Summary
MAP records tokens by subtask/agent/phase, including research-agent tokens, but it does not surface whether delegated research reduced downstream Actor/Monitor exploration or paid for itself. Add run-level metrics that make research ROI visible.
Paper basis
FastContext reports both end-to-end score and main-agent token savings. Their key efficiency claim is that exploration tokens move out of the expensive main trajectory and return compact evidence. For MAP, the practical equivalent is tracking research-agent cost versus downstream Actor/Monitor token/turn cost and artifact quality.
Current evidence
- src/mapify_cli/templates_src/hooks/map-token-meter.py.jinja:13-31 meters SubagentStop and Stop events.
- src/mapify_cli/templates_src/map/scripts/map_step_runner.py.jinja:839-923 records token events attributed to subtask/phase/agent.
- src/mapify_cli/templates_src/map/scripts/map_step_runner.py.jinja:929-1028 rolls up token_accounting.json by subtask, agent, and phase.
- tests/test_map_token_meter.py:86-206 validates subagent and main-session token attribution.
- README.md:166-172 advertises token budget and run-health diagnostics, but not research ROI.
Proposal
Extend token_accounting.json, token_report, or run_health_report with research-specific metrics:
- research token share by subtask
- Actor/Monitor tokens after research
- count of research artifacts and locations returned
- malformed/low-confidence research counts once a research validator exists
- optional before/after proxy: Actor broad-search commands after research, when detectable from transcripts
Acceptance criteria
- Token report shows research-agent/researcher cost separately from Actor/Monitor/orchestrator.
- Run health includes a concise research section: artifacts present, confidence/status if parseable, low-confidence warnings, and token share.
- Tests cover aggregation when research-agent has tokens, when only direct research is saved, and when no research tokens are available.
- The metric is advisory and never blocks workflow completion.
Summary
MAP records tokens by subtask/agent/phase, including research-agent tokens, but it does not surface whether delegated research reduced downstream Actor/Monitor exploration or paid for itself. Add run-level metrics that make research ROI visible.
Paper basis
FastContext reports both end-to-end score and main-agent token savings. Their key efficiency claim is that exploration tokens move out of the expensive main trajectory and return compact evidence. For MAP, the practical equivalent is tracking research-agent cost versus downstream Actor/Monitor token/turn cost and artifact quality.
Current evidence
Proposal
Extend token_accounting.json, token_report, or run_health_report with research-specific metrics:
Acceptance criteria