This article details the integration of the ADK Go Graph Workflow with Google Research's Harness² framework, focusing on Phase 4 and Phase 5 developments. The integration aims to create a standardized bridge, the BenchmarkAdapter, to enable Harness²'s recursive optimization engine to work with the Go Agent. Key design philosophies, including the "Zero-Leakage Barrier" to prevent memorization of answers and "Behavioral Trajectory Contrast" using OpenTelemetry Spans, are explained to ensure the agent's real-world performance and prevent overfitting to benchmarks. AI
IMPACT This integration could lead to more robust and generalizable AI agents by preventing overfitting and improving evaluation methodologies.
RANK_REASON The article details technical integration and design philosophies for an AI agent framework, aligning with research and development in the field. [lever_c_demoted from research: ic=1 ai=1.0]
- ADK Go Graph Workflow
- Behavioral Trajectory Contrast
- BenchmarkAdapter
- Google Research
- Kenta Sato
- kkdai/line-card-assistant
- LINE AI Business Card Assistant
- OpenTelemetry Spans
- Zero-Leakage Barrier
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