NerdCabalMCP

ARTIFEX NERD SWARM Architecture

A2A Protocol & ADK Framework Compliance Review

Generated: 2025-11-28 Version: 1.0.0 Framework: Agent Development Kit (ADK) + Agent-to-Agent (A2A) Protocol

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Executive Summary

The ARTIFEX NERD SWARM is a composable, modular, and interoperable multi-agent system for AI/ML research lab operations. All 10 agents implement:

• A2A Protocol Compliance: Each agent exposes an AgentCard with name, version, description, capabilities, input/output schemas
• ADK Pattern Support: Agents can be composed using Sequential, Parallel, Loop, and Coordinator patterns
• MCP Integration: All agents accessible via Model Context Protocol (JSON-RPC 2.0)
• Notebook-Based Outputs: Interactive Google Colab notebooks for human-in-loop workflows

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Agent Inventory

1. LLM-Rubric Architect

Version: 1.0.0 Capabilities:

• rubric_generation
• prompt_engineering
• evaluation_framework_design
• quality_criteria_definition

A2A Card: ✅ Implemented Input Schema: RubricGenerationRequest Output Schema: RubricGenerationOutput ADK Integration: Can be used as first step in Sequential pipeline for experiment design

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2. Experimental Designer

Version: 1.0.0 Capabilities:

• experimental_design_generation
• hypothesis_formulation
• methodology_planning
• validation_strategy_design

A2A Card: ✅ Implemented Input Schema: ExperimentalDesignerRequest Output Schema: ExperimentalDesignReport ADK Integration: Works sequentially after Rubric Architect; can trigger Budget Agent via delegation

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3. Budget Agent

Version: 1.0.0 Capabilities:

• grant_budget_generation
• investor_budget_generation
• budget_comparison
• roi_projection
• cost_optimization

A2A Card: ✅ Implemented Input Schema: BudgetAnalysisRequest Output Schema: BudgetAnalysisOutput ADK Integration: Can run in parallel with Experimental Designer or sequentially after; outputs feed into Comptroller

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4. The Comptroller

Version: 1.0.0 Capabilities:

• financial_operations
• burn_rate_tracking
• runway_calculation
• alert_generation
• cost_allocation

A2A Card: ✅ COMPTROLLER_AGENT_CARD Input Schema: ComptrollerRequest Output Schema: Google Colab Notebook ADK Integration: Consumes Budget Agent outputs; runs in parallel with Administrator; reports to Conductor

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5. The Administrator

Version: 1.0.0 Capabilities:

• organizational_governance
• timezone_optimization
• sop_template_generation
• dispute_resolution
• authorship_management

A2A Card: ✅ ADMINISTRATOR_AGENT_CARD Input Schema: AdministratorRequest Output Schema: Google Colab Notebook ADK Integration: Runs in parallel with Comptroller; both feed into hierarchical Operations cluster

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6. MLflow MCP Agent

Version: 1.0.0 Capabilities:

• mlflow_query_planning
• field_selection_optimization
• experiment_tracking_integration
• mcp_server_orchestration

A2A Card: ✅ MLFLOW_AGENT_CARD Input Schema: MLflowAgentRequest Output Schema: MLflowAgentOutput with query plan and connection config ADK Integration: Coordinates with Experimental Designer; tracks outputs from Dataset Builder and model training loops

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7. Dataset Builder

Version: 1.0.0 Capabilities:

• single_turn_dataset_creation
• multi_turn_dataset_creation
• sft_conversion
• dpo_pair_generation
• dataset_filtering
• quality_control
• huggingface_integration

A2A Card: ✅ DATASET_BUILDER_AGENT_CARD Input Schema: DatasetBuilderRequest Output Schema: DatasetBuilderOutput ADK Integration: Sequential pipeline → Experimental Designer → Dataset Builder → MLflow Agent (for tracking)

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8. The CISO (Chief Information Security Officer)

Version: 1.0.0 Capabilities:

• threat_modeling_stride
• incident_response_protocols
• cryptography_management
• social_engineering_defense
• zero_trust_architecture
• red_team_simulations

