Render the multi-agent orchestration DAG for a session — parent→child subagent edges, tree depth, and fan-out — from the Agent Monitor workflow intelligence API. Cross-checks the orchestration dataset against the raw agent records and session detail. Use when visualizing how a session's agent structure was organized.
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Render the subagent orchestration graph for one Claude Code session as a depth-ordered DAG.
The user provides: $ARGUMENTS
A session ID. If empty, fetch GET /api/sessions?limit=1 and use the most recent session, stating which one you picked.
| Endpoint | Returns |
|---|---|
GET /api/workflows/{sessionId} | The orchestration dataset: DAG nodes (agent id, type, model, status, depth) and parent→child edges |
GET /api/agents | Raw subagent records (status, type, depth, parent) to cross-check node/edge counts |
GET /api/sessions/{sessionId} | Full session detail with nested agents[] to confirm the root and total agent count |
From orchestration: the root agent, total agent count, max depth, and max fan-out (most children under any one parent). Confirm the agent count against /api/agents filtered to this session.
Every parent→child edge, grouped by depth, formatted as:
depth d: parent[model] → child[type, status]
Mark leaf agents (no children) and any orphan nodes (a parent that is not present in the node set).
| Depth | Agents at depth | Children spawned | Avg fan-out |
|---|
A simple indented tree rendering of the DAG, e.g.:
root [opus, completed]
├─ explore [sonnet, completed]
└─ code-review [sonnet, error]
└─ debugger [sonnet, completed]
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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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