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Add adaptive cost-quality routing primitives
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Bernd Worsch
2026-05-17 21:32:27 +02:00
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workplans/llm-connect-WP-0004-adaptive-cost-quality-routing.md
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@@ -1,6 +1,20 @@
---
id: LLM-WP-0004
type: workplan
title: Adaptive Cost-Quality Routing
domain: custodian
status: completed
owner: llm-connect
created: 2026-05-17
repo: llm-connect
planning_priority: high
planning_order: 4
state_hub_workstream_id: e1807fab-e29e-4517-b362-95737a96582d
---
# LLM-WP-0004 — Adaptive Cost-Quality Routing
**status:** todo
**status:** completed
**owner:** llm-connect
**repo:** llm-connect
**created:** 2026-05-17
@@ -60,53 +74,240 @@ consumer (`inter-hub`, `markitect`) adopts it.
## Tasks
The fenced `task` blocks below are the State Hub registration index. Keep them
in sync with the detailed task tables that follow.
```task
id: T01
title: 'QualityObservation dataclass: task_type, adapter_id, model_id, cost_usd, quality_score (0..1), latency_ms, tokens_in, tokens_out, baseline_adapter_id, recorded_at, tags'
priority: high
status: done
state_hub_task_id: "1c285bec-c30b-45a8-a408-3f91d810a078"
```
```task
id: T02
title: 'QualityLedger append-only JSONL store with file-locked writes, configurable path, simple query helpers (by_task_type, recent, mean_quality)'
priority: high
status: done
state_hub_task_id: "5249f171-a047-499f-9ec4-cb50e1477765"
```
```task
id: T03
title: 'TTL helpers: prune_before(timestamp) and is_stale(observation, max_age)'
priority: medium
status: done
state_hub_task_id: "adb255cf-7e89-4fea-b822-6be437d99789"
```
```task
id: T04
title: 'Functional contract doc for the ledger schema and quality_score semantics'
priority: medium
status: done
state_hub_task_id: "51a33180-a99d-4aa4-96be-2fcee15bfbc3"
```
```task
id: T05
title: 'Tests: ledger round-trip, concurrent writes, query helpers, TTL, malformed-line resilience'
priority: high
status: done
state_hub_task_id: "458610c5-c903-4b42-9602-cd511999c9ba"
```
```task
id: T06
title: 'GradingResult dataclass: quality_score, notes, grader_id, baseline_response, candidate_response'
priority: high
status: done
state_hub_task_id: "c12a595b-90fc-4a80-8394-549edbda2031"
```
```task
id: T07
title: 'BaselineGrader protocol plus PairedGrader that runs baseline and candidate calls and delegates to a Judge'
priority: high
status: done
state_hub_task_id: "80b98e31-06fc-4462-b030-a12881095f93"
```
```task
id: T08
title: 'Judge protocol and built-ins: ExactMatchJudge, EmbeddingSimilarityJudge, LLMJudge'
priority: high
status: done
state_hub_task_id: "c2887fe3-bae6-4298-8c26-f9a519264dcf"
```
```task
id: T09
title: 'Functional contract doc covering judge bias caveats'
priority: medium
status: done
state_hub_task_id: "7a4fd87a-b0ba-41b0-8e1a-a60fdaded905"
```
```task
id: T10
title: 'Tests: judges with canned inputs, stable grader result, deterministic LLMJudge rubric seed'
priority: high
status: done
state_hub_task_id: "8415a11d-d508-4d17-8082-10f93e9d16c5"
```
```task
id: T11
title: 'AdaptiveRoutingPolicy extends RoutingPolicy and selects the cheapest adapter whose observed mean quality clears the floor'
priority: high
status: done
state_hub_task_id: "0e9f9f8e-5066-4257-913b-a19f5b3fc47d"
```
```task
id: T12
title: 'Tie-breaking: prefer lower observed cost, then explicit preferred adapter from static rules'
priority: medium
status: done
state_hub_task_id: "59d44712-1088-41ac-bad8-5d95db6f3a4f"
```
```task
id: T13
title: 'Cold-start behaviour falls through to static RoutingPolicy.resolve when observations are missing'
priority: high
status: done
state_hub_task_id: "1927d369-f5f6-48d3-8f53-7e4f1cae370e"
```
```task
id: T14
title: 'Functional contract doc for adaptive policy and sample-size/freshness trade-off'
priority: medium
status: done
state_hub_task_id: "4d4717c1-8849-4fed-8f8d-515901ecafe0"
```
```task
id: T15
title: 'Tests: floor enforcement, tie-break, cold-start, window-size effect, fallback chain'
priority: high
status: done
state_hub_task_id: "304bd782-db15-4b7a-8d05-49e064a926c3"
```
```task
id: T16
title: 'ShadowingAdapter wraps a candidate adapter, also invokes the baseline adapter, grades, and appends to QualityLedger'
priority: medium
status: done
state_hub_task_id: "62dd507f-536a-4623-8cbd-fa9f78e85ca6"
```
```task
id: T17
title: 'Sampling: caller-configurable shadow_rate so production load is not doubled'
priority: medium
status: done
state_hub_task_id: "ccb73e92-1fca-42f9-8437-9b2b50e6424c"
```
```task
id: T18
title: 'Failure isolation: shadow errors never affect the candidate response returned to the caller'
