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Outcomes

The Managed Agents primitive for defining done: you supply a rubric, and the platform runs an iterate, grade, revise loop until the work passes, graded by a separate context.

Hand-drawn line illustration in warm terracotta and cream: a loopy line-drawn hand holds a cream card up against a taller cream tablet marked with three checkmarks, two more cream cards waiting below, a one-line profile face observing.

What it is​

An Outcome is a definition of done attached to a session. Per the Managed Agents docs (verify against platform.claude.com/docs; beta surfaces drift), you send the session an outcome with a rubric, and the harness runs a loop: the agent works, a grader scores the result against the rubric, and if it fails, the agent revises. The loop defaults to 3 iterations with a maximum of 20. The grader is separate from the worker and holds its own context.

Why the separate grader is the point​

Anthropic's guidance keeps returning to one failure mode: the builder must not grade its own work, because a model reviewing its own output inside the same context tends toward self-preferential bias. The evaluator-optimizer pattern in Building effective agents (December 2024) and the trajectory-grading guidance in Demystifying evals for AI agents (January 2026) both split generation from judgment. Outcomes bakes that split into the platform: the grader arrives fresh, reads the rubric and the work, and has no stake in the draft it is scoring.

This is also the platform's structural answer to silent failure. An agent that decides for itself that it finished is exactly the agent that thinks it finished and did not. An agent whose finish line is a rubric applied by someone else has to earn the word done.

What it replaces​

Teams building on the raw API assemble this loop by hand: a generator call, a judge prompt, retry logic, iteration caps, logging of each pass. It is buildable, and the published evidence shows how much the loop matters when built well:

  • Pendo evaluates its Managed Agents-powered Novus against PM-reviewed standards and reports "The agent had a 90%+ success rate against that standard." (source)
  • OffDeal credits eval-driven iteration, not the model alone, for its climb: "Increased eval accuracy from 25% to 85% after switching to the Claude Agent SDK and iterating on MCP design and prompting." (source)
  • CircleCI's Chunk agent closes the loop against reality itself, validating each fix in a real CI pipeline before delivering a green pull request. (source)

How to use it well​

The rubric is the product surface. A vague rubric ("the report should be good") grades nothing; a concrete one ("every claim carries a source link; totals reconcile to the input ledger") turns the loop into leverage. Write the rubric the way you would brief a contractor whose work you will not re-check, because that is the operational bet you are making. Anthropic's own launch-your-agent reference skill treats this as the core move: it grades a founder's new agent "against your own definition of done."

Further Reading​