ci: add deterministic AI regression gate

[KostasCherv]

What

• add a versioned 20-case AI contract regression dataset
• exercise production router parsing, citation provenance/selection, and finance tool selection/planning
• enforce 100% overall and per-category scores on every PR without credentials or network calls
• upload a commit-keyed JSON score report as a CI artifact
• document local execution and extension policy

Why

Cortex already has DSPy and DeepEval for model-backed semantic evaluation, but those checks require providers and are not reliable or inexpensive enough for every pull request. This gate reuses the production boundaries and existing evaluation area to catch deterministic AI behavior regressions immediately, without introducing another framework.

Impact

Every backend CI run now produces an auditable score keyed by commit SHA and fails if any case or category falls below 100%. Model-judge evaluation remains manual and complementary.

Checks

• uv run python -m src.evals.regression_gate --output reports/ai-regression-local.json — 20/20
• uv run pytest tests/test_ai_regression_gate.py tests/test_citations.py tests/test_output_parsers.py -q — 24 passed
• full CI-equivalent backend suite with dummy Supabase configuration — 556 passed
• uv run ruff check src/evals/regression_gate.py tests/test_ai_regression_gate.py
• graphify update .

Limitations

This PR intentionally protects deterministic AI contracts rather than judging free-form model quality. The existing DeepEval/DSPy workflow remains the model-backed semantic evaluation path. Future production failures at these boundaries should be promoted into this dataset.

[KostasCherv]

@codex

[chatgpt-codex-connector]

Codex Review: Didn't find any major issues. More of your lovely PRs please.

Reviewed commit: 777e52f150

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