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anthropics_skills/skills/claude-api/shared/managed-agents-outcomes.md
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Lance Martin 1f630fdf92 Update claude-api skill: Managed Agents July launch wave, partner pricing, tool-runner corrections (#1463)
Syncs the claude-api skill with the current upstream source.

## Managed Agents — five new features

- `effort` on the agent's `model` object (a level string or `{"type": "<level>"}`).
  It is agent-configuration only: setting it in a per-session `model` override is
  silently ignored.
- Optional `version` on agent update, for optimistic concurrency. Omit it for
  last-write-wins.
- `initial_events` on session create, collapsing create plus first send into one
  call. Validation is all-or-nothing and only `user.message` and
  `user.define_outcome` are accepted.
- Environment and memory-store webhooks: four `environment.*` events and three
  `memory_store.*` events.
- Event deltas on per-thread streams. Previews are thread-scoped, so a child
  thread's previews never reach the session-level stream.

## Corrections to behavior the skill already documented

- Tool output offload triggers at 100,000 characters (~25k tokens), not 100K
  tokens, and covers built-in tools rather than MCP alone.
- `system.message` works on four models, checks only the primary model, appends
  system context instead of replacing the prompt, and is accepted during a
  `requires_action` idle when it trails a tool result in the same request.
- Vault-to-MCP credential matching is normalized (scheme and host lowercased,
  default ports and trailing slashes stripped), not byte-exact.
- Multiagent depth greater than 1 is a validation error, not silently ignored.
- Outcome `interrupted` fires even when evaluation never started, and then
  carries an empty-string `outcome_evaluation_start_id`.
- Deployment jitter is 15% of the run interval, floor 5s, cap 9 minutes.
- Webhooks retry three times with jittered 5-120s backoff, then drop silently.
  Auto-disable is duration-based with three named triggers.
- `session.status_terminated` means completion or error.
- `agent.thinking` is a progress signal and carries no thinking content.
- `processed_at` is already populated on first sighting for
  `user.define_outcome`, `user.custom_tool_result`, and `user.tool_result`.
- Session creation does not provision the sandbox.
- Skill `version` applies to Anthropic-authored skills too.
- Console-created vault credentials are header-injection only, and vault
  environment-variable substitution skips secrets in URL paths.
- Console trace URLs need the real workspace ID when the API key is not in the
  Default workspace.

## Elsewhere in the skill

- Note partner pricing under the first-party price table: Microsoft Foundry
  bills at standard API rates, while Amazon Bedrock and Vertex AI are
  partner-operated with separate pricing.
- Correct the tool-runner human-in-the-loop guidance, which previously pointed
  readers at the manual loop for approval gates the runner's per-turn hooks
  already cover.
- Repoint links away from the retired documentation hosts.
- Dedupe eagerly loaded SKILL.md content that the per-section files already
  cover.
2026-07-22 14:20:09 -04:00

6.5 KiB

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
        }
    ],
)
Field Type Notes
type "user.define_outcome"
description string The task. This is what the agent works toward — no separate user.message needed.
rubric {type: "text", content} | {type: "file", file_id} Required. Markdown with explicit, independently gradeable criteria. Upload once via client.beta.files.upload(...) (beta files-api-2025-04-14) to reuse across sessions.
max_iterations int 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.

Event Payload highlights Meaning
span.outcome_evaluation_start outcome_id, iteration (0-indexed) Grader began scoring iteration N.
span.outcome_evaluation_ongoing outcome_id Heartbeat while the grader runs. Grader reasoning is opaque — you see that it's working, not what it's thinking.
span.outcome_evaluation_end outcome_evaluation_start_id, outcome_id, iteration, result, explanation, usage Grader finished one iteration. result drives what happens next (table below).

span.outcome_evaluation_end.result

result Next
satisfied Session → idle. Terminal for this outcome.
needs_revision Agent starts another iteration.
max_iterations_reached No further grader cycles. Agent may run one final revision, then session → idle.
failed Session → idle. Rubric fundamentally doesn't match the task (e.g. description and rubric contradict).
interrupted Emitted whenever a user.interrupt arrives while an outcome is active — even if evaluation hadn't started. In that case outcome_evaluation_start_id is an empty string rather than an event ID, so don't use it as a lookup key without checking.
{
  "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).