AI BriefingImproving Performance with Structured CodeAgent
Key point
It presents a method of combining CodeAgent with structured JSON output to enhance the reasoning and execution reliability of agents.
Details
We introduce a new approach that combines the flexibility of CodeAgent with the reliability of Structured Generation to improve how AI agents execute actions.
Existing JSON agents offer stable tool calls, but have the drawback of being limited to a predefined set of tools and struggling with complex state management. On the other hand, CodeAgent provides infinite flexibility by writing Python code directly, but is prone to errors when parsing code from markdown.
To address this, the proposed Structured CodeAgent forces the LLM to generate a JSON block containing thoughts (reasoning) and code (executable code).
The key advantages of this approach are as follows:
- Explicit Reasoning: The
thoughtsfield induces the agent to go through a reasoning process before taking any action. - Reliable Parsing: Using a JSON structure eliminates markdown parsing errors.
- Combining Flexibility and Stability: It secures both the powerful capability of code execution and the stability of structured output at the same time.
Benchmark results on SmolBench (GAIA, MATH, SimpleQA, etc.) showed that Structured CodeAgent recorded higher accuracy than existing JSON agents and regular CodeAgents, proving its performance advantage.
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