool interfaces. It establishes system guardrails, including detailed tracing and strict caps on tokens, execution time, and overall cost.