priority: high
status: done
state_hub_task_id: "b879d232-d6ce-4ff6-b534-616729ea5ad7"
```
```task
id: T19
title: 'Functional contract doc for ShadowingAdapter'
priority: low
status: done
state_hub_task_id: "99d2c1bc-f1d8-42b3-9e04-6eea49460943"
```
```task
id: T20
title: 'Tests: candidate response survives baseline failure, ledger sampling rate, sync vs async modes'
priority: high
status: done
state_hub_task_id: "f533fbf4-484f-4408-8260-7e84e23bdc46"
```
```task
id: T21
title: 'Example script: route fixture batch through three candidate adapters and populate the ledger'
priority: medium
status: done
state_hub_task_id: "7ef0c143-74b0-4740-81fa-819a826cf8f3"
```
```task
id: T22
title: 'Integration test: cold-start, static fallback, first observations, convergence to cheapest qualifying adapter'
priority: high
status: done
state_hub_task_id: "c4c6743f-157b-4445-8576-9caa6421d463"
```
```task
id: T23
title: 'Consumer integration guide showing how infospace-bench wires task types into adaptive policy'
priority: medium
status: done
state_hub_task_id: "3a073ff7-0170-4a95-9c2a-a5daa84964e6"
```
### T01 — Quality observation data model + ledger
| ID | Title | Priority | Status |
|-----|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------|
| T01 | `QualityObservation` dataclass: `task_type`, `adapter_id`, `model_id`, `cost_usd`, `quality_score` (0..1), `latency_ms`, `tokens_in`, `tokens_out`, `baseline_adapter_id`, `recorded_at`, `tags` | high | todo |
| T02 | `QualityLedger` append-only JSONL store with file-locked writes, configurable path, simple query helpers (`by_task_type`, `recent`, `mean_quality`) | high | todo |
| T03 | TTL helpers: `prune_before(timestamp)` and `is_stale(observation, max_age)` so callers can refresh observations without re-reading the whole ledger | medium | todo |
| T04 | Functional contract doc for the ledger schema and the field semantics of `quality_score` | medium | todo |
| T05 | Tests: round-trip, concurrent writes, query helpers, TTL, malformed-line resilience | high | todo |
| T01 | `QualityObservation` dataclass: `task_type`, `adapter_id`, `model_id`, `cost_usd`, `quality_score` (0..1), `latency_ms`, `tokens_in`, `tokens_out`, `baseline_adapter_id`, `recorded_at`, `tags` | high | done |
| T02 | `QualityLedger` append-only JSONL store with file-locked writes, configurable path, simple query helpers (`by_task_type`, `recent`, `mean_quality`) | high | done |
| T03 | TTL helpers: `prune_before(timestamp)` and `is_stale(observation, max_age)` so callers can refresh observations without re-reading the whole ledger | medium | done |
| T04 | Functional contract doc for the ledger schema and the field semantics of `quality_score` | medium | done |
| T05 | Tests: round-trip, concurrent writes, query helpers, TTL, malformed-line resilience | high | done |
### T02 — Baseline grader
| ID | Title | Priority | Status |
|-----|----------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------|
| T06 | `GradingResult` dataclass: `quality_score`, `notes`, `grader_id`, `baseline_response`, `candidate_response` | high | todo |
| T07 | `BaselineGrader` protocol: `.grade(baseline_adapter, candidate_adapter, prompt, run_config)` → `GradingResult`; built-in concrete `PairedGrader` runs both calls and delegates to a `Judge` | high | todo |
| T08 | `Judge` protocol + three built-ins: `ExactMatchJudge`, `EmbeddingSimilarityJudge` (uses an embedding adapter), `LLMJudge` (uses a third adapter with a fixed rubric prompt) | high | todo |
| T09 | Functional contract doc covering judge bias caveats (length bias, format bias, position bias for `LLMJudge`) | medium | todo |
| T10 | Tests: each judge against canned inputs, grader emits stable result with both responses preserved, deterministic seed for `LLMJudge` rubric | high | todo |
| T06 | `GradingResult` dataclass: `quality_score`, `notes`, `grader_id`, `baseline_response`, `candidate_response` | high | done |
| T07 | `BaselineGrader` protocol: `.grade(baseline_adapter, candidate_adapter, prompt, run_config)` → `GradingResult`; built-in concrete `PairedGrader` runs both calls and delegates to a `Judge` | high | done |
| T08 | `Judge` protocol + three built-ins: `ExactMatchJudge`, `EmbeddingSimilarityJudge` (uses an embedding adapter), `LLMJudge` (uses a third adapter with a fixed rubric prompt) | high | done |
| T09 | Functional contract doc covering judge bias caveats (length bias, format bias, position bias for `LLMJudge`) | medium | done |
| T10 | Tests: each judge against canned inputs, grader emits stable result with both responses preserved, deterministic seed for `LLMJudge` rubric | high | done |
