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Measure tokens, tool calls, and latency during agent evaluation

Record agent resource use alongside quality evaluation to detect excessive cost for marginal quality gains.

Goal

Create comparable baselines for tool calls, tokens, and runtime across agent iterations.

Steps

  1. Choose a fixed evaluation set and run configuration; record model and SDK versions. [efficiency]
  2. Read tool_call_count_v1, inference_call_count_v1, token_usage_v1, and invocation_duration_v1. [efficiency]
  3. Compare quality and resource metrics before and after changes, noting sample size and measurement limits. [efficiency]
  4. For clear regressions, optimize tool planning or context and rerun the same benchmark. [efficiency]

How to verify

The report shows quality, model calls, tool calls, tokens, and latency under matching settings.

Troubleshooting

If usage is not comparable, align cases, concurrency, and model version; efficiency metrics alone do not establish quality.

Permissions

Prerequisites

Permissions

Limitations

This procedure follows Google ADK at the pinned revision; other versions, models, or remote services may differ in APIs and event semantics. Recheck permissions, retention, and recovery before production.

Pinned upstream source

https://github.com/google/adk-python/blob/e9b74d36140f2594b24c481fb7a3825b2cb01e96/docs/guides/evaluation/efficiency_evaluators/index.md

Repository
google/adk-python
File path
docs/guides/evaluation/efficiency_evaluators/index.md
Pinned commit
e9b74d36140f2594b24c481fb7a3825b2cb01e96
Source checked
2026-10-14 · Reviewed against the pinned upstream source

Repository metrics

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License: Apache-2.0

Main topic

Take a coding Agent change from context to verification

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