Audit model delegation and subagent effectiveness for a session — which models handled which subagent types, per-type success rates and average durations, and wasted delegations (heavy models on trivial work or types that consistently fail) — using the Agent Monitor workflow intelligence API. Use when reviewing how a session delegated work across models and subagents.
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Audit how a Claude Code session delegated work: model-to-subagent mapping and whether each delegation paid off.
The user provides: $ARGUMENTS
A session ID. If empty, fetch GET /api/sessions?limit=1 and audit the most recent session, stating which one.
| Endpoint | Returns |
|---|---|
GET /api/workflows/{sessionId} | The modelDelegation dataset (which models are delegated which subagent types) and the effectiveness dataset (per-type completion/success rate, avg duration, task success) |
GET /api/agents | Raw subagent records (type, model, status, depth, parent) to corroborate counts and statuses |
From modelDelegation: a model × subagent-type table of how many agents of each type each model ran.
| Model | explore | code-review | debugger | ... | Total |
|---|
From effectiveness: per type, the success rate and average duration.
| Subagent type | Count | Success rate | Avg duration | Verdict |
|---|---|---|---|---|
| Mark types below ~70% success as low-yield. |
Flag, with evidence:
Concrete model reassignments grounded in the matrix and effectiveness data. State the type, the model used, the success rate, and the suggested model — only where the data supports it.
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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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