v0.1.0-research — Research Snapshot of Certified Workflow Conversion

v0.1.0-research — Research Snapshot of Certified Workflow Conversion

This release is a research snapshot of Certified Workflow Conversion (CWC), a local-first Python toolkit for diagnosing bottlenecks in long-running AI agent workflows under typed evidence, declared contracts, and fail-closed reporting rules.

CWC treats an AI agent pipeline as a workflow conversion network. Instead of asking whether a model is generally “smart enough,” it asks a narrower engineering question:

Given the declared evidence ledger, contracts, witnesses, checks, and workflow graph, which conversion layer limits conservative certified throughput?

This release is intended as a stable reference point for citation, archival, review, and reuse. It is not a commitment to active feature development.

What this release contains

• A local-first Python implementation of evidence-bound workflow conversion diagnostics.
• Typed evidence records and append-oriented local state handling.
• Workflow conversion network definitions.
• Light-profile bottleneck diagnostics for engineering triage.
• Full-profile conservative lower-bound reporting checks.
• Fail-closed behavior when required evidence, witnesses, roots, or contracts are missing or inactive.
• JSON schemas, examples, tests, documentation, and reproducible local execution via uv.

Intended use

This release is intended for:

• AI infrastructure engineers studying long-running agent pipelines.
• Researchers working on auditable AI workflows and evidence-bound reporting.
• Developers exploring model-independent bottleneck analysis.
• Agent-system designers who want a reference implementation for conservative workflow-throughput claims.

The main practical use is to clarify where candidate AI outputs fail to become certified usable workflow output: generation, validation, review, authorization, release, rollback, incident response, memory governance, or another conversion layer.

Scope and limitations

CWC does not certify factual truth, model intelligence, alignment, or production safety.

A CWC report should be interpreted more narrowly:

A lower-bound workflow claim is procedurally admissible under the declared evidence, contracts, witnesses, roots, and checks.

Production deployments still require independent sandboxing, secrets management, identity controls, policy enforcement, external-effect gating, domain-specific validation, monitoring, and incident-response infrastructure.

Maintenance status

This release is a research snapshot.

No active feature development is currently planned. Issues and pull requests may not be actively reviewed. Users should treat this repository as a reference implementation and archival artifact rather than a maintained production dependency.

Repository

https://github.com/kadubon/certified-workflow-conversion

Citation

If you use, discuss, adapt, or evaluate this software, please cite the associated Zenodo record once available.

Suggested citation format:

Takahashi, K. (2026). Certified Workflow Conversion: Evidence-Bound Workflow Diagnostics and Conservative Lower-Bound Reporting for Long-Running AI Agent Pipelines (v0.1.0-research) [Computer software]. Zenodo. DOI: https://doi.org/10.5281/zenodo.20060372