SDD+ – A Comprehensive Paper

Title: SDD+ – Spec-Driven Development for Distributed Multi-Agent Systems

Abstract

SDD+ (Spec-Driven Development Plus) is a comprehensive methodology that combines rigorous artifact-driven development with modern distributed multi-agent system patterns. It provides two core capabilities: (1) treating specifications, architecture history, prompt history, tests, and automated evaluations as first-class artifacts for traceability and quality control, and (2) offering patterns, templates, and reference implementations for building scalable, distributed multi-agent applications using OpenAI Agents SDK, MCP, A2A, Docker, Kubernetes, Dapr (Actors & Workflows), and Ray. SDD+ enables teams to define specs, spin up services, orchestrate agents, and ship production-ready stacks faster with guardrails, CI-friendly scaffolds, and complete audit trails. This paper defines SDD+, details its dual focus on artifact management and multi-agent orchestration, and provides implementation guidance for modern AI-augmented systems.

Keywords: SDD+, Spec-Driven Development, multi-agent systems, OpenAI Agents SDK, MCP, A2A, Kubernetes, Dapr, Ray, AHR, PHR, evals, distributed systems, agent orchestration

Introduction
Core Pillars of SDD+
Multi-Agent Architecture Components
Artifact-Driven Development Framework
Agent Orchestration Patterns
Cloud-Native Runtime Stack
Specifications for Agent Systems
Architecture History for Distributed Systems
Prompt History in Multi-Agent Context
Testing & Evaluation in Agent Networks
Implementation Patterns with Modern Stacks
CI/CD for Multi-Agent Applications
Example: Building a Multi-Agent System with SDD+
Migration Strategy and Adoption
Conclusion
Glossary & Abbreviations

1. Introduction

Modern software systems increasingly rely on distributed multi-agent architectures where autonomous agents collaborate to solve complex problems. Traditional development methodologies struggle with the unique challenges of agent systems: coordination complexity, prompt engineering, distributed state management, and the need for rigorous testing of emergent behaviors.

SDD+ addresses these challenges by combining two powerful approaches:

Artifact-Driven Development: Rigorous tracking of specifications, architecture decisions, prompts, tests, and evaluations as first-class artifacts
Multi-Agent System Patterns: Production-ready templates and patterns for building scalable agent applications with modern stacks

This dual focus enables teams to build complex agent systems with confidence, maintaining both development velocity and system reliability through comprehensive tooling, patterns, and guardrails.

2. Core Pillars of SDD+

2.1 Artifact-Driven Foundation

SDD+ treats all development artifacts as first-class citizens:

Specifications define agent behaviors and inter-agent protocols
Architecture History Records (AHR) capture distributed system design decisions
Prompt History Records (PHR) version and track agent prompts and templates
Tests and Evaluations validate both individual agents and emergent system behaviors
Traceability Links connect all artifacts for audit and debugging

2.2 Multi-Agent Orchestration

SDD+ provides comprehensive patterns for:

Agent Definition: Using OpenAI Agents SDK for agent creation
Communication: MCP (Model Context Protocol) and A2A (Agent-to-Agent) protocols
Orchestration: Dapr Actors and Workflows for stateful agent coordination
Scaling: Ray for distributed compute and parallel agent execution
Deployment: Docker and Kubernetes for containerized agent services

2.3 Production-Ready Scaffolding

Pre-built templates for common agent patterns
CI/CD pipelines optimized for agent deployments
Monitoring and observability for distributed agent systems
Guardrails and safety mechanisms for agent interactions

3. Multi-Agent Architecture Components

3.1 OpenAI Agents SDK Integration

The OpenAI Agents SDK serves as the foundation for individual agent implementation in SDD+:

agent_spec:
  id: customer-service-agent
  sdk: openai-agents-v2
  capabilities:
    - natural_language_understanding
    - tool_use
    - memory_management
  tools:
    - database_query
    - api_calls
    - knowledge_retrieval

3.2 MCP (Model Context Protocol)

MCP enables standardized context sharing between agents:

Context Windows: Managed sharing of conversation history
State Synchronization: Consistent state across agent boundaries
Protocol Versioning: Backward-compatible agent communication

3.3 A2A (Agent-to-Agent) Communication

A2A protocols define how agents collaborate:

Message Formats: Structured inter-agent communication
Negotiation Protocols: For task distribution and conflict resolution
Event Streams: Real-time agent coordination

3.4 Dapr Integration

Dapr provides the distributed application runtime:

Actors: Stateful agent instances with guaranteed single-threaded execution
Workflows: Orchestration of multi-agent processes
State Management: Distributed state stores for agent memory
Pub/Sub: Event-driven agent communication

