This document defines the complete agent swarm implementation for Tesseract, including all 40+ specialized AI agents organized into execution phases with parallel processing capabilities.
The agent swarm operates in 7 distinct phases, each optimized for specific parallelization patterns:
- Discovery Phase (Fully Parallel) - 12 agents
- Initial Analysis Phase (Parallel) - 5 agents
- Deep Analysis Phase (Parallel with Dependencies) - 15 agents
- Synthesis Phase (Sequential) - 5 agents
- Planning Phase (Parallel) - 5 agents
- Execution Phase (Parallel) - 6 agents
- Validation Phase (Parallel) - 4 agents
Total: 52 Specialized Agents
- FileSystemScout - Maps directory structure, file types, sizes
- GitHistoryScout - Analyzes commit history, author patterns, change frequency
- DependencyScout - Traces package dependencies, version conflicts
- TestScout - Locates test suites, calculates coverage mapping
- ConfigurationScout - Finds config files, environment settings
- BuildSystemScout - Identifies build tools, compilation flags
- SecurityConfigScout - Security configurations, certificates, policies
- ContainerScout - Container configurations, Dockerfiles, orchestration
- DocumentationScout - Finds documentation, README files, wikis
- DatabaseSchemaScout - Database migrations, schemas, data models
- EnvironmentScout - Environment profiles, deployment configs
- LicenseScout - License files, compliance requirements
- StