Reducing Artifact Data¶

This model-assisted example combines two tool results. It requires a tool-capable model; a requested strategy does not guarantee the model follows it.

End-to-End Multi-Artifact Reduction¶

The following program exposes two large datasets as ordinary Kedi tools. The agent receives compact references, joins the hidden payloads in CodeMode, and returns only the aggregate needed by the template:

> approval: allow
> artifacts:
    threshold: 1kb
    preview_chars: 120

@load_services() -> list:
    ###
    Return service ownership records.
    ###
    = ```
    return [
        {
            "service_id": f"svc-{index:04d}",
            "owner": f"team-{index % 20:02d}",
        }
        for index in range(5000)
    ]
    ```

@load_incidents() -> list:
    ###
    Return unresolved P1 incident records.
    ###
    = ```
    return [
        {
            "service_id": f"svc-{index % 5000:04d}",
            "severity": "P1",
            "unresolved": True,
        }
        for index in range(12000)
    ]
    ```

> use:
    load_services
    load_incidents

>> Call each data tool once. Join their artifact results by service_id with
run_artifact_code. The owner with the most unresolved P1 incidents is [owner],
with [count: int] incidents. Release both source artifacts after the aggregate
is known.

= <owner> owns <count> unresolved P1 incidents.

Neither large list is copied into model history. The source tool calls produce compact tool_call_result_N references, run_artifact_code returns a small owner/count reduction, and release_artifact frees both payloads after the evidence has been consumed. If the reduction itself crosses threshold, it is stored as an artifact_code_result_N and remains readable after its source artifacts are released.