This document outlines the comprehensive planning approach for building Deep Agents - sophisticated multi-agent AI systems that combine planning, specialized sub-agents, persistent memory, and coordinated intelligence to solve complex, real-world problems.
Key Innovation: Moving from simple tool-using chatbots to autonomous, collaborative agent ecosystems that can handle enterprise-level tasks with human-like planning and execution capabilities.
Create an autonomous multi-agent system that can:
- Plan complex workflows with multiple dependencies
- Spawn specialized sub-agents for domain-specific tasks
- Maintain persistent state across long-running operations
- Coordinate intelligently between agents and human stakeholders
- Task Completion Rate: >90% for complex multi-step workflows
- Agent Coordination Efficiency: <2 seconds inter-agent communication
- Memory Persistence: 100% state retention across sessions
- Scalability: Support 10+ concurrent sub-agents per workflow
Purpose: Central orchestration and workflow management
Core Capabilities:
- Task Decomposition: Break complex goals into executable subtasks
- Dependency Management: Handle task prerequisites and sequencing
- Resource Allocation: Assign appropriate sub-agents to tasks
- Progress Monitoring: Track completion status and handle failures
- Dynamic Replanning: Adapt to changing conditions or failures
Implementation Approach:
class PlanningTool:
def __init__(self):
self.task_graph = TaskGraph()
self.resource_manager = ResourceManager()
self.execution_monitor = ExecutionMonitor()
async def create_plan(self, objective: str) -> ExecutionPlan:
# Decompose objective into subtasks
subtasks = await self.decompose_objective(objective)
# Build dependency graph
plan = self.task_graph.build_execution_plan(subtasks)
# Assign resources and agents
plan = await self.resource_manager.assign_agents(plan)
return planKey Technologies:
- LangGraph: For workflow state management
- Task Graph Libraries: NetworkX for dependency modeling
- Event-Driven Architecture: For real-time coordination
Purpose: Domain-specific task execution with specialized expertise
Agent Types:
- Specialty: Information gathering and analysis
- Tools: Web search, document analysis, data extraction
- Output: Structured research reports and insights
- Specialty: Software development and technical tasks
- Tools: Code generation, testing, debugging, deployment
- Output: Working code, tests, documentation
- Specialty: Human interaction and content creation
- Tools: Email, messaging, document generation, presentations
- Output: Professional communications and deliverables
- Specialty: Data processing and business intelligence
- Tools: Statistical analysis, visualization, reporting
- Output: Charts, reports, recommendations
Implementation Pattern:
class BaseSubAgent:
def __init__(self, specialty: str, tools: List[Tool]):
self.specialty = specialty
self.tools = tools
self.memory = AgentMemory()
self.communication_channel = MessageChannel()
async def execute_task(self, task: Task) -> TaskResult:
# Load relevant context from memory
context = await self.memory.get_context(task)
# Execute using specialized tools
result = await self.process_with_tools(task, context)
# Store results and learnings
await self.memory.store_result(task, result)
return resultPurpose: Shared workspace and long-term memory management
Components:
- Document Storage: Structured file hierarchy for agent outputs
- Version Control: Track changes and maintain history
- Access Control: Manage permissions between agents
- Search & Indexing: Quick retrieval of relevant information
- Working Memory: Current task context and temporary data
- Long-term Memory: Persistent knowledge and learned patterns
- Shared Memory: Cross-agent communication and coordination
- External Memory: Integration with databases and APIs
Implementation Architecture:
class DeepAgentFileSystem:
def __init__(self):
self.virtual_fs = VirtualFileSystem()
self.memory_manager = MemoryManager()
self.search_index = SearchIndex()
async def store_document(self, path: str, content: Any, metadata: Dict):
# Store in virtual file system
await self.virtual_fs.write(path, content)
# Update memory structures
await self.memory_manager.index_content(content, metadata)
# Update search index
await self.search_index.add_document(path, content, metadata)
async def retrieve_context(self, query: str) -> List[Document]:
# Semantic search across all stored content
return await self.search_index.search(query)Purpose: Core behavioral guidelines and coordination protocols
Components:
- Mission Statement: Primary objectives and values
- Behavioral Guidelines: How agents should interact and behave
- Quality Standards: Expected output quality and formats
- Safety Protocols: Error handling and risk mitigation
- Communication Standards: How agents exchange information
- Escalation Procedures: When to involve humans or senior agents
- Conflict Resolution: Handling disagreements between agents
- Resource Sharing: Guidelines for tool and memory access
Example System Prompt Structure:
# Deep Agent System Prompt
## Mission
You are part of a sophisticated multi-agent system designed to solve complex problems through intelligent collaboration.
