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SuperDispatch

Status and limits

SuperDispatch is early. These docs describe what the current source does, and this page says what it does not.

Where it stands

Section titled “Where it stands”
  • Placement is proven end to end on staging: filtering, scoring, holds in tiers, fairness across boards and the placement window, with decisions settled against what became of each card.
  • v0.0.1 runs in production, with no board opted in yet. A deploy opts no board in, so in production it sweeps nothing until the operator opts a board in.
  • The settings that widen what it does are off: the candidate set is the roster (SUPERDISPATCH_CANDIDATES=roster), the dispatch policy is not asked (SUPERDISPATCH_POLICY=off) and learned routing is off (SUPERDISPATCH_LEARNED=off).

What it does not do yet

Section titled “What it does not do yet”

The dispatch policy is not enforced on pull. SuperPipeline does not check Accounts’ dispatch policy when an agent claims. With SUPERDISPATCH_POLICY=on, SuperDispatch will not hold a card for an agent the policy refuses, but that agent can still claim the card by pull. Until SuperPipeline enforces it, policy here is advice like the rest of SuperDispatch. See The dispatch policy.

Decisions are not published as events. The charter decision says every decision is published through treaty’s relay, so SuperWitness can audit it and chat can show it. That needs a decision event in treaty’s catalogue first. Until then, read decisions on the operator API.

Suggestions do not reach chat. Roster suggestions and supply proposals are kept in SuperDispatch’s own store and read on the operator API (GET /v1/suggestions). Nothing posts them to a board’s room yet.

Station filters need AgentPod’s capability catalog. A stage’s placement (isolation, models, tools, credentials) filters candidates only when AgentPod answers its capability registry (GET /api/catalog/stations) for this service. A hub without the catalog read, or one that refuses it, leaves those needs unchecked: the pass logs that and places as if none were stated. See Station filters.

Learned routing needs data first. The dataset, training and shadow mode exist, but a model fitted on a few dozen decisions is noise. It is off by default and should run in shadow long before it places anything. See Learned routing.

Cards carry no labels yet. The dispatch policy is asked about an agent, a board stage, labels and capabilities, but SuperPipeline cards have no labels, so none are sent.

The stage record joins claims, not results. A learned model’s target is whether the agent claimed the card. Whether the claimed run then succeeded is SuperWitness’s to say, and is not joined into the dataset yet. The scoring formula does read finished runs from SuperWitness, when it is set.

The reservation window is not settled. The charter left its length to be measured against real claim latency. The defaults (60 seconds for the winner, 30 for each runner-up, a 10-second placement window) are where it starts.