Evaluating Agentic Performance #
When benchmarking for agentic workflows, standard MMLU or HumanEval scores are almost useless. What actually matters is the "Success Rate per Task" over multiple iterations.
Currently the gold standard for agentic loops. It has a superior ability to parse stack traces and actually fix the bug rather than just apologizing and repeating the same mistake.Claude3.5 Sonnet:GPT-4o: Strong at initial scaffolding, but often gets stuck in "looping" behavior where it applies the same failed fix three times in a row.DeepSeek-V3: Surprisingly competitive in raw logic, though its ability to navigate complex file structures in a real-world repo is slightly behind Sonnet.
The Critical Role of Agentic Test Processes #
A robust AI workflow requires the agent to write its own tests before writing the implementation. If the agent defines the "definition of done" via a test suite, the hallucination rate drops significantly.
The ideal agentic loop looks like this:
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Analysis: Agent maps the codebase and identifies the change point.
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Test Generation: Agent writes a failing test case (TDD style).
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Implementation: Agent writes the code to make the test pass.
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Verification: Agent runs the test in a sandboxed environment.
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Refinement: If the test fails, the agent uses the error log to pivot.
For those building their own LLM agent, focusing on the "Verification" step is more important than the prompt engineering of the "Implementation" step. The model's ability to self-correct is the only way to achieve production-grade code without constant human babysitting.
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