---
name: agent-quality-review
description: Review a generated a2a-pack agent before sandbox testing or deploy. Use to catch broken cards, missing schemas, fake tools, unwired DeepAgents skills, missing runtime mirrors, unsafe IO, and stale live Agent Card schemas.
---
# Agent Quality Review

Before `test_agent_in_sandbox` and before deploy, review the generated project
as source code, not just as text.

## Required Files

Every generated project needs:

- `agent.py`
- `a2a.yaml`
- `requirements.txt`
- Optional `skills/<skill-name>/SKILL.md` bundles for nontrivial DeepAgents
  behavior

Run `list_agent_files` and read the files that matter. If the project was
deployed before, use `sync_agent_workspace_from_repo` before editing when the
builder needs to continue from the current managed repo source.

## Agent.py Review

Check these items:

- Public `@skill` methods are async and have `RunContext[...]` plus typed JSON
  arguments.
- The public schema is small and stable. No unbounded command strings, hidden
  prompt fragments, or provider credentials as public inputs.
- The implementation reads `ctx.llm` when `llm_provisioning` is
  `CALLER_PROVIDED`.
- Any use of DeepAgents file tools passes `backend=ctx.workspace_backend()`.
- Any project skills are seeded into the backend and passed with
  `skills=skill_sources or None`.
- Custom subagents that need project skills include their own `skills` field.
- Deterministic tools do exact work; no fake canned-response tools.
- File-producing skills call `ctx.write_artifact` and `ctx.emit_artifact`.
- Commands that create downloadable outputs run through `ctx.workspace_shell`
  or `ctx.workspace_python`; `/workspace` writes persist directly and other
  changed sandbox rootfs files are mirrored under `outputs/rootfs-captures/...`.
  Plain subprocesses in the agent container are not durable workspace writes.
- External calls, secrets, workspace access, pricing, and resources are
  declared on the class.

## A2A YAML Review

Mirror deployment-only details in `a2a.yaml` because the platform reads it
before importing user code:

```yaml
name: research-agent
version: 0.1.0
entrypoint: agent:ResearchAgent
expose:
  public: true
runtime:
  apt_packages: [ffmpeg]
  resources:
    cpu: "2"
    memory: 2Gi
    max_runtime_seconds: 900
```

Use `runtime.apt_packages` only for Debian system binaries. Python packages
belong in `requirements.txt`.

## Sandbox And Live Card

Always run `test_agent_in_sandbox(name)` before deploy. Treat nonzero exit as
source failure, then read stderr, edit, and rerun.

After `cp_deploy_tarball`, compare `live_skills[].input_schema` with the
actual public `@skill` signatures. If a live schema is missing an argument or
shows an old shape, refresh or redeploy until the live Agent Card matches the
source.

## Minimal Good Result

For most generated agents, a good result is one public A2A `@skill`, one
workspace-backed DeepAgent, one or two internal DeepAgents skills, and a few
small deterministic tools. Resist broad endpoint sets unless the user asked
for a multi-operation agent.