3.5 Ray for Distributed Compute

Ray enables massive agent parallelization:

Distributed Training: For agent model improvements
Parallel Execution: Running thousands of agents simultaneously
Resource Management: Optimal GPU/CPU allocation for agents

4. Artifact-Driven Development Framework

4.1 Specification Types for Agent Systems

Agent Behavior Specifications

id: spec-agent-001
type: agent_behavior
agent: customer-service
behaviors:
  - trigger: user_greeting
    response: personalized_welcome
    sla: 200ms
  - trigger: complaint
    response: empathetic_resolution
    escalation: human_handoff_if_needed

Inter-Agent Protocol Specifications

id: spec-protocol-001
type: a2a_protocol
participants: [agent-a, agent-b]
messages:
  - type: task_request
    schema: ./schemas/task_request.json
  - type: task_response
    schema: ./schemas/task_response.json
coordination: async_with_timeout

4.2 Architecture History for Distributed Systems

AHR in SDD+ captures distributed system decisions:

# ADR-015: Choose Dapr Actors for Agent State Management
Date: 2025-09-29
Status: Accepted
Context: Need reliable state management for 10,000+ concurrent agent instances
Decision: Use Dapr Actors with Redis state store
Alternatives:
  - Ray Serve: Better for stateless, high-throughput
  - Custom solution: Too much complexity
Consequences: 
  - Guaranteed single-threaded execution per agent
  - Automatic failover and state recovery
  - Some latency overhead for actor activation
Related: spec-agent-001, deployment-config-k8s

4.3 Prompt History for Multi-Agent Systems

PHR tracks prompts across all agents:

{
  "prompt_id": "phr-multi-001",
  "agent": "coordinator-agent",
  "version": "v2",
  "template": "You are coordinating between {agent_count} specialized agents...",
  "chain_prompts": [
    {"agent": "analyzer", "prompt_ref": "phr-analyzer-003"},
    {"agent": "synthesizer", "prompt_ref": "phr-synth-002"}
  ],
  "performance": {
    "coordination_accuracy": 0.89,
    "latency_ms": 450
  }
}

5. Agent Orchestration Patterns

5.1 Pipeline Pattern

Agents process requests in sequence:

pipeline:
  name: document-processing
  stages:
    - agent: ocr-agent
      input: raw_document
      output: extracted_text
    - agent: nlp-agent
      input: extracted_text
      output: structured_data
    - agent: validation-agent
      input: structured_data
      output: validated_result

5.2 Supervisor Pattern

A coordinator agent manages specialist agents:

supervisor:
  coordinator: meta-agent
  workers:
    - type: research-agent
      count: 5
      assignment: dynamic
    - type: writer-agent
      count: 3
      assignment: round-robin

5.3 Consensus Pattern

Multiple agents vote on decisions:

consensus:
  voters: [agent-1, agent-2, agent-3]
  voting_mechanism: weighted_majority
  tie_breaker: supervisor-agent

6. Cloud-Native Runtime Stack

6.1 Docker Containerization

Each agent runs in its own container:

FROM python:3.11-slim
RUN pip install openai-agents-sdk dapr ray
COPY ./agent /app/agent
COPY ./specs /app/specs
COPY ./phr /app/phr
CMD ["python", "-m", "agent.main"]

6.2 Kubernetes Orchestration

Deploy agents as Kubernetes resources:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: customer-agent
  annotations:
    dapr.io/enabled: "true"
    dapr.io/app-id: "customer-agent"
spec:
  replicas: 10
  template:
    spec:
      containers:
      - name: agent
        image: myregistry/customer-agent:v2
        env:
        - name: AGENT_SPEC_ID
          value: "spec-agent-001"

6.3 Dapr Configuration

Enable stateful agent coordination:

apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
  name: agent-statestore
spec:
  type: state.redis
  metadata:
  - name: redisHost
    value: redis-master:6379
  - name: actorStateStore
    value: "true"

7. Specifications for Agent Systems

7.1 Agent Capability Specifications

Define what each agent can do:

Tool access and permissions
Memory and context limits
Response time requirements
Escalation triggers

7.2 Coordination Specifications

Define how agents work together:

Communication protocols
Task distribution strategies
Conflict resolution mechanisms
Consensus requirements

7.3 Safety Specifications

Define guardrails and constraints:

Rate limits per agent
Content filtering rules
Audit logging requirements
Human-in-the-loop triggers

8. Architecture History for Distributed Systems

Track critical decisions in multi-agent architecture:

8.1 Scaling Decisions

When to scale horizontally vs vertically
Agent pooling strategies
Resource allocation policies

8.2 Communication Architecture

Synchronous vs asynchronous patterns
Message queue selections
Protocol versioning strategies

8.3 State Management

Centralized vs distributed state
Consistency models
Backup and recovery strategies

9. Prompt History in Multi-Agent Context

9.1 Prompt Versioning Across Agents

Coordinated prompt updates
A/B testing across agent fleets
Rollback strategies

9.2 Context Window Management

Shared context between agents
Context compression strategies
Memory hierarchy design

9.3 Prompt Chain Optimization

Multi-hop prompt sequences
Dynamic prompt selection
Performance tracking per chain

10. Testing & Evaluation in Agent Networks

10.1 Unit Testing Individual Agents

def test_customer_agent_greeting():
    agent = CustomerAgent(spec_id="spec-agent-001")
    response = agent.process("Hello")
    assert response.tone == "friendly"
    assert response.latency_ms < 200

10.2 Integration Testing Agent Interactions

def test_agent_coordination():
    supervisor = SupervisorAgent()
    workers = [WorkerAgent(i) for i in range(3)]
    result = supervisor.coordinate_task(task, workers)
    assert result.consensus_reached == True

10.3 System-Level Evaluations

End-to-end latency measurements
Throughput under load
Failure recovery testing
Emergent behavior validation

10.4 Continuous Evaluation Pipelines

eval_pipeline:
  - stage: agent_unit_tests
    frequency: on_commit
  - stage: integration_tests
    frequency: hourly
  - stage: load_tests
    frequency: daily
  - stage: chaos_tests
    frequency: weekly

11. Implementation Patterns with Modern Stacks

11.1 OpenAI Agents SDK Pattern

from openai_agents import Agent, Tool
from sdd_plus import Specification, PHR

class CustomAgent(Agent):
    def __init__(self, spec_id):
        self.spec = Specification.load(spec_id)
        self.phr = PHR.load(self.spec.phr_id)
        super().__init__(
            instructions=self.phr.get_prompt(),
            tools=self.spec.get_tools()
        )

11.2 MCP Integration Pattern

from mcp import ContextProtocol
from sdd_plus import AHR

class AgentWithMCP:
    def __init__(self):
        self.context = ContextProtocol(
            version="1.0",
            history_limit=AHR.get_config("context_limit")
        )
    
    def share_context(self, other_agent):
        return self.context.export_for(other_agent.id)

11.3 Dapr Actor Pattern

from dapr.actor import Actor
from sdd_plus import ActorSpec

class StatefulAgent(Actor):
    def __init__(self, actor_id):
        super().__init__(actor_id)
        self.spec = ActorSpec.load(actor_id)
        self.state = {}
    
    async def process_message(self, message):
        # Guaranteed single-threaded execution
        self.state.update(message.context)
        return await self.execute_with_spec(message)

11.4 Ray Distributed Pattern

import ray
from sdd_plus import RayConfig

@ray.remote(num_gpus=RayConfig.get("gpu_per_agent"))
class DistributedAgent:
    def __init__(self, agent_spec):
        self.spec = agent_spec
        self.model = self.load_model()
    
    def process_batch(self, requests):
        return [self.process(req) for req in requests]

# Scale to thousands of agents
agents = [DistributedAgent.remote(spec) for _ in range(1000)]

12. CI/CD for Multi-Agent Applications

12.1 Build Pipeline

stages:
  - validate_specs:
      - Check spec syntax
      - Verify spec coverage
      - Validate linked artifacts
  
  - test_agents:
      - Unit tests per agent
      - Integration tests
      - Prompt validation
  
  - build_containers:
      - Build agent images
      - Security scanning
      - Size optimization
  
  - run_evals:
      - Functionality tests
      - Performance benchmarks
      - Safety checks

12.2 Deployment Pipeline

deployment:
  - stage: staging
    steps:
      - Deploy with Helm
      - Run smoke tests
      - Monitor for 1 hour
  
  - stage: canary
    steps:
      - Deploy 10% traffic
      - Compare metrics
      - Auto-rollback on failure
  
  - stage: production
    steps:
      - Blue-green deployment
      - Full traffic migration
      - Archive artifacts

13. Example: Building a Multi-Agent System with SDD+

Complete Example: Customer Support System

Step 1: Define Specifications

# specs/system-spec.yaml
id: spec-system-001
name: Multi-Agent Customer Support
agents:
  - triage-agent: Routes inquiries
  - research-agent: Finds solutions
  - response-agent: Crafts responses
  - escalation-agent: Handles complex cases
sla:
  response_time: 5s
  resolution_rate: 85%

Step 2: Architecture Decision

# ahr/adr-020.md
Title: Multi-Agent Architecture for Customer Support
Decision: Use supervisor pattern with specialized agents
Rationale: Allows independent scaling and specialization
Stack: OpenAI Agents SDK + Dapr Actors + K8s

Step 3: Create Agent Implementations

# agents/triage_agent.py
from openai_agents import Agent
from dapr.actor import Actor

class TriageAgent(Actor, Agent):
    def __init__(self):
        Agent.__init__(self, 
            instructions=PHR.load("triage-v1"),
            tools=["categorize", "route"])
        Actor.__init__(self, "triage-agent")
    
    async def process_inquiry(self, inquiry):
        category = await self.categorize(inquiry)
        target_agent = self.route_logic(category)
        return await self.forward_to(target_agent, inquiry)

Step 4: Deploy with Kubernetes

# k8s/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: triage-agent
spec:
  replicas: 5
  template:
    spec:
      containers:
      - name: agent
        image: support-system/triage-agent:v1
        env:
        - name: DAPR_HTTP_PORT
          value: "3500"

Step 5: Setup Monitoring & Evals

# evals/support-system.yaml
evaluations:
  - name: response_quality
    frequency: hourly
    metrics:
      - accuracy: 0.85
      - latency_p99: 5000ms
  - name: agent_coordination
    frequency: daily
    checks:
      - proper_routing
      - no_infinite_loops
      - successful_handoffs

14. Migration Strategy and Adoption

14.1 For Teams New to Agent Development

Start with single-agent systems using OpenAI SDK
Add PHR for prompt management
Introduce Dapr for state management
Scale to multi-agent with MCP/A2A
Add comprehensive evals and monitoring

14.2 For Teams with Existing Agent Systems

Document current architecture in AHR
Create specs for existing agents
Add PHR for prompt versioning
Integrate evaluation pipelines
Migrate to containerized deployment
Adopt Dapr/Ray for better orchestration

14.3 Training and Enablement

Workshops on agent design patterns
Hands-on labs with OpenAI SDK
Dapr/K8s training for DevOps teams
Prompt engineering best practices
Evaluation design workshops

15. Conclusion

SDD+ represents a comprehensive methodology for building modern distributed multi-agent systems. By combining artifact-driven development with production-ready patterns for agent orchestration, it addresses the full lifecycle of agent system development—from specification through deployment and operation. The integration of OpenAI Agents SDK, MCP, A2A protocols, and cloud-native technologies like Docker, Kubernetes, Dapr, and Ray provides teams with a complete toolkit for building scalable, maintainable, and auditable agent systems.

The dual focus on rigorous artifact management and practical multi-agent patterns ensures that teams can move fast while maintaining quality, safety, and compliance. As agent systems become increasingly central to modern applications, SDD+ provides the framework necessary to build them with confidence.

16. Glossary & Abbreviations

Core SDD+ Terms

SDD+: Spec-Driven Development Plus – methodology combining artifact-driven development with multi-agent system patterns
Spec: Specification – precise description of agent behavior and system requirements
AHR: Architecture History Record – versioned log of architecture decisions for distributed systems
PHR: Prompt History Record – versioned history of prompts across all agents
Eval: Evaluation – automated tests measuring agent and system quality

Multi-Agent Technologies

OpenAI Agents SDK: Framework for building AI agents with tool use and memory
MCP: Model Context Protocol – standardized context sharing between agents
A2A: Agent-to-Agent – communication protocols for agent collaboration
Dapr: Distributed Application Runtime – provides Actors and Workflows for agent orchestration
Ray: Distributed computing framework for parallel agent execution

Infrastructure Terms

Docker: Container platform for packaging agents
Kubernetes (K8s): Container orchestration for deploying agent fleets
CI/CD: Continuous Integration/Delivery – automated pipelines for agent deployment
Helm: Package manager for Kubernetes applications

Agent Patterns

Supervisor Pattern: Coordinator agent managing specialist agents
Pipeline Pattern: Sequential agent processing
Consensus Pattern: Multi-agent voting mechanisms
Actor Model: Stateful, single-threaded agent execution

Development Practices

TDD: Test-Driven Development – writing tests before agent implementation
BDD: Behavior-Driven Development – specification through scenarios
SSOT: Single Source of Truth – authoritative artifact repository
ADR: Architecture Decision Record – formal architecture decision documentation

End of document.

SDD Concepts Vibe Coding in Production

SDD+ Concepts