## Core Principles
1. **Collaboration First**: Always consider how your actions affect other agents
2. **Quality Focus**: Deliver high-quality, well-documented outputs
3. **Transparency**: Communicate your reasoning and progress clearly
4. **Adaptability**: Adjust your approach based on feedback and results
## Coordination Protocols
- Use structured communication formats for inter-agent messages
- Store all significant outputs in the shared file system
- Escalate to the Planning Tool for complex decisions
- Maintain detailed logs of your actions and reasoning
## Specialized Instructions
[Agent-specific instructions based on role and capabilities]Objective: Build core infrastructure and basic agent framework
Deliverables:
- Basic agent communication system
- Virtual file system implementation
- Simple planning tool prototype
- Core system prompt framework
Key Technologies:
- FastAPI for agent communication
- SQLite/PostgreSQL for persistent storage
- LangChain for LLM integration
- WebSocket for real-time updates
Objective: Develop specialized sub-agents with domain expertise
Deliverables:
- Research Agent with web search capabilities
- Code Agent with development tools
- Communication Agent for human interaction
- Analysis Agent for data processing
Integration Points:
- MCP (Model Context Protocol) for tool discovery
- LangGraph for agent workflow management
- Custom tool libraries for each agent type
Objective: Implement sophisticated planning and coordination
Deliverables:
- Advanced task decomposition algorithms
- Dynamic resource allocation system
- Failure recovery and replanning mechanisms
- Performance monitoring and optimization
Advanced Features:
- Machine learning for task optimization
- Predictive resource allocation
- Automated performance tuning
Objective: Scale, secure, and deploy the system
Deliverables:
- Security and access control systems
- Scalability and load balancing
- Monitoring and observability
- Documentation and training materials
- LangChain/LangGraph: Agent orchestration and workflow management
- FastAPI: High-performance API framework
- WebSocket: Real-time communication
- PostgreSQL: Persistent data storage
- Redis: Caching and session management
- OpenAI GPT-4: Primary language model
- Anthropic Claude: Alternative/specialized model
- Local Models: Ollama for cost-effective operations
- Embedding Models: For semantic search and similarity
- Docker: Containerization and deployment
- Kubernetes: Orchestration and scaling
- Monitoring: Prometheus + Grafana
- Logging: Structured logging with ELK stack
- 1 Senior AI Engineer: Architecture and agent design
- 2 Backend Engineers: API and infrastructure development
- 1 Frontend Engineer: User interface and monitoring dashboards
- 1 DevOps Engineer: Deployment and infrastructure management
- Cloud Computing: $500-2000 (depending on scale)
- AI Model APIs: $200-1000 (based on usage)
- Database & Storage: $100-500
- Monitoring & Tools: $100-300
- Total Development: 16 weeks
- MVP Delivery: 8 weeks
- Production Ready: 16 weeks
- Ongoing Maintenance: 20% of development effort
- Successfully decompose complex tasks into executable subtasks
- Coordinate multiple specialized agents simultaneously
- Maintain persistent state across long-running workflows
- Handle failures gracefully with automatic recovery
- Provide real-time progress updates and transparency
- Task completion rate >90% for complex workflows
- Inter-agent communication latency <2 seconds
- System uptime >99.5%
- Support for 10+ concurrent workflows
- Memory usage optimization for long-running processes
- Comprehensive test coverage >80%
- Detailed documentation for all components
- Security audit and compliance verification
- User acceptance testing with real-world scenarios
- Performance benchmarking and optimization
- Model Reliability: Mitigation through multiple model providers and fallbacks
- Scalability Challenges: Early load testing and horizontal scaling design
- Integration Complexity: Modular architecture with well-defined interfaces
- Data Consistency: ACID transactions and eventual consistency patterns
- Cost Overruns: Regular budget monitoring and cost optimization
- Timeline Delays: Agile development with regular milestone reviews
- User Adoption: Early user feedback and iterative improvement
- Competition: Focus on unique value proposition and rapid iteration
- Machine Learning Integration: Predictive task optimization
- Natural Language Planning: Voice and text-based workflow creation
- Multi-Modal Capabilities: Image, video, and audio processing
- External System Integration: CRM, ERP, and business tool connectivity
- Enterprise Deployment: Multi-tenant architecture
- Industry Specialization: Domain-specific agent libraries
- API Marketplace: Third-party agent and tool integration
- Cloud Service: SaaS offering for broader market
- Stakeholder Alignment: Review and approve this planning document
- Team Assembly: Recruit and onboard development team
- Technology Validation: Proof of concept for core components
- Development Kickoff: Begin Phase 1 implementation
- Regular Reviews: Weekly progress updates and milestone assessments
This planning document serves as the foundation for building sophisticated Deep Agent systems. Regular updates and refinements will be made based on development progress and stakeholder feedback.