VI · Multi-AgentEmerging★

Progressive Delegation

also known as Trust-Graded Handoff, Permission Ratchet

Stage the human-to-agent handoff over time: the agent starts producing drafts a human always reviews; its autonomy expands action-by-action as measured trust accrues.

Context

A team is introducing an agent that will eventually take over parts of a human workflow — drafting code review comments, triaging support tickets, scheduling meetings. The end state is fully autonomous on routine cases; the starting state is human-supervised because trust has not been built.

Problem

One-shot deployment swings between two failure modes. Going fully autonomous on day one yields trust incidents because the team has no measured basis for confidence. Going fully supervised forever yields no learning — the team never accumulates the success-rate data that would justify expansion, and the agent's value is capped at 'faster drafter'. Without a per-action ratchet, autonomy decisions are calendar-driven, not evidence-driven.

Forces

  • Trust must be earned per action class, not per agent.
  • The success-rate window per action must be long enough to be evidence.
  • Demotion when a class regresses must be cheap and visible.
  • Multiple action classes can be at different trust levels simultaneously.

Example

A meeting-scheduling agent runs three action classes: propose-times (autonomous from day one), send-invite (assisted: drafts an invite, human clicks send), and reschedule (autonomous after 200 successful proposals without complaint). After two months reschedule reaches its bar and promotes; a complaint a month later demotes it back automatically.

Diagram

Solution

Therefore:

Tag each action class with a current autonomy level (draft -> assisted-send -> autonomous). For each class the runtime tracks a rolling success-rate window. Promotion fires automatically when the window clears a bar over enough samples; demotion fires when it drops below. The promotion mechanism is the policy of record, not a verbal decision in standup. The same agent runs many action classes at different levels simultaneously.

What this pattern forbids. Agent autonomy on an action class must not be promoted by calendar or seniority; promotion requires the documented success-rate window to clear the bar.

The smaller patterns that complete this one —

  • usesApproval Queue★★— Queue agent-proposed actions for asynchronous human review while the agent continues other work.
  • usesHuman-in-the-Loop★★— Require explicit human approval at defined points before the agent performs an action.

And the patterns that stand alongside it, or against it —

  • complements[crawl-walk-run-automation-gating]
  • complementsAutonomy Slider★— Expose agent autonomy as a continuous adjustable parameter so the same codebase can span scripted assistant to fully autonomous worker without re-architecting.
  • composes-withCost-Aware Action Delegation★— Classify every agent action by risk/cost and route each tier to a different approval policy, bounding the autonomy surface per-action instead of by one global flag.
  • complementsShadow Canary★★— Run a candidate agent version in shadow alongside the champion, comparing outputs without affecting users.

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Used in recipes

References

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