# Native Agent Evals

> Evaluate or audit Codex native Multi-Agent V2 behavior with small reproducible probes and structural rollout evidence. Use for V2 surface checks, direct-tool lifecycle behavior, bounded fanout, interruption semantics, resume behavior, or skill-invocation evidence.

- Skill: `kbediako/native-agent-evals` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add kbediako/native-agent-evals`
- Raw SKILL.md: https://api.skillmd.com/api/skills/kbediako/native-agent-evals/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: Kbediako (https://skillmd.com/u/kbediako)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/kbediako/native-agent-evals

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# Native Agent Evals

## Define the claim

1. State one observable behavior to test.
2. Define pass, fail, and inconclusive outcomes before running.
3. Prefer the smallest read-only probe that can falsify the claim.
4. Exclude production mutations unless the user explicitly requests them.

Load [native-subagents-first](../native-subagents-first/SKILL.md) before running native agents. Follow it as the lifecycle, model, budget, resume, and direct-tool contract.

## Hold the worker fixed

Compare treatment and control under the same Astra worker condition:

- model, effort, role, tool surface, prompt, permissions, and artifact state stay fixed;
- only the named treatment changes;
- behavior-level tests repeat before promotion.

Mark a comparison confounded when the worker condition differs.

## Run a minimal probe

Use one or more short V2 scenarios:

- surface: inspect direct collaboration tools visible to a tiny read-only child;
- lifecycle: spawn, wait for the final answer, and inspect status;
- wait: distinguish observation mechanism from continuation lifetime;
- fanout: use two independent read-only scopes only when fanout is under test;
- interrupt: interrupt a harmless bounded task and inspect returned status;
- resume: reconcile an existing child after steering or compaction;
- invocation: distinguish skill loaded, read, announced, and applied.

Consume and verify the child final answer. Status is lifecycle evidence, not task-result evidence.

## Audit rollouts

Read [references/rollout-audit.md](references/rollout-audit.md) before inspecting Windows or WSL JSONL, classifying invocation, or deduplicating resumed history.

Parse JSONL structurally. Treat raw `rg` matches as candidate locations only. Exclude the current audit family. Report exact session IDs, source paths, call IDs, names, namespaces, arguments, outputs, and status transitions.

For outbound evidence, use `scripts/export_structural_rollout.py` with a schema-v3 spec. Keep:

- exact source hash pinning and fresh-byte verification;
- bounded selectors and session-turn binding;
- one-turn coherence and final-answer/completion equality;
- exact selected calls, arguments, outputs, and complete answer;
- physical and logical replay deduplication;
- study-wide worker expectations, actual cross-run worker invariance,
  classification, and fail-closed promotion-candidate gates.

Never attach a source rollout or hand-author evidence presented as independently auditable.

## Report

- Claim and fixed worker condition.
- Treatment, control, and repetition count.
- Pass, fail, or inconclusive result.
- Final child evidence and parent verification.
- Source hashes, paths, session IDs, selectors, and exact direct-tool evidence.
- Deduplication, classification, worker matching, and promotion status.
- Confounds, mutations, and remaining uncertainty.