### T03 — Adaptive routing policy
| ID | Title | Priority | Status |
|-----|--------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------|
| T11 | `AdaptiveRoutingPolicy` extends `RoutingPolicy`: given `task_type` + `quality_floor` + `ledger`, returns the cheapest adapter whose observed mean quality clears the floor over a configurable window | high | todo |
| T12 | Tie-breaking: when two adapters meet the floor, prefer lower observed cost; if still tied, prefer the explicitly-preferred adapter from the underlying static rules | medium | todo |
| T13 | Cold-start behaviour: when no observations exist for a `(task_type, adapter)` pair, fall through to the static `RoutingPolicy.resolve` result so the system stays usable on day zero | high | todo |
| T14 | Functional contract doc; document the trade-off between sample size and freshness | medium | todo |
| T15 | Tests: floor enforcement, tie-break, cold-start, window-size effect, fallback chain | high | todo |
| T11 | `AdaptiveRoutingPolicy` extends `RoutingPolicy`: given `task_type` + `quality_floor` + `ledger`, returns the cheapest adapter whose observed mean quality clears the floor over a configurable window | high | done |
| T12 | Tie-breaking: when two adapters meet the floor, prefer lower observed cost; if still tied, prefer the explicitly-preferred adapter from the underlying static rules | medium | done |
| T13 | Cold-start behaviour: when no observations exist for a `(task_type, adapter)` pair, fall through to the static `RoutingPolicy.resolve` result so the system stays usable on day zero | high | done |
| T14 | Functional contract doc; document the trade-off between sample size and freshness | medium | done |
| T15 | Tests: floor enforcement, tie-break, cold-start, window-size effect, fallback chain | high | done |
### T04 — Shadow-mode observation wrapper
| ID | Title | Priority | Status |
|-----|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------|
| T16 | `ShadowingAdapter` wraps a candidate adapter; on each call, also invokes the baseline adapter (sync or via a thread pool), grades, and appends to a `QualityLedger` | medium | todo |
| T17 | Sampling: caller-configurable fraction (`shadow_rate=0.1` means grade one call in ten) so production load is not doubled | medium | todo |
| T18 | Failure isolation: shadow errors never affect the candidate response returned to the caller; failures are logged but not raised | high | todo |
| T19 | Functional contract doc | low | todo |
| T20 | Tests: candidate response always returned even when baseline raises, ledger gets exactly `shadow_rate × calls` entries (within tolerance), sync vs async modes | high | todo |
| T16 | `ShadowingAdapter` wraps a candidate adapter; on each call, also invokes the baseline adapter (sync or via a thread pool), grades, and appends to a `QualityLedger` | medium | done |
| T17 | Sampling: caller-configurable fraction (`shadow_rate=0.1` means grade one call in ten) so production load is not doubled | medium | done |
| T18 | Failure isolation: shadow errors never affect the candidate response returned to the caller; failures are logged but not raised | high | done |
| T19 | Functional contract doc | low | done |
| T20 | Tests: candidate response always returned even when baseline raises, ledger gets exactly `shadow_rate × calls` entries (within tolerance), sync vs async modes | high | done |
### T05 — End-to-end example + integration test
| ID | Title | Priority | Status |
|-----|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------|
| T21 | Example script: route a small fixture batch through three candidate adapters (one OpenRouter cheap, one OpenRouter mid, `ClaudeCodeAdapter` as baseline), grade each, populate ledger | medium | todo |
| T22 | Integration test with mocked adapters covering: cold-start → static fallback → first observations → adaptive selection converges to the cheapest qualifying adapter | high | todo |
| T23 | Brief consumer-integration guide in `docs/` showing how `infospace-bench` (or any caller) wires task-type-per-stage into the adaptive policy | medium | todo |
| T21 | Example script: route a small fixture batch through three candidate adapters (one OpenRouter cheap, one OpenRouter mid, `ClaudeCodeAdapter` as baseline), grade each, populate ledger | medium | done |
| T22 | Integration test with mocked adapters covering: cold-start → static fallback → first observations → adaptive selection converges to the cheapest qualifying adapter | high | done |
| T23 | Brief consumer-integration guide in `docs/` showing how `infospace-bench` (or any caller) wires task-type-per-stage into the adaptive policy | medium | done |
## Risks and open questions