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Ft/agentic bench scale - #75
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Navigate logical layers of code changes, visualize relationships, and explore their blast radius. 📝 WalkthroughWalkthroughThe pull request adds single-table and multi-table Berka synthesis and evaluation workflows, plus a mock SaaS billing benchmark. SynBench gains role-specific LLM configuration, task eligibility and sampling controls, dialogue-turn metrics, and benchmark display support. The changes also update retail evaluation notebooks and benchmark tasks, add dependencies and download helpers, and document the new workflows. Priority: ⬇️ Low Estimated code review effort: 5 (Critical) | ~120 minutes Merge Risk: 🟡 Moderate · up to This change adds a SaaS billing benchmark and role-specific model settings, but several scoring and reporting problems affect benchmark trustworthiness. The published gpt-4o results are actually the Gemini results. The communication check can accept wrong decimal amounts. The per-task summary table can undercount passes. The retail tasks now give full credit for incomplete order confirmations. Fix these before relying on the benchmark numbers. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 78.07% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 114 functions across 33 files. (58 skipped: 58 unsupported.)
✨ Finishing Touches 💡 2📝 Generate docstrings 💡
⚔️ Resolve merge conflicts 💡
🧪 Generate unit tests (beta)
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Actionable comments posted: 18
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Inline comments:
In `@aieng-synthetic-data/aieng/syn_data/synbench/agents/pipeline.py`:
- Around line 37-38: Update the hard-task notebook’s AgentPipeline construction
to pass the explicitly configured user_client as user_simulator_client, so
UserSimulator reuses that client instead of creating another one.
In `@aieng-synthetic-data/aieng/syn_data/synbench/display.py`:
- Around line 340-341: Update the reward aggregation around rewards,
type_rewards, n_pass, and cells so repeated runs for the same task type and
personality are retained rather than overwritten. Count every run’s pass in
n_pass, and display each personality cell as its single result or an aggregate
of its repeated results.
In `@aieng-synthetic-data/aieng/syn_data/synbench/environment/communicate.py`:
- Around line 14-17: Update the combined-message and required-item normalization
in the communicate reward matching logic to remove periods only when they are
not followed by a digit. Apply the same normalization to both sides so sentence
and abbreviation periods are ignored while decimal points in amounts and values
remain significant.
In `@aieng-synthetic-data/aieng/syn_data/synbench/generation/llm.py`:
- Around line 16-17: Update call_llm_json to pass its explicit model argument to
get_client when provided, falling back to get_generator_model() only when model
is absent.
In `@implementations/agent_benchmark_generation/1-check_access_to_model.ipynb`:
- Around line 66-69: Update the model access-check flow in the notebook to
resolve the generator, agent, and user-simulator models through their
role-specific accessors, then send a test request to each resolved model; ensure
no request uses the unrelated hard-coded `gemini-3.1-flash-lite-preview` model.
In `@implementations/agent_benchmark_generation/5-saas-billing-scale.ipynb`:
- Around line 1752-1754: The gpt-4o evaluation cell writes the Gemini summary
instead of its own results. In `gpt_metrics.summary()` at
`implementations/agent_benchmark_generation/5-saas-billing-scale.ipynb` lines
1752-1754, write `gpt_summary` to the summary file; at
`implementations/agent_benchmark_generation/data/benchmarks/mock_saas_billing/results/gpt-4o/benchmark_summary.json`
lines 2-183, regenerate the file from the gpt-4o run or rebuild it from the
notebook’s per-task gpt-4o output.
- Around line 427-429: Update the tutorial labels so they match the models:
change Agent 1’s displayed model name from `gpt-40` to `gpt-4o`, and update the
`show_pipeline_messages` title for the `gemini-3-flash-preview` session from
“Weaker” to “Stronger.”
In
`@implementations/agent_benchmark_generation/data/benchmarks/mock_retail/tasks.json`:
- Line 30: Update the `communicate_info` criteria for both the pending inquiry
and cancellation tasks in `tasks.json` to require `widget-a` and the full
shipping address, `123 main st, boston, ma`, alongside their existing status
values. Do not add a trace-level ordering check.
