Managed Agents — Outcomes

An outcome elevates a session from conversation to work : you state what "done" looks like, and the harness runs an iterate → grade → revise loop until the artifact meets the rubric, hits max iterations, or is interrupted. A separate grader (independent context windo. Use it to ground design choices in named patterns, trade-offs and examples.

Editorial note: curated source snapshot published by Collider.club under the MIT License. Source attribution is preserved in the front matter.

Source snapshot

Managed Agents — Outcomes

An outcome elevates a session from conversation to work: you state what "done" looks like, and the harness runs an iterate → grade → revise loop until the artifact meets the rubric, hits max_iterations, or is interrupted. A separate grader (independent context window) scores each iteration against your rubric and feeds per-criterion gaps back to the agent.

The SDK sets the managed-agents-2026-04-01 beta header automatically on all client.beta.sessions.* calls; no additional header is required for outcomes.

The user.define_outcome event

Outcomes are not a field on sessions.create(). You create a normal session, then send a user.define_outcome event. The agent starts working on receipt — do not also send a user.message to kick it off.

You can collapse both calls into one by passing a single user.define_outcome in the session's initial_events array — same event, same rules, one round trip (see shared/managed-agents-core.md → Seeding a session with initial_events). More than one user.define_outcome in that array, or one without a rubric, rejects the whole create with a 400.

session = client.beta.sessions.create(
    agent=AGENT_ID,
    environment_id=ENVIRONMENT_ID,
    title="Financial analysis on Costco",
)

client.beta.sessions.events.send(
    session_id=session.id,
    events=[
        {
            "type": "user.define_outcome",
            "description": "Build a DCF model for Costco in .xlsx",
            "rubric": {"type": "text", "content": RUBRIC_MD},
            # or: "rubric": {"type": "file", "file_id": rubric.id}
            "max_iterations": 5,  # optional; default 3, max 20
        }
    ],
)

The event is echoed back on the stream with a server-assigned outcome_id and processed_at.

Writing rubrics. Use explicit, gradeable criteria ("CSV has a numeric price column"), not vibes ("data looks good") — the grader scores each criterion independently, so vague criteria produce noisy loops. If you don't have a rubric, have Claude analyze a known-good artifact and turn that analysis into one.

Outcome-specific events

These appear on the standard event stream (sessions.events.stream / .list) alongside the usual agent.* / session.* events.

span.outcome_evaluation_end.result

{
  "type": "span.outcome_evaluation_end",
  "id": "sevt_01jkl...",
  "outcome_evaluation_start_id": "sevt_01def...",
  "outcome_id": "outc_01a...",
  "result": "satisfied",
  "explanation": "All 12 criteria met: revenue projections use 5 years of historical data, ...",
  "iteration": 0,
  "usage": { "input_tokens": 2400, "output_tokens": 350, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 1800 },
  "processed_at": "2026-03-25T14:03:00Z"
}

Checking status & retrieving deliverables

Status — either watch the stream for span.outcome_evaluation_end, or poll the session and read outcome_evaluations:

session = client.beta.sessions.retrieve(session.id)
for ev in session.outcome_evaluations:
    print(f"{ev.outcome_id}: {ev.result}")  # outc_01a...: satisfied

Deliverables — the agent writes to /mnt/session/outputs/. Once idle, fetch via the Files API with scope_id=session.id. This is the same session-outputs mechanism documented in shared/managed-agents-environments.md → Session outputs (including the dual-beta-header requirement on files.list).

Interaction rules & pitfalls

• One outcome at a time. Chain by sending the next user.define_outcome only after the previous one's terminal span.outcome_evaluation_end (satisfied / max_iterations_reached / failed / interrupted). The session retains history across chained outcomes.
• Steering is allowed but optional. You may send user.message events mid-outcome to nudge direction, but the agent already knows to keep working until terminal — don't send "keep going" prompts.
• user.interrupt pauses the current outcome — it marks result: "interrupted" and leaves the session idle, ready for a new outcome or conversational turn.
• After terminal, the session is reusable — continue conversationally or define a new outcome.
• Outcome ≠ session-create field. Don't put outcome, rubric, or description on sessions.create() — outcomes are always sent as a user.define_outcome event.
• Idle-break gate is unchanged. In your drain loop, keep using event.type === 'session.status_idle' && event.stop_reason?.type !== 'requires_action' — do not gate on span.outcome_evaluation_end alone (on needs_revision the session keeps running). See shared/managed-agents-client-patterns.md Pattern 5.

For the raw HTTP shapes and per-language SDK bindings beyond Python, WebFetch https://platform.claude.com/docs/en/managed-agents/define-outcomes.md (see shared/live-sources.md).

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