Long-Running Agent Job with Claude Code: Result JSON Schema
A production playbook for long-running agent job in cross-industry operations using Claude Code: result json schema, run-scoped inputs, logs, typed results, and artifacts.
Audience: AI product teams running multi-minute workflows
The problem
AI product teams running multi-minute workflows need long-running agent job to run repeatedly against documents, repos, tool access, and output contracts. In cross-industry operations, the pain is not one good answer; it is repeatability, auditability, exception handling, and evidence that survives handoff.
Implementation path
Define the outer result contract once, let the long-running agent job skill own body.data, and reject terminal output that does not match the expected schema.
Tradeoffs and failure modes
Schema enforcement adds upfront design work, but removes prompt parsing from the product surface. For long-running agent job, the practical test is whether a second run can be debugged, retried, and consumed by a product without reading the raw agent transcript.
Result shape
{
"schema_version": "argo.result.v1",
"summary": "long-running agent job completed",
"body": { "type": "long_running_agent_job", "data": {}, "exceptions": [] },
"artifacts": []
}
Run this on Argo