In
`@implementations/agent_benchmark_generation/domains/mock_saas_billing/verify.py`:
- Around line 30-49: Update the billing verification flow that uses
_check_billing_email and _check_update_seats to resolve the scenario user’s
account, then reject write actions targeting a different account. Apply the
ownership check to invoice, billing-email, and subscription actions, including
_check_void_invoice, and include its violations in the errors returned by draft
verification.
In
`@implementations/tabular_data/evaluation/privacy/privacy_evaluation_pipeline.ipynb`:
- Around line 81-82: Correct the opening brace in the copied meta_info JSON
example so it is valid JSON. In
implementations/tabular_data/evaluation/privacy/privacy_evaluation_pipeline.ipynb,
lines 81–82, and
implementations/tabular_data/evaluation/quality/quality_evaluation_pipeline.ipynb,
lines 68–69, replace the closing-brace line immediately after the bash code
fence with an opening-brace line.
In
`@implementations/tabular_data/evaluation/quality/quality_evaluation_pipeline.ipynb`:
- Line 402: Update the permutations argument in the
KolmogorovSmirnovAndTotalVariation setup from 10 to 1000, matching the
configured value in config_berka.yaml.
In
`@implementations/tabular_data/multi_table/data_preprocessing/data_preprocessing.ipynb`:
- Around line 233-235: Update the `pd.read_csv` call that loads `account.csv` in
the sample-table inspection cell to use the semicolon delimiter, so `accounts`
is parsed into separate columns.
In `@implementations/tabular_data/multi_table/README.md`:
- Line 22: Update the “Multi table” workflow line in the README to use paths
relative to its current directory: remove the redundant multi_table/ prefix from
the data_preprocessing, training, and synthesizing links, link the relational
metrics notebook to evaluation/multi_table_metrics.ipynb, and point shared
per-table metrics to ../evaluation/.
In `@implementations/tabular_data/multi_table/training/ClavaDDPM_training.ipynb`:
- Line 250: Update the checkpoint references in the notebook markdown and log
message associated with clava_clustering to use the actual filename
cluster_ckpt.pkl instead of clustering_ckpt.pkl. Make the markdown’s cleanup
guidance identify the file users must delete to recompute clusters, and keep the
log path consistent with the checkpoint name used by the load operation.
In `@implementations/tabular_data/multi_table/training/README.md`:
- Line 330: Align the example hyperparameter values in the README with the
shipped config.yaml for diffusion_config and classifier_config, including layer
widths, timesteps, iterations, and batch sizes. Update the accompanying text
that describes the example and expected training duration so it accurately
reflects the shipped full-run configuration; alternatively, explicitly label the
differing values as a separate smoke-test configuration.
In
`@implementations/tabular_data/single_table/data_processing/data_processing.ipynb`:
- Around line 153-156: Update the raw-data preview’s pd.read_csv call to use the
semicolon separator so raw_data loads trans.csv into its expected columns.
In
`@implementations/tabular_data/single_table/synthesizing/tabddpm_synthesizing.ipynb`:
- Around line 219-222: Update the checkpoint-loading else branch so missing
model files stop execution with a FileNotFoundError instead of logging that
training will occur and falling through without defining models. Include the
missing paths from model_file_paths in the error message.
In `@implementations/tabular_data/utils.py`:
- Around line 18-23: Update the dependency constraint for gdown to require
version 6.4.0 or later, and pass a finite timeout of 30 seconds to the
gdown.download_folder call so folder discovery and file transfers are bounded.
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📒 Files selected for processing (98)
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rjavadi
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Sep 29, 2026
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Summary
Added a new domain with a larger scale (Saas billing) to the agentic benchmark RI.
Now we can have different LLM backends for different parts of the pipeline (generator LLM, vs agent LLM, vs user sim LLM)
Documentation across the whole RI is improved and updated.
Type of Change
Changes Made
Testing
uv run pytest tests/)uv run mypy <src_dir>)uv run ruff check src_dir/)Manual testing details:
Unit tests are added.
All the notebooks run with no issues.
Screenshots/Recordings
Related Issues
Deployment Notes
Checklist