Benchmark

Benchmark

Score a session against the rolling population average and report its percentile on

cost, tokens, tool count, and complexity using Agent Monitor data.

Input

The user provides: $ARGUMENTS

This may be:

• A single session ID — benchmark that session
• "latest" — benchmark the most recent session
• "latest N" — benchmark the N most recent sessions, each vs the average
• empty — benchmark the most recent session (default)

Data Sources

| Endpoint | Returns |

|----------|---------|

| GET /api/sessions?limit=N | Population of sessions with cost, model, started_at, metadata (turn_count, total_turn_duration_ms) — builds the rolling baseline |

| GET /api/pricing/cost/{sessionId} | { total_cost, breakdown:[{ input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost }] } — the target session's cost and tokens |

| GET /api/workflows/{sessionId} | complexity (score), stats (tool/event counts), toolFlow (distinct tools used) — the target session's tool count and complexity |

| GET /api/analytics | avg_events_per_session, tool_usage, daily_sessions — corroborates population-level averages |

Report Sections

1. Build the Baseline

Fetch the population with GET /api/sessions?limit=200 (the rolling set). For each

session gather cost (GET /api/pricing/cost/{id} or the list cost field), total

tokens (sum of the 4 token types from the pricing breakdown), tool count and

complexity (GET /api/workflows/{id}). Compute mean, median, and standard

deviation for each metric across the population.

2. Measure the Target

For the requested session, pull the same four metrics:

• Cost — total_cost from GET /api/pricing/cost/{id}.
• Total tokens — input + output + cache_read + cache_write summed from the breakdown.
• Tool count — distinct/total tools from GET /api/workflows/{id} stats/toolFlow.
• Complexity score — complexity.score from GET /api/workflows/{id}.

3. Percentile and Deviation

For each metric report the target's percentile within the population (share of

sessions at or below it) and its z-score (value − mean) / stddev. Label each:

below average / typical / above average / outlier (|z| > 2).

4. Verdict

State whether the session was normal overall. If it is an outlier, name which

metric drove it (e.g., complexity p96, cost p91 → an unusually heavy session).

Output

• A Markdown table: metric | session value | population mean | percentile | z-score | label.
• Currency in USD to 4 decimals; tokens and tool counts as integers; complexity to 2 decimals.
• Use ▲ for above-average and ▼ for below-average vs the mean.
• One-line verdict: "Normal session" or "Outlier — driven by <metric> (pNN)".
• When benchmarking multiple sessions, one row block per session plus a summary line.
• Read-only: percentiles come only from the fetched population; never fabricate the baseline.