feat(batch-agent): add E2E evaluation harness with Langfuse integration

- eval/mock_executor.py: intercepts execute_on_client, serves fixture
  files from disk, records all mutations (insert/update/delete)
- eval/config.py: YAML fixture loader with prompt variants, expected
  results, seed records, model overrides
- eval/scorer.py: FieldMatchScorer (fuzzy title match, per-field
  accuracy, precision/recall/F1) + LLMJudgeScorer (semantic eval)
- eval/langfuse_eval.py: sync fixtures to Langfuse datasets, create
  dataset runs, post scores, link traces to runs
- eval/runner.py: orchestrates fixture → mock → agent pipeline →
  scoring → Langfuse reporting
- eval/cli.py: CLI (python -m eval run/list/sync) with --models,
  --variants, --fixture, --no-judge flags
- eval/fixtures/: example Italian freelance scenario with 3 prompt
  variants (baseline, detailed_italian, minimal)

services/batch-agent/eval/requirements.txt

@@ -0,0 +1,2 @@
+# Extra dependencies for the eval harness (on top of the service requirements.txt)
+pyyaml>=6.0.0