Experiments

Experiments define how eval cases run: target or target matrix, setup, scripts, timeout, sandbox, case filters, and repeat-run policy. Eval files stay focused on what is tested: prompts, datasets, assertions, and task fixtures.

Experiment YAML

Committed experiments conventionally live under experiments/:

name: baseline
target: codex-gpt5
evals: "agent-*"
timeout_seconds: 720
repeat:
  count: 4
  strategy: pass_at_k
  cost_limit_usd: 2.00
setup:
  - script: bun install
scripts:
  - build

Wire fields use snake_case. AgentV translates to internal camelCase when it loads the file.

Repeat runs

repeat is the full AgentV replacement for the old eval-level execution.trials shape. It supports the same core strategies:

repeat:
  count: 3
  strategy: mean
  cost_limit_usd: 1.50

Supported strategies:

Strategy	Behavior
pass_at_k	Uses the best passing attempt; early-exits by default unless the experiment sets early_exit: false
mean	Aggregates repeated attempt scores by mean
confidence_interval	Uses the lower bound of a 95% confidence interval as the conservative score

repeat.cost_limit_usd caps repeat-run spend. repeat.costLimitUsd is also accepted for prerelease trial-schema parity, but new YAML should use cost_limit_usd.

Vercel-compatible shorthand

AgentV also accepts Vercel-style top-level runs and early_exit:

runs: 4
early_exit: true

This is shorthand for a pass_at_k repeat run. Use repeat when you need AgentV-specific strategy or cost-limit fields.

Do not set both repeat and runs in the same experiment. repeat is the canonical AgentV shape; runs exists only for Vercel-compatible shorthand.

Vercel defines the requested run count at the experiment level. Some result summaries show fewer actual runs for a case because earlyExit: true stops remaining attempts after the first pass; smoke runs can also force one run. AgentV follows the same experiment-level placement while keeping the richer repeat block for AgentV strategies.

Repeat-enabled cases use a Vercel-style physical layout with AgentV aggregate provenance:

<run-dir>/index.jsonl
<run-dir>/summary.json
<run-dir>/<suite>/<case-id>/summary.json
<run-dir>/<suite>/<case-id>/run-1/result.json
<run-dir>/<suite>/<case-id>/run-1/grading.json
<run-dir>/<suite>/<case-id>/run-1/metrics.json
<run-dir>/<suite>/<case-id>/run-1/timing.json
<run-dir>/<suite>/<case-id>/run-1/transcript.json
<run-dir>/<suite>/<case-id>/run-1/transcript-raw.jsonl
<run-dir>/<suite>/<case-id>/run-1/outputs/answer.md

The repeated case aggregate folder uses summary.json for run-count, pass-rate, fingerprint, and flattened snake_case timing fields such as mean_duration_ms. Each run-N/result.json is the per-attempt manifest and includes grading_path, transcript/output paths, and embedded timing/o11y metrics. Each attempt also keeps AgentV grading.json, metrics.json, and timing.json sidecars for detailed inspection. Root index.jsonl and root summary.json remain stable for existing CI summary scripts and uploaded artifact consumers.

Targets and setup

Experiments reuse targets from .agentv/targets.yaml; they do not define a new provider registry.

targets:
  - copilot
  - claude
  - name: gemini-with-hooks
    use_target: gemini

Setup and scripts belong on the experiment because they are often the A/B variable:

setup:
  - script: cp skills/with-docs/AGENTS.md AGENTS.md
scripts:
  - script: bun test
    timeout_seconds: 120

Running experiments

Run a specific experiment:

bun agentv eval evals/suite.eval.yaml --experiment experiments/default.yaml

If no experiment is passed, AgentV checks .agentv/config.yaml for a default:

experiments:
  default: experiments/default.yaml

If no default is configured, AgentV keeps the old behavior and uses the default experiment label.

Schema

The generated JSON Schema is available at skills-data/agentv-eval-writer/references/experiment-schema.json.