O2C Deployment Workbench

A complete, clearly synthetic customer implementation for ERP receivables: source onboarding, canonical data, cash application, deductions, collections, controls, KPI benchmarking, MCP access, multi-agent exception review, observability, UAT, cutover, and hypercare.

All SAP S/4HANA-style, Oracle Fusion-style, and NetSuite-style files in this repository are generated simulations. They are not genuine exports or live integrations. KPI movement is a controlled implementation benchmark on synthetic data, not customer impact.

Why this is an implementation project

The workbench starts with customer configuration and three incompatible source layouts, not a clean analytics table. It applies validation gates, preserves payment residuals, routes exceptions, prioritizes collections, shows unmet targets, and produces the artifacts a deployment team would use from discovery through go-live.

The architecture deliberately keeps financial matching deterministic and auditable. Agents handle judgment-heavy work: exception investigation, data-quality diagnosis, collection planning, and go-live monitoring. They cannot post to an ERP, issue a refund, write off debt, or alter master data.

flowchart LR
    A["Three simulated ERP export sets"] --> B["Configuration-driven adapters"]
    B --> C["Seven canonical receivables tables"]
    C --> D["Validation control gate"]
    D --> E["Deterministic cash matching"]
    E --> F["Exception and collections queues"]
    F --> G["MCP evidence tools"]
    G --> H["Supervisor agent"]
    H --> I["Cash Application Agent"]
    H --> J["Data Quality Agent"]
    H --> K["Collections Agent"]
    I --> L["Human approval gate"]
    J --> L
    K --> L
    E --> M["Controlled KPI benchmark"]
    M --> N["Go Live Monitor Agent"]
    O["OpenTelemetry traces"] -.-> B
    O -.-> E
    O -.-> H
    O -.-> N

Run it

python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
o2c-workbench --project-root .
streamlit run app.py

The reproducible seed-42 run creates 1,908 canonical records, detects 18 intentional duplicate bank transactions as warnings, and leaves zero validation errors. Open the static dashboard or use the Streamlit app.

MCP server

The server uses the current MCP Python SDK and exposes structured tools, resources, and prompts. Local clients should use stdio; deployed clients can use Streamable HTTP.

o2c-mcp --transport stdio
o2c-mcp --transport streamable-http --port 8000

Tools: get_implementation_summary, investigate_payment, get_collection_priority, and run_controlled_benchmark. Resources: o2c://configuration, o2c://benchmark, and o2c://observability.

Multi-agent paths

The default path is offline and deterministic so a reviewer can reproduce handoffs and controls with no account or API key. It produces output/agent_decisions.csv, output/agent_control_summary.json, and OpenTelemetry spans.

An optional model-backed review uses the OpenAI Agents SDK with specialist handoffs and structured output:

pip install -e ".[ai]"
export OPENAI_API_KEY="..."
o2c-agent-review SAP-P001-09 --project-root .

The model path is intentionally not required for benchmark KPIs. It can recommend but cannot execute controlled financial actions.

Repository map

• config/ — implementation configuration, mappings, tolerances, weights, routes, controls, KPI targets, agent limits.

• data/raw/ — generated, visibly labeled simulated ERP exports.

• data/canonical/ — normalized customers, invoices, payments, remittances, deductions, promises, and activities.

• src/o2c_workbench/ — adapters, matching, collections, controls, agents, MCP, observability, KPI logic.

• output/ — benchmark evidence, queues, traces, and dashboards.

• docs/ — process designs, mapping, controls, RTM, 28 UAT cases, cutover, monitoring, training, and research basis.

• deliverables/ — implementation workbook.

• demo/ — five-minute narrated implementation walkthrough and script.

Seed-42 benchmark

Four stretch targets remain unmet, so the monitor recommends a conditional go, not a victory lap. That is intentional implementation realism: proceed only with human approval controls and focused hypercare for unapplied cash and residual overpayments.

Verification

pytest -q

See research basis for the primary sources that informed the payment cases, MCP transport choice, agent handoffs, and telemetry conventions.