Enterprise AI Workflow Pattern

A Knowledge-to-Asset Workflow Pattern for Governed Enterprise AI.

Use AI to understand and compile enterprise knowledge, materialize the output into governed assets, and execute approved logic deterministically with traceability.

This repository documents a reusable architecture pattern for enterprise AI workflows where business inputs are document-heavy, policy-driven, and review-sensitive.

The core idea is simple:

unstructured inputs -> AI-assisted understanding and compilation -> governed assets -> deterministic execution -> review and audit

Why This Pattern Exists

Enterprise AI workflows do not fail only because models make mistakes.

They fail when model outputs become hidden runtime dependencies that cannot be versioned, reviewed, replayed, or audited.

This pattern separates the system into explicit responsibilities:

Responsibility	Role
Understanding	Use AI to classify, extract, validate, and structure enterprise inputs.
Compilation	Use AI to transform policy knowledge into structured rule and flow assets.
Asset Materialization	Store facts, rules, flows, and traces as durable governed artifacts.
Deterministic Execution	Execute approved assets with repeatable rule evaluation and trace output.
Human Review	Present evidence, results, and execution path for accountable business action.

The target output is not a one-shot answer.

The target output is a governed workflow capability.

Reference Architecture

Design Commitments

• AI is used for semantic understanding and knowledge compilation, not final runtime authority.
• Model output must be converted into durable assets before downstream execution.
• Runtime execution should be deterministic, testable, replayable, and traceable.
• Human approval, rejection, override, and escalation remain explicit business actions.
• Cross-module exchange should happen through stable contracts, not prompts or hidden implementation state.

Canonical Contract Surface

The pattern defines a minimal contract set:

Contract	Purpose
WorkflowRequest	Entry contract for submitting a workflow request.
FactAsset	Structured facts extracted from documents or other enterprise inputs.
RuleAsset	Approved executable rule or flow package.
ExecutionContext	Runtime input context for executing a rule asset.
ExecutionResult	Business-facing result from deterministic execution.
ExecutionTrace	Step-by-step path and decision record for audit and explanation.
ErrorEnvelope	Stable failure payload across modules.
Meta	Shared request, correlation, case, timestamp, and schema version metadata.

Repository Map

enterprise-ai-workflow-pattern/
├── README.md
├── LICENSE
├── docs/
│   ├── 01_problem_and_thesis.md
│   ├── 02_reference_architecture.md
│   ├── 03_design_boundaries.md
│   ├── 04_canonical_contracts.md
│   └── 05_schema_examples.md
├── schemas/
│   ├── workflow_request.schema.json
│   ├── fact_asset.schema.json
│   ├── rule_asset.schema.json
│   ├── execution_context.schema.json
│   ├── execution_result.schema.json
│   ├── execution_trace.schema.json
│   └── error_envelope.schema.json
├── examples/
│   └── underwriting_case/
├── src/
│   └── enterprise_ai_workflow_pattern/
│       └── minimal_executor.py
├── tests/
│   └── test_minimal_executor.py
└── assets/
    ├── architecture-overview.svg
    ├── workflow-data-lifecycle.svg
    └── user-journey.svg

Example Scenario

The examples/underwriting_case/ folder provides a compact example of how the pattern can be applied to a document-heavy underwriting workflow:

1. Submit a workflow request with case documents and policy sources.
2. Convert documents into a FactAsset.
3. Convert policy logic into a RuleAsset.
4. Execute the approved rule asset with an ExecutionContext.
5. Return an ExecutionResult and ExecutionTrace.
6. Present facts, result, evidence, and trace for human review.

Underwriting is used as the example domain, but the pattern is reusable for compliance review, eligibility checking, contract review, audit preparation, case adjudication, and other governed enterprise AI workflows.

Run The Minimal Executor

The repository includes a small deterministic executor for the example rule asset. It is intentionally narrow and has no runtime model dependency.

PYTHONPATH=src python3 -m enterprise_ai_workflow_pattern.minimal_executor \
  examples/underwriting_case/rule_asset.json \
  examples/underwriting_case/execution_context.json

Run the tests:

PYTHONPATH=src python3 -m unittest discover -s tests

Documentation

• Problem and Thesis
• Reference Architecture
• Design Boundaries
• Canonical Contracts
• Schema Examples

Status

This repository is a living architecture pattern. It currently focuses on the core architecture, boundaries, contracts, example artifacts, and a minimal deterministic executor needed to explain and reuse the pattern.

Future iterations may add schema validation commands, regression case replay, governance event examples, and a richer function registry.

License

MIT License.