A2A Card: ✅ CISO_AGENT_CARD Input Schema: CISORequest Output Schema: Google Colab Notebook with 4 security blocks ADK Integration: Runs in parallel across all workflows; provides security review for Experimental Designer, Budget Agent, Dataset Builder, MLflow Agent

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9. The Orchestrator

Version: 1.0.0 Capabilities:

• capability_gap_analysis
• agent_suggestion
• workflow_optimization
• hierarchy_design
• adk_pattern_recommendation

A2A Card: ✅ ORCHESTRATOR_AGENT_CARD Input Schema: OrchestratorRequest Output Schema: OrchestratorOutput with gap analysis, agent suggestions, workflows, hierarchy ADK Integration: Meta-agent - analyzes and optimizes entire swarm; recommends how to compose other agents

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10. The Creative Director

Version: 1.2.0 Capabilities:

• palette_generation
• typography_system_design
• css_framework_creation
• ui_component_generation
• image_prompt_engineering
• brand_consistency_auditing
• voice_persona_rewriting
• creative_strategy_development

A2A Card: ✅ CREATIVE_DIRECTOR_AGENT_CARD Input Schema: CreativeDirectorRequest Output Schema: CreativeDirectorOutput with modular design artifacts ADK Integration: Provides brand/design layer for all agent outputs; can audit Experimental Designer reports, Budget presentations, Administrator governance docs

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ADK Pattern Implementation Examples

Pattern 1: Sequential Research Pipeline

from google.adk.agents import SequentialAgent, LlmAgent

# Step 1: Generate evaluation rubric
rubric_architect = LlmAgent(
    name="RubricArchitect",
    instruction="Generate evaluation rubric for experiment",
    output_key="rubric"
)

# Step 2: Design experiment using rubric
experimental_designer = LlmAgent(
    name="ExperimentalDesigner",
    instruction="Design experiment using rubric from context",
    input_context=["rubric"],
    output_key="experiment_design"
)

# Step 3: Build dataset based on experiment design
dataset_builder = LlmAgent(
    name="DatasetBuilder",
    instruction="Create training dataset for experiment",
    input_context=["experiment_design"],
    output_key="dataset"
)

# Step 4: Track everything in MLflow
mlflow_tracker = LlmAgent(
    name="MLflowAgent",
    instruction="Set up experiment tracking with MLflow MCP",
    input_context=["experiment_design", "dataset"],
    output_key="tracking_uri"
)

research_pipeline = SequentialAgent(
    name="ResearchPipeline",
    sub_agents=[rubric_architect, experimental_designer, dataset_builder, mlflow_tracker]
)

Use Case: End-to-end experiment setup from evaluation criteria to tracked execution

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Pattern 2: Parallel Operations (Fan-Out/Gather)

from google.adk.agents import ParallelAgent, LlmAgent, SequentialAgent

# Financial operations
comptroller = LlmAgent(
    name="Comptroller",
    instruction="Monitor burn rate and generate financial alerts",
    output_key="financial_report"
)

# Organizational operations
administrator = LlmAgent(
    name="Administrator",
    instruction="Manage team coordination and SOPs",
    output_key="governance_report"
)

# Security operations (runs continuously)
ciso = LlmAgent(
    name="CISO",
    instruction="Perform threat modeling and incident response",
    output_key="security_report"
)

operations_cluster = ParallelAgent(
    name="OperationsCluster",
    sub_agents=[comptroller, administrator, ciso]
)

# Aggregator collects all reports
aggregator = LlmAgent(
    name="OperationsAggregator",
    instruction="Synthesize financial, governance, and security reports",
    input_context=["financial_report", "governance_report", "security_report"],
    output_key="operations_summary"
)

operations_workflow = SequentialAgent(
    name="OperationsWorkflow",
    sub_agents=[operations_cluster, aggregator]
)

Use Case: Parallel execution of independent operations domains with unified reporting

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Pattern 3: Iterative Refinement (Generator-Critic with Loop)

from google.adk.agents import LoopAgent, LlmAgent

# Generator: Create initial experiment design
generator = LlmAgent(
    name="ExperimentalDesigner",
    instruction="Generate experiment methodology",
    output_key="design"
)

# Critic: Budget agent evaluates cost feasibility
critic = LlmAgent(
    name="BudgetAgent",
    instruction="Analyze budget feasibility and suggest optimizations. Return 'APPROVED' if under budget, else 'REVISE: [specific issues]'",
    input_context=["design"],
    output_key="budget_review"
)

# Reviser: Experimental designer refines based on feedback
reviser = LlmAgent(
    name="ExperimentalDesignerReviser",
    instruction="Revise experiment design based on budget feedback",
    input_context=["design", "budget_review"],
    output_key="design"
)

def check_approval(context):
    return "APPROVED" in context.state.get("budget_review", "")

budget_optimized_design = LoopAgent(
    name="BudgetOptimizedDesignLoop",
    sub_agents=[generator, critic, reviser],
    max_iterations=5,
    stop_condition=check_approval
)

Use Case: Iteratively refine experiment design until budget constraints are satisfied

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Pattern 4: Hierarchical Decomposition (Conductor Pattern)

from google.adk.agents import LlmAgent

# Top-level coordinator (The Orchestrator as Conductor)
conductor = LlmAgent(
    name="Conductor",
    instruction="Analyze incoming research request and delegate to appropriate cluster",
    output_key="delegation_plan",
    transfer_to_agent_on_output=True  # LLM-driven delegation
)

# Define clusters as callable agents
research_cluster = SequentialAgent(...)  # Rubric → Designer → Dataset → MLflow
operations_cluster = ParallelAgent(...)   # Comptroller || Administrator
security_cluster = LlmAgent(name="CISO", ...)
design_cluster = LlmAgent(name="CreativeDirector", ...)

# Conductor routes to appropriate cluster based on request
# Uses transfer_to_agent() for dynamic delegation

Agent Hierarchy:

Conductor (Orchestrator)
├── Research Cluster
│   ├── Rubric Architect
│   ├── Experimental Designer
│   ├── Dataset Builder
│   └── MLflow Agent
├── Operations Cluster
│   ├── Comptroller
│   └── Administrator
├── Security Cluster
│   └── CISO
└── Design Cluster
    └── Creative Director

Use Case: Intelligent routing of heterogeneous requests to specialized agent clusters

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Pattern 5: Human-in-Loop with Security Review

from google.adk.agents import SequentialAgent, LlmAgent

# Step 1: Design experiment
experimental_designer = LlmAgent(
    name="ExperimentalDesigner",
    instruction="Design experiment methodology",
    output_key="design"
)

# Step 2: CISO security review (critical checkpoint)
ciso_review = LlmAgent(
    name="CISOSecurityReview",
    instruction="Perform STRIDE threat modeling on experiment design. Identify security risks.",
    input_context=["design"],
    output_key="security_review"
)

# Step 3: Human approval (external tool call or webhook)
human_approval = ExternalAgent(
    name="HumanApproval",
    instruction="Wait for human to approve or reject security-reviewed design",
    input_context=["design", "security_review"],
    output_key="approval_status"
)

# Step 4: Proceed only if approved
def check_human_approval(context):
    return context.state.get("approval_status") == "APPROVED"

secure_experiment_workflow = SequentialAgent(
    name="SecureExperimentWorkflow",
    sub_agents=[experimental_designer, ciso_review, human_approval],
    conditional_execution=check_human_approval  # Only continues if approved
)

Use Case: Security-critical workflows requiring human oversight before proceeding

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Interoperability Matrix

Agent → Consumes Output From ↓	Rubric	Experimental	Budget	Comptroller	Admin	MLflow	Dataset	CISO	Orchestrator	Creative
Rubric Architect	-	✅	❌	❌	❌	❌	❌	✅	✅	✅
Experimental Designer	✅	-	✅	❌	❌	✅	❌	✅	✅	✅
Budget Agent	❌	✅	-	❌	❌	❌	❌	✅	✅	✅
Comptroller	❌	❌	✅	-	✅	✅	❌	✅	✅	✅
Administrator	❌	❌	❌	✅	-	❌	❌	✅	✅	✅
MLflow Agent	❌	✅	❌	❌	❌	-	✅	✅	✅	❌
Dataset Builder	✅	✅	❌	❌	❌	✅	-	✅	✅	❌
CISO	✅	✅	✅	✅	✅	✅	✅	-	✅	✅
Orchestrator	✅	✅	✅	✅	✅	✅	✅	✅	-	✅
Creative Director	✅	✅	✅	✅	✅	❌	❌	✅	✅	-

