feat(agent): adaptive / complexity-aware model router

**Context:** ROADMAP.md → Adaptive model router + Complexity-aware model router (Cost governance)

---

## Component

Agent (Python runtime)

## Describe the feature

**Per-turn model selection** by task complexity: cheaper models for reads/simple edits, stronger models for complex reasoning—while honoring `max_budget_usd` and Cedar policy. Target ~30–40% cost reduction.

## Use case

Using Opus-class models for every turn is expensive. Many turns are file reads or trivial edits that a smaller model handles well.

## Proposed solution

1. Router heuristic or lightweight classifier (turn type, diff size, tool invoked).
2. Blueprint: allowed model set + routing policy.
3. Fallback to strong model on failure or low confidence.
4. Emit per-turn model choice in telemetry for cost attribution (#215).
5. A/B measure cost vs merge rate in evaluation pipeline.

## Other information

- Single issue covers both roadmap entries (compute + cost governance).
- Design context: `docs/design/COST_MODEL.md`, `agent/src/runner.py`.

- [ ] This might be a breaking change

Beginne mit docs/design/COST_MODEL.md und untersuche anschließend agent/src/runner.py, um den aktuellen Modellfluss pro Turn zu verstehen. Definiere, wie Komplexität, zulässige Modelle, max_budget_usd, Cedar policy, Fallback-Verhalten, Telemetrie und Evaluierung zusammenspielen. Als abgeschlossen gilt die Aufgabe, wenn der Router hinsichtlich seiner Auswirkungen auf Kosten und Merge-Rate gemessen wird, ohne Policy oder Budget zu verletzen.

Vom Indexierungsmodell aus dem Issue-Text verfasst.