[Feature]: Evaluator: pluggable model providers + drop hardcoded `temperature` support OpenAI GPT‑5.6 Sol and Anthropic Claude Opus 4.7 / 4.8

### Description

The evaluation framework (scripts/aidlc-evaluator) is hard‑wired to the Amazon Bedrock Converse API on the bedrock-runtime endpoint, and its scorer sends a hardcoded temperature. This makes it impossible to evaluate against several current Bedrock models by config alone. Please add a thin model‑provider abstraction and make temperature optional.

Current limitations

Execution agents are Converse‑only. packages/execution/src/aidlc_runner/agents/executor.py (~L220) and simulator.py (~L137) build strands.models.bedrock.BedrockModel(model_id=...), which calls Converse on bedrock-runtime. OpenAI GPT‑5.6 Sol (openai.gpt-5.6-sol) is served only on the bedrock-mantle endpoint via the Responses API (.../openai/v1/responses), so setting model_id: openai.gpt-5.6-sol fails at invoke time.
Scorer hardcodes temperature and is Converse‑only. packages/qualitative/src/qualitative/scorer.py (~L217) calls client.converse(..., inferenceConfig={"maxTokens": ..., "temperature": 0.0}). Anthropic Claude Opus 4.7 and 4.8 reject temperature (ValidationException: temperature is deprecated for this model), so the scorer cannot run on claude-opus-4-7 or claude-opus-4-8 — the evaluator is effectively stuck on opus-4-6. Opus 4.7+ replaced temperature with an effort control (high/medium/low, effort-2025-11-24 beta header).
Config assumes global/Converse conventions. config/default.yaml uses ids like global.anthropic.claude-opus-4-6-v1; GPT‑5.6 Sol is in‑region only (e.g. us‑east‑1), with no geo/global inference profile.

Proposed change

Honor the existing models..provider field:
provider: "bedrock" → current BedrockModel (Converse) path (unchanged default).
provider: "openai_responses" (or auto‑detect model_id starting with openai.) → build strands.models.openai_responses.OpenAIResponsesModel with bedrock_mantle_config={"region": , "boto_session": }. Strands (already a dependency, ≥1.44.0) mints Bedrock bearer tokens from the standard AWS credential chain, so no separate API key is required.
Scorer: route through the same abstraction (or the OpenAI Responses SDK against the Mantle base URL) instead of a raw client.converse call; make temperature optional/omittable (don't send it for Opus 4.7/4.8), and optionally support an effort setting for Opus 4.7+.

Implementation notes / gotchas

Mantle base‑URL path: GPT‑5.6 Sol uses https://bedrock-mantle.{region}.api.aws/openai/v1, which differs from the generic .../v1 template in Strands' _openai_bedrock.py; the provider must allow a base_url (path) override.
Extras: requires the strands-agents[openai] extra (aws_bedrock_token_generator); add to pyproject.toml.
Region: support in‑region‑only models (skip the global./geo prefix assumption).

Acceptance criteria

run.py full
/
batch
succeed with
model_id: openai.gpt-5.6-sol
,
provider: openai_responses
, region
us-east-1
, using the standard AWS credential chain.
Scorer (and executor/simulator) run on
claude-opus-4-7 and claude-opus-4-8
with
no temperature sent
, and on GPT‑5.6 Sol.
effort
(
high
/
medium
/
low
) is optionally configurable per role for Opus 4.7+ (ignored where unsupported).
Existing Bedrock/Converse runs (Anthropic ≤4.6) are unchanged by default.
Config presets added under
config/
(e.g.
config/gpt-5-6-sol.yaml
,
config/opus-4-8.yaml
) and documented in
README.md
.
Unit tests cover provider selection (bedrock vs openai_responses) and the scorer's optional‑temperature / effort behavior.

### Use Case

We want to benchmark the AI‑DLC workflow across the newest frontier models — including the latest Anthropic Claude (Opus 4.7 / 4.8) and non‑Anthropic models (OpenAI GPT‑5.6 Sol) — to compare quality, token cost, and cross‑model consistency, which is a core purpose of the evaluator (NFR + qualitative scoring across models).

Today "just change the model id" does not work for these models:

Opus 4.7 / 4.8 fail immediately in the scorer because of the hardcoded temperature=0.0, forcing the evaluator to stay on the older opus-4-6.
GPT‑5.6 Sol can't be reached at all because it's only on bedrock-mantle/Responses, not Converse/bedrock-runtime.

This blocks anyone trying to validate rule/prompt changes against current‑generation models or produce an up‑to‑date cross‑model comparison report. A small provider abstraction + optional temperature removes the blocker and future‑proofs the evaluator as more models ship on the Mantle/Responses path.

### Version

v1 (current)

### Area

Other

### Additional Context

GPT‑5.6 Sol model card (endpoints/APIs, in‑region only, Responses‑only): https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-56-sol.html

API compatibility matrix by model: https://docs.aws.amazon.com/bedrock/latest/userguide/models-api-compatibility.html

Bedrock OpenAI / Mantle inference docs: https://docs.aws.amazon.com/bedrock/latest/userguide/inference-openai.html

Why not just wait for Converse support on GPT‑5.6 Sol? The split is architectural, not a temporary gap: open‑weight OpenAI models (gpt-oss-*) are on bedrock-runtime and ARE Converse‑compatible, but the proprietary GPT‑5.x line (5.4 → 5.6 Sol/Terra/Luna) is served only on bedrock-mantle via the Responses API. AWS designed Mantle to expose these models through their native OpenAI surface rather than Converse, so a Converse path for Sol is not expected — supporting the Responses provider is required, not optional.

Strands already ships the needed provider (strands/models/openai_responses.py, strands/models/_openai_bedrock.py), so this is primarily wiring + config, not new transport code.

Start with packages/execution/src/aidlc_runner/agents/executor.py, simulator.py, and packages/qualitative/src/qualitative/scorer.py, then inspect config/default.yaml and the existing Strands provider modules. Run the evaluator's full and batch paths with the relevant model configurations. Done means both provider paths work, optional temperature and effort behavior is covered by unit tests, existing Converse runs remain unchanged, and the new presets and README documentation are present.

Written by the indexing model from the issue text.