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Add ASCII art figures to all 6 generated Internet-Drafts

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Each draft gets 2 illustrative figures:
- ABVP: architecture components + verification workflow
- ATD: example DAG structure + execution state transitions
- HITL: primitive framework overview + approval workflow sequence
- AEM/PPALP: federated learning architecture + aggregation flow
- RARP: cross-domain architecture + two-phase rollback protocol
- APAE: layered architecture + cross-domain provenance tracking

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Christian Nennemann
2026-03-04 02:20:39 +01:00
parent 404092b938
commit 3c3d7e649f
6 changed files with 236 additions and 0 deletions
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32
data/reports/generated-drafts/draft-ai-agent-behavior-verification-protocol-00.txt
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@@ -299,6 +299,21 @@ Table of Contents
with existing agent authentication and authorization frameworks as
defined in [RFC6749] and referenced in [draft-aylward-aiga-1].
+----------+ +--------------+ +-------------+
| | | Behavior | | |
| Agent |--->| Attestation |--->| Verification|
| | | Engine | | Registry |
+----------+ +------+-------+ +------+------+
| |
v v
+------+-------+ +------+------+
| Behavior | | Trust |
| Traces | | Anchor |
| (signed) | | Management |
+--------------+ +-------------+
Figure 1: ABVP Architecture Components
The Behavior Attestation Engine (BAE) serves as the core component
responsible for capturing agent behavior traces and generating
cryptographic attestations. Each BAE MUST maintain a secure
@@ -511,6 +526,23 @@ Table of Contents
existing authorization frameworks while providing the behavioral
assurance layer necessary for autonomous agent operations.
Agent BAE Registry Verifier
| | | |
|--action-->| | |
| |--trace------>| |
| |--attest----->| |
| | | |
| | |<--query-----|
| | |---proof---->|
| | | |--eval
| | | |
| | | [batch] |
| | |<--audit-----|
| | |---report--->|
| | | |
Figure 2: Real-time Verification Workflow
Real-time verification workflows enable immediate assessment of
agent behavior during active operations. When an agent initiates
actions that require behavioral validation, the Behavior

40
data/reports/generated-drafts/draft-ai-agent-learning-protocol-00.txt
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@@ -337,6 +337,29 @@ Table of Contents
architectural levels to provide defense-in-depth protection
against information leakage.
Tenant A Tenant B Tenant C
+--------+ +--------+ +--------+
| Agent | | Agent | | Agent |
| Local | | Local | | Local |
| Model | | Model | | Model |
+---+----+ +---+----+ +---+----+
| | |
v v v
+---+----+ +---+----+ +---+----+
| DP | | DP | | DP |
| Noise | | Noise | | Noise |
+---+----+ +---+----+ +---+----+
| | |
+-------+-------+-------+------+
| |
+----+-----+ +----+------+
| Secure | | Privacy |
| Aggre- | | Compliance|
| gation | | Verifier |
+----------+ +-----------+
Figure 1: PPALP Federated Learning Architecture
The PPALP architecture defines four primary component roles that
collectively ensure privacy-preserving federated learning
operations. The Federated Coordinator serves as the central
@@ -471,6 +494,23 @@ Table of Contents
computational efficiency suitable for practical federated learning
deployments.
Agent_i Aggregator Verifier
| | |
|--local grad--->| |
| + DP noise | |
| | |
| [...N agents contribute...] |
| | |
| |--aggregate---->|
| | model update |
| | |
| |<--ZK proof-----|
| | compliance |
|<--global upd---| |
| | |
Figure 2: Privacy-Preserving Aggregation Flow
The Multi-Tenant Federated Averaging mechanism serves as the
foundation for secure parameter aggregation. Each tenant's Privacy
Compliance Verifier MUST encrypt their differentially-private

35
data/reports/generated-drafts/draft-ai-agent-provenance-assurance-ecosystem-00.txt
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@@ -370,6 +370,28 @@ Table of Contents
to implement provenance assurance incrementally without disrupting
existing agent ecosystems.
+------------------------------------------------+
| Provenance Query Layer |
| (cross-domain search, history retrieval) |
+------------------------------------------------+
| Cross-Domain Tracking Layer |
| (gateway coordination, chain linking) |
+------------------------------------------------+
| Operational Audit Layer |
| (action logging, decision trails) |
+------------------------------------------------+
| Capability Verification Layer |
| (skill attestation, scope validation) |
+------------------------------------------------+
| Identity Attestation Layer |
| (agent ID, key management, TEE binding) |
+------------------------------------------------+
| Trust Infrastructure Layer |
| (PKI, trust anchors, revocation) |
+------------------------------------------------+
Figure 1: APAE Layered Architecture
The Identity Attestation Layer serves as the foundational trust
anchor for the APAE framework, establishing and maintaining
verifiable agent identities throughout their operational
@@ -683,6 +705,19 @@ Table of Contents
established protocols and standards while extending them with
agent-specific provenance metadata and verification mechanisms.
Origin Org Gateway Target Org
+----------+ +---------+ +----------+
| Agent | |Provnce | | Verifier |
| Registry |--->| Bridge |--->| Registry |
+----+-----+ +----+----+ +----+-----+
| | |
+----+-----+ +----+----+ +----+-----+
|Provenance| | Chain | |Provenance|
| Record |--->| Linker |--->| Record |
+----------+ +---------+ +----------+
Figure 2: Cross-Domain Provenance Tracking
Integration with OAuth 2.0 and OpenID Connect ecosystems follows
the Agent Authorization Profile (AAP) [draft-aap-oauth-profile]
extension pattern. Existing OAuth 2.0 authorization servers can be

46
data/reports/generated-drafts/draft-ai-agent-rollback-protocol-00.txt
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@@ -319,6 +319,30 @@ Table of Contents
gateway-intercomm-framework] to provide cross-domain rollback
coordination capabilities.
Domain A Domain B
+---------------------+ +---------------------+
| +------+ +------+ | | +------+ +------+ |
| |Agt 1 | |Agt 2 | | | |Agt 3 | |Agt 4 | |
| +--+---+ +--+---+ | | +--+---+ +--+---+ |
| | | | | | | |
| +--+---------+----+ | | +--+---------+----+ |
| |Checkpoint Manager| | | |Checkpoint Manager| |
| +--------+---------+ | | +--------+---------+ |
| | | | | |
| +--------+---------+ | | +--------+---------+ |
| |Rollback Coordintr| | | |Rollback Coordintr| |
| +--------+---------+ | | +--------+---------+ |
+----------|------------+ +----------|------------+
| |
+----------+ +--------------+
| |
+----+--+----+
| Agent |
| Gateway |
+------------+
Figure 1: RARP Cross-Domain Architecture
The core RARP architecture consists of three primary component
types: Rollback Coordinators, Checkpoint Managers, and Agent
Rollback Interfaces. Rollback Coordinators serve as the
@@ -461,6 +485,28 @@ Table of Contents
to validate the rollback request against their local checkpoint
metadata.
Coordinator Agent A Agent B Agent C
| | | |
|--PREPARE---->| | |
|--PREPARE---------------->| |
|--PREPARE------------------------------>|
| | | |
|<--READY------| | |
|<--READY------------------| |
|<--READY---------------------------------|
| | | |
|--COMMIT----->| | |
|--COMMIT----------------->| |
|--COMMIT------------------------------->|
| | | |
| (rollback) | (rollback) | (rollback) |
| | | |
|<--DONE-------| | |
|<--DONE-------------------| |
|<--DONE---------------------------------|
Figure 2: Two-Phase Coordinated Rollback
The coordination messaging framework builds upon the Cross-Domain
Agent Collaboration Protocol [draft-han-rtgwg-agent-gateway-
intercomm-framework] to enable rollback operations across

39
data/reports/generated-drafts/draft-ai-agent-task-a-00.txt
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@@ -310,6 +310,33 @@ Table of Contents
execution parameters that govern how the workflow should be
processed.
+-----------+
| Task A |
| (ingest) |
+-----+-----+
/ \
/ \
v v
+---------+ +---------+
| Task B | | Task C |
| (parse) | |(enrich) |
+----+----+ +----+----+
\ /
\ /
v v
+-----------+
| Task D |
| (merge) |
+-----+-----+
|
v
+-----------+
| Task E |
| (report) |
+-----------+
Figure 1: Example Agent Task DAG Structure
Task nodes within the DAG represent atomic units of work that can
be executed by autonomous agents. Each task node MUST specify its
execution requirements, including required agent capabilities,
@@ -386,6 +413,18 @@ Table of Contents
alternate execution paths depending on the configured failure
handling policies.
+---------+ deps met +-------+ assign +----------+
| Pending |----------->| Ready |--------->|Executing |
+---------+ +---+---+ +----+--+--+
| | |
| skip fail| |ok
v v v
+--------+ +------+--+---+
|Skipped | |Failed|Compl.|
+--------+ +------+------+
Figure 2: Task Node Execution State Transitions
Integration with existing agent protocols occurs through
standardized interfaces that abstract the underlying communication
mechanisms. The framework MUST support protocol-agnostic bindings

44
data/reports/generated-drafts/draft-ai-primitives-for-00.txt
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@@ -308,6 +308,32 @@ Table of Contents
these primitives to be composed into comprehensive oversight
systems tailored to specific operational requirements.
+-----------------------+
| AI Agent Decision |
| Loop |
+-----------+-----------+
|
v
+-----------+-----------+
| Decision Point |
| Evaluator |
+-----------+-----------+
/ | \
/ | \
v v v
+------+ +------+ +---------+
|Appro-| |Over- | |Explain- |
| val | |ride | |ability |
+--+---+ +--+---+ +----+----+
| | |
v v v
+--+--------+-----+ +-+--------+
| Human Operator | | Audit |
| Interface | | Log |
+------------------+ +---------+
Figure 1: HITL Primitive Framework Overview
At the architectural level, HITL primitives integrate with agent
systems through well-defined decision points where human
intervention may be required. These decision points are identified
@@ -355,6 +381,24 @@ Table of Contents
preserving operational efficiency through selective intervention
points.
Agent HITL Middleware Human Operator
| | |
|--action----->| |
| request |--approval req---->|
| | +context, risk |
| | |
| |<---approve/deny---|
| | +auth token |
|<--result-----| |
| proceed | |
| or halt | |
| | |
| [timeout] | |
|<--fallback---| |
| | |
Figure 2: Approval Workflow Sequence
The core approval request structure MUST include the proposed
action description, associated risk assessment, relevant context
for human evaluation, and expected execution timeline. Each