TinyFish publishes guides on agentic search architecture
MarkTechPost: TinyFish Breaks Down Agentic Search Architecture for Production AI Agents
An AI search agent can work in a demo but fail under production workloads when retrieval returns incomplete evidence, retries repeat unsuccessful actions, or execution exceeds time and cost limits. TinyFish, which provides web infrastructure for AI agents, has published two technical guides addressing these challenges. The first defines a production agentic search architecture, while the second shares lessons from building a deep research system. Together, they examine how an autonomous search
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An agentic search architecture enables AI agents to plan retrieval, access sources, evaluate evidence, and adapt their approach as new information becomes available by separating model reasoning from application-level execution and tool management. This matters because AI search agents that work in demos often fail under production workloads when retrieval returns incomplete evidence, retries repeat unsuccessful actions, or execution exceeds time and cost limits. The architecture operates through a seven-phase control loop that defines tasks, plans retrieval, selects tools, discovers and retrieves information, evaluates evidence, recovers from failures, and validates outputs, with routing decisions following the information gap rather than a fixed sequence. Key lessons from building production systems include using unified reasoning loops instead of separate planners, replacing subagents with tool calls for repeatable tasks, and prioritizing evidence quality and relevance over source coverage.
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