[KongchangAI]
PaperAgent Seer: Synthesizing Scenarios from Specification Understanding

Agent Seer

A research method that automatically generates evaluation scenarios for AI agents from tool specifications. By parsing semantic information such as function names, natural language descriptions, and parameter schemas, it synthesizes multi-dimensional test scenarios without executing the tools, and can dynamically adapt to changes in tool APIs.

Timeline (last 90 days)

Sep 11

Agent Seer proposes that tool specifications themselves already encode sufficient semantic information to synthesize realistic evaluation scenarios without the need for manual curation or actual tool execution.

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Sep 11

Agent Seer performs scenario inference along three dimensions—single-tool usage patterns, multi-tool combination patterns, and multi-turn dialogue iteration processes—based on a tool's function name, description, and parameter schema.

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Sep 11

Agent Seer has dynamic adaptability: when a tool API changes, only the corresponding specification document needs to be updated to regenerate matching evaluation scenarios, without requiring manual redesign of the entire benchmark.

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Sep 11

The core goal of Agent Seer is to synthesize evaluation scenarios from an understanding of tool specifications (Synthesizing Scenarios from Specification Understanding).

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