Move use cases section to appendix

Use cases (medtech SDLC, financial trading, logistics) are
motivating examples, not protocol definition. Moving them to
the appendix keeps the normative body focused on format,
transport, validation, and security.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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2026-02-25 16:53:18 +01:00
parent e62b62ff99
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@@ -885,171 +885,6 @@ When an audit ledger is deployed, the implementation MUST provide:
The ledger SHOULD be maintained by an entity independent of the
workflow agents to reduce the risk of collusion.
# Use Cases {#use-cases}
This section describes representative use cases demonstrating how
ECTs provide execution records in regulated environments. These
examples demonstrate ECT mechanics; production deployments would
include additional domain-specific requirements beyond the scope
of this specification.
Note: task identifiers in this section are abbreviated for
readability. In production, all "jti" values are required to be
UUIDs per {{exec-claims}}.
## Medical Device SDLC Workflow
In a medical device software development lifecycle (SDLC),
AI agents assist across multiple phases from requirements
analysis through release approval. Regulatory frameworks
including {{FDA-21CFR11}} Section 11.10(e) and {{EU-MDR}} require
audit trails documenting the complete development process for
software used in medical devices.
~~~
Agent A (Spec Reviewer):
jti: task-001 par: []
exec_act: review_requirements_spec
Agent B (Code Generator):
jti: task-002 par: [task-001]
exec_act: implement_module
Agent C (Test Agent):
jti: task-003 par: [task-002]
exec_act: execute_test_suite
Agent D (Build Agent):
jti: task-004 par: [task-003]
exec_act: build_release_artifact
Human Release Manager:
jti: task-005 par: [task-004]
exec_act: approve_release
ext: {witnessed_by: [...]} (extension metadata)
~~~
{: #fig-medtech-sdlc title="Medical Device SDLC Workflow"}
ECTs record that requirements were reviewed before implementation
began, that tests were executed against the implemented code, that
the build artifact was validated, and that a human release manager
explicitly approved the release. The DAG structure ensures no
phase was skipped or reordered.
### FDA Audit with DAG Reconstruction
During a regulatory audit, an FDA reviewer requests evidence of
the development process for a specific software release. The
auditing authority retrieves all ECTs sharing the same workflow
identifier ("wid") from the audit ledger and reconstructs the
complete DAG:
~~~
task-001 (review_requirements_spec)
|
v
task-002 (implement_module)
|
v
task-003 (execute_test_suite)
|
v
task-004 (build_release_artifact)
|
v
task-005 (approve_release) [human, witnessed]
~~~
{: #fig-fda-audit title="Reconstructed DAG for FDA Audit"}
The reconstructed DAG provides cryptographic evidence that:
- Each phase was executed by an identified and authenticated agent.
- The execution sequence was maintained (no step was bypassed).
- A human-in-the-loop approved the final release, with independent
witness attestation.
- Timestamps and execution durations are recorded for each step.
This can contribute to compliance with:
- {{FDA-21CFR11}} Section 11.10(e): Computer-generated audit trails
that record the date, time, and identity of the operator.
- {{EU-MDR}} Annex II: Technical documentation traceability for the
software development lifecycle.
- {{EU-AI-ACT}} Article 12: Automatic logging capabilities for
high-risk AI systems involved in the development process.
- {{EU-AI-ACT}} Article 14: ECTs can record evidence that human
oversight events occurred during the release process.
## Financial Trading Workflow
In a financial trading workflow, agents perform risk assessment,
compliance verification, and trade execution. The DAG structure
records that compliance checks were evaluated before trade
execution.
~~~
Agent A (Risk Assessment):
jti: task-001 par: []
exec_act: calculate_risk_exposure
Agent B (Compliance):
jti: task-002 par: [task-001]
exec_act: verify_compliance
Agent C (Execution):
jti: task-003 par: [task-002]
exec_act: execute_trade
~~~
{: #fig-finance title="Financial Trading Workflow"}
This can contribute to compliance with:
- {{MIFID-II}}: ECTs provide cryptographic records of the execution
sequence that can support transaction audit requirements.
- {{DORA}} Article 12: ECTs contribute to ICT activity logging.
- {{EU-AI-ACT}} Article 12: Logging of decisions made by AI-driven
systems.
## Compensation and Rollback
Compensation and rollback use cases are described in
{{I-D.nennemann-wimse-ect-pol}}. The core
ECT mechanism supports compensation through the "par" claim,
which links a remediation ECT to the original task.
## Autonomous Logistics Coordination
In a logistics workflow, multiple compliance checks complete
before shipment commitment. The DAG structure records that all
required checks were completed:
~~~
Agent A (Route Planning):
jti: task-001 par: []
exec_act: plan_route
Agent B (Customs):
jti: task-002 par: [task-001]
exec_act: validate_customs
Agent C (Safety):
jti: task-003 par: [task-001]
exec_act: verify_cargo_safety
Agent D (Payment):
jti: task-004 par: [task-002, task-003]
exec_act: authorize_payment
System (Commitment):
jti: task-005 par: [task-004]
exec_act: commit_shipment
~~~
{: #fig-logistics title="Logistics Workflow with Parallel Tasks"}
Note that tasks 002 and 003 both depend only on task-001 and can
execute in parallel. Task 004 depends on both, demonstrating the
DAG's ability to represent parallel execution with a join point.
# Security Considerations
This section addresses security considerations following the
@@ -1376,6 +1211,177 @@ registry are defined in
--- back
# Use Cases {#use-cases}
{:numbered="false"}
This section describes representative use cases demonstrating how
ECTs provide execution records in regulated environments. These
examples demonstrate ECT mechanics; production deployments would
include additional domain-specific requirements beyond the scope
of this specification.
Note: task identifiers in this section are abbreviated for
readability. In production, all "jti" values are required to be
UUIDs per {{exec-claims}}.
## Medical Device SDLC Workflow
{:numbered="false"}
In a medical device software development lifecycle (SDLC),
AI agents assist across multiple phases from requirements
analysis through release approval. Regulatory frameworks
including {{FDA-21CFR11}} Section 11.10(e) and {{EU-MDR}} require
audit trails documenting the complete development process for
software used in medical devices.
~~~
Agent A (Spec Reviewer):
jti: task-001 par: []
exec_act: review_requirements_spec
Agent B (Code Generator):
jti: task-002 par: [task-001]
exec_act: implement_module
Agent C (Test Agent):
jti: task-003 par: [task-002]
exec_act: execute_test_suite
Agent D (Build Agent):
jti: task-004 par: [task-003]
exec_act: build_release_artifact
Human Release Manager:
jti: task-005 par: [task-004]
exec_act: approve_release
ext: {witnessed_by: [...]} (extension metadata)
~~~
{: #fig-medtech-sdlc title="Medical Device SDLC Workflow"}
ECTs record that requirements were reviewed before implementation
began, that tests were executed against the implemented code, that
the build artifact was validated, and that a human release manager
explicitly approved the release. The DAG structure ensures no
phase was skipped or reordered.
### FDA Audit with DAG Reconstruction
{:numbered="false"}
During a regulatory audit, an FDA reviewer requests evidence of
the development process for a specific software release. The
auditing authority retrieves all ECTs sharing the same workflow
identifier ("wid") from the audit ledger and reconstructs the
complete DAG:
~~~
task-001 (review_requirements_spec)
|
v
task-002 (implement_module)
|
v
task-003 (execute_test_suite)
|
v
task-004 (build_release_artifact)
|
v
task-005 (approve_release) [human, witnessed]
~~~
{: #fig-fda-audit title="Reconstructed DAG for FDA Audit"}
The reconstructed DAG provides cryptographic evidence that:
- Each phase was executed by an identified and authenticated agent.
- The execution sequence was maintained (no step was bypassed).
- A human-in-the-loop approved the final release, with independent
witness attestation.
- Timestamps and execution durations are recorded for each step.
This can contribute to compliance with:
- {{FDA-21CFR11}} Section 11.10(e): Computer-generated audit trails
that record the date, time, and identity of the operator.
- {{EU-MDR}} Annex II: Technical documentation traceability for the
software development lifecycle.
- {{EU-AI-ACT}} Article 12: Automatic logging capabilities for
high-risk AI systems involved in the development process.
- {{EU-AI-ACT}} Article 14: ECTs can record evidence that human
oversight events occurred during the release process.
## Financial Trading Workflow
{:numbered="false"}
In a financial trading workflow, agents perform risk assessment,
compliance verification, and trade execution. The DAG structure
records that compliance checks were evaluated before trade
execution.
~~~
Agent A (Risk Assessment):
jti: task-001 par: []
exec_act: calculate_risk_exposure
Agent B (Compliance):
jti: task-002 par: [task-001]
exec_act: verify_compliance
Agent C (Execution):
jti: task-003 par: [task-002]
exec_act: execute_trade
~~~
{: #fig-finance title="Financial Trading Workflow"}
This can contribute to compliance with:
- {{MIFID-II}}: ECTs provide cryptographic records of the execution
sequence that can support transaction audit requirements.
- {{DORA}} Article 12: ECTs contribute to ICT activity logging.
- {{EU-AI-ACT}} Article 12: Logging of decisions made by AI-driven
systems.
## Compensation and Rollback
{:numbered="false"}
Compensation and rollback use cases are described in
{{I-D.nennemann-wimse-ect-pol}}. The core
ECT mechanism supports compensation through the "par" claim,
which links a remediation ECT to the original task.
## Autonomous Logistics Coordination
{:numbered="false"}
In a logistics workflow, multiple compliance checks complete
before shipment commitment. The DAG structure records that all
required checks were completed:
~~~
Agent A (Route Planning):
jti: task-001 par: []
exec_act: plan_route
Agent B (Customs):
jti: task-002 par: [task-001]
exec_act: validate_customs
Agent C (Safety):
jti: task-003 par: [task-001]
exec_act: verify_cargo_safety
Agent D (Payment):
jti: task-004 par: [task-002, task-003]
exec_act: authorize_payment
System (Commitment):
jti: task-005 par: [task-004]
exec_act: commit_shipment
~~~
{: #fig-logistics title="Logistics Workflow with Parallel Tasks"}
Note that tasks 002 and 003 both depend only on task-001 and can
execute in parallel. Task 004 depends on both, demonstrating the
DAG's ability to represent parallel execution with a join point.
# Related Work
{:numbered="false"}