TinyFish Breaks Down Agentic Search Architecture for Production AI Agents
MarkTechPost Asif Razzaq
TinyFish published two technical guides describing how to design an autonomous agentic search system that works under production constraints instead of demos. It reported a 79.0% completion rate on OpenBenchmarks coding tasks with a lowest median task cost of $0.050. The guides shift architecture toward a rigid 7-phase control loop with evidence evaluation, failure-specific recovery, and tighter integration of reasoning and deterministic tool execution.
Why it matters
TinyFish published two engineering research articles on agentic search. One defines a 7-phase production architecture with evidence contracts and bounded retries. The other explains why its deep research system dropped a separate planner and per-query subagents. The post TinyFish Breaks Down Agentic Search Architecture for Production AI Agents appeared first on MarkTechPost.