Legend:

• ✅ High Interoperability: Agent directly consumes or benefits from other agent’s outputs
• ❌ Low Interoperability: Minimal direct interaction

Key Insights:

• CISO and Orchestrator are universal consumers - they integrate with all other agents
• Creative Director provides cross-cutting design layer for user-facing outputs
• Research Cluster (Rubric → Experimental → Dataset → MLflow) forms a tight sequential pipeline
• Operations Cluster (Comptroller + Administrator) operates semi-independently but shares financial data

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Modularity & Composability Assessment

✅ Strengths

1. A2A Protocol Compliance: All agents expose AgentCard with standardized capabilities
2. JSON-RPC MCP Interface: Uniform access pattern via Model Context Protocol
3. Schema-Driven I/O: TypeScript types ensure type-safe agent composition
4. Notebook Outputs: Human-in-loop integration via Google Colab for critical decisions
5. ADK Primitive Support: Agents work with Sequential, Parallel, Loop, Coordinator patterns
6. Shared Session State: Context can flow between agents via context.state in ADK

⚠️ Areas for Enhancement

1. Explicit Agent Tools: Consider wrapping each agent as an AgentTool for easier discovery
2. Streaming Outputs: Long-running agents (Experimental Designer, Dataset Builder) could support streaming
3. Checkpoint/Resume: For expensive operations (dataset creation), add checkpoint support
4. Retry Logic: Add exponential backoff for LLM calls in ADK patterns
5. Observability: Integrate OpenTelemetry tracing for multi-agent workflows
6. Versioning: Implement semantic versioning for agent cards to handle breaking changes

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Recommended Workflow Templates

Template 1: Full Research Lifecycle

[User Request]
  → Conductor (Orchestrator)
    → Research Cluster (Sequential)
      → Rubric Architect
      → Experimental Designer
      → Dataset Builder
      → MLflow Agent (tracking)
    → Operations Cluster (Parallel)
      → Budget Agent → Comptroller
      → Administrator
    → Security Layer (Parallel)
      → CISO (threat modeling)
    → Design Layer
      → Creative Director (report formatting)
  → [Human Review] → [Execute]

Template 2: Budget-Constrained Experiment

[User Request]
  → Experimental Designer (initial design)
  → LoopAgent:
    → Budget Agent (review)
    → If over budget: Designer revises
    → If under budget: Exit loop
  → CISO (security review)
  → MLflow Agent (setup tracking)
  → Dataset Builder (create data)
  → [Execute]

Template 3: Operations Dashboard

[Scheduled Trigger - Daily]
  → ParallelAgent:
    → Comptroller (financial metrics)
    → Administrator (team updates)
    → CISO (security alerts)
    → MLflow Agent (experiment summaries)
  → Aggregator LLM (synthesize)
  → Creative Director (format dashboard)
  → [Email to Leadership]

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Conclusion

The ARTIFEX NERD SWARM demonstrates high composability, modularity, and interoperability through:

• ✅ Consistent A2A agent card pattern
• ✅ MCP-based uniform interface
• ✅ ADK pattern compatibility (Sequential, Parallel, Loop, Coordinator)
• ✅ Clear capability boundaries with minimal overlap
• ✅ Hierarchical clustering (Research, Operations, Security, Design)
• ✅ Human-in-loop integration via notebooks

Next Steps:

1. Implement 5 workflow templates as reusable ADK SequentialAgent compositions
2. Add OpenTelemetry tracing for observability
3. Create agent tool wrappers for easier LLM-driven delegation
4. Build unified CLI for invoking multi-agent workflows
5. Implement checkpoint/resume for long-running pipelines

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Maintained by: ARTIFEX NERD SWARM Contact: [Your Research Lab] License: [Your License]

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