agentclash-challenge-pack-planner

Agent evaluation requires defining task boundaries, execution modes, and scoring strategies before writing test code. The challenge-pack planner structures these elements into challenges, input sets, and evaluation specifications to prevent wasted effort on poorly defined benchmarks.

The best way to structure benchmark packs is by defining execution modes like prompt_eval versus native, and applying scoring strategies. This creates structured, executable challenge packs suitable for CI gates and regression suites for AI agents.

To define task boundaries and input sets, you specify the exact challenges, required tools, and validation logic your agent must navigate. This structures edge-case testing by ensuring every input has a corresponding evaluation specification.

Yes, you can use prompt_eval and native execution modes when creating AI agent benchmarks. The planning process explicitly structures these execution modes alongside task boundaries and scoring strategies within the challenge-pack design.

You need a structured challenge pack when transitioning from a vague evaluation idea to an executable regression suite or CI gate. It bridges the gap by defining input sets and scoring strategies, preventing wasted effort on poorly defined benchmarks.

Yes, this approach supports both deterministic and LLM-judged scoring strategies. Evidence-based design ensures every test case within the challenge pack has a clear pass/fail criteria regardless of the chosen validation logic.