Analyze workflow patterns using the Agent Monitor's workflow intelligence API — orchestration DAGs, tool flow transitions, subagent effectiveness, model delegation patterns, error propagation by depth, concurrency lanes, compaction impact, and agent co-occurrence. Produces prioritized optimization recommendations with quantified impact.
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Analyze Claude Code workflows using the Agent Monitor's workflow intelligence engine.
The user provides: $ARGUMENTS
Options: "analyze", a session ID for single-session analysis, or a focus: "tools", "subagents", "cost", "errors".
| Endpoint | Returns |
|---|---|
GET /api/sessions?limit=100 | Session list with metadata |
GET /api/workflows/{sessionId} | 11 workflow datasets (see below) |
GET /api/analytics | Tool usage top 20, event types, agent types |
GET /api/pricing | Model pricing rules for cost comparison |
GET /api/workflows/{sessionId})Returns these 11 datasets per session:
| Dataset | Content |
|---|---|
stats | Aggregate session stats: tool count, agent depth, event count |
orchestration | DAG: agent nodes with parent/child edges, depths, types |
toolFlow | Transition matrix: tool A → tool B with counts (common sequences) |
effectiveness | Subagent success: per-type completion rates, avg duration, task success |
patterns | Recurring sequences: detected workflow patterns with frequency |
modelDelegation | Model choices: which models are delegated which tasks |
errorPropagation | Error flow by depth: where in the agent tree errors originate and propagate |
concurrency | Concurrency lanes: overlapping agent execution timelines |
complexity | Complexity score: numerical score based on depth, breadth, tool diversity |
compaction | Compaction impact: token savings, frequency, context health |
cooccurrence | Agent pairs: which agents frequently run together |
From toolFlow transition data:
From effectiveness + orchestration:
From modelDelegation:
From errorPropagation:
From concurrency:
From compaction:
Prioritized recommendations table:
| # | Recommendation | Source Data | Impact | Effort | Est. Savings |
|---|
Top 5 recommendations with detailed explanation, supporting data from the workflow API, and implementation steps.
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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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