Trace error propagation through a multi-agent session by agent depth — where failures originated, the depth at which they appeared, and how they cascaded up to parent agents — using the Agent Monitor workflow intelligence API and the session event stream. Use when a multi-agent run failed and you need to find the origin and blast radius of the failure.
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Trace where a multi-agent session's failures started and how far they spread.
The user provides: $ARGUMENTS
A session ID. If empty, fetch GET /api/sessions?limit=1, but prefer the most recent session whose status is error or abandoned; state which one you picked.
| Endpoint | Returns |
|---|---|
GET /api/workflows/{sessionId} | The errorPropagation dataset: failures grouped by agent depth, with originating depth and cascade paths to parents |
GET /api/events?session_id={sessionId} | The event stream — corroborate with APIError, SubagentStop, and failing PostToolUse events (event_type, tool_name, summary, timestamp) |
From errorPropagation: total errors, the depth where the first error originated, and how many distinct agents were affected.
Origin depth: d · Errors: N · Agents affected: M
| Depth | Errors originated | Errors inherited from children | Net failing agents |
|---|---|---|---|
| Show whether failures concentrate deep in the tree (leaf subagents) or shallow (orchestrator). |
For each originating failure, the path it propagated along:
debugger (depth 2, tool failure) → code-review (depth 1, marked error) → root (depth 0, aborted)
Tie each step to a concrete event from /api/events (event_type + tool_name + timestamp) where available.
Break errors down by type from the event stream: APIError vs failing tool calls vs SubagentStop with error status. Note the most frequent tool involved in failures.
Whether failures were contained at the depth where they originated or leaked to parents. Name any parent that aborted solely because a child failed — a candidate for better error handling / isolation.
errorPropagation and the event stream; never invent failures or causes.npm start from the repo root.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Inspect fired CCAM alerts and manage alert rules for token thresholds, event patterns, inactivity, and status duration. Use when acknowledging alerts, creating or editing a rule, checking cooldowns, or connecting alert rules to webhook targets.
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Identify anomalous sessions using Agent Monitor data — cost outliers from the pricing engine, token anomalies (cache miss spikes, compaction baseline surges), unusual event type ratios (PreToolUse/PostToolUse gaps, APIError clusters), behavioral deviations from workflow intelligence (complexity score outliers, error propagation anomalies), and sessions with abnormal metadata (extreme turn_count, high thinking_blocks, zero turn_duration).
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Produce a detailed report on APIError events from Agent Monitor data — counts over time, which sessions and models are affected, and the likely root cause (rate limits, overload/529, or context-window pressure) inferred from each event's summary and data payload. Use when API errors spike or when you need to explain why requests are failing.
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Benchmark one session (or a small recent set) against the rolling average using Agent Monitor data — cost, total tokens, tool count, and workflow complexity score — and report where each metric lands as a percentile of the population. Tells you whether a session was normal, cheap, or an outlier. Use when judging whether a session was typical or out of band.
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Define a spend budget for Claude Code and, optionally, create a cost alert rule that fires when usage crosses the limit, via POST /api/alerts/rules on the Agent Monitor dashboard. Reads current spend from /api/pricing/cost to size the budget sensibly and explains every rule field before writing. Use when setting a spend limit or wiring up a budget guardrail.
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Analyze prompt-cache effectiveness for Claude Code usage from the Agent Monitor dashboard — cache hit rate (total_cache_read / (total_cache_read + total_input)), cache_write vs cache_read reuse, cache-read vs cache-write spend, and the sessions with the poorest reuse. Pulls token totals from /api/analytics, per-session detail from /api/sessions, and dollar splits from /api/pricing/cost. Use when diagnosing cache spend or deciding whether prompt caching is paying off.
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