Agentic AI Benchmark
Designed and evaluated multi-agent AI workflows using OpenClaw to benchmark reasoning, planning, debugging, tool orchestration, and software engineering capabilities of large language models.
Project Detail
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Case Study Notes
Created realistic multi-stage benchmark scenarios reflecting production software engineering workflows.
Standardized evaluation methodologies through reproducible execution environments and structured scoring frameworks.
Identified planning failures, reasoning limitations, safety issues, and tool-use behaviors to improve future model performance.
Produced benchmark datasets supporting reinforcement learning, autonomous agents, and LLM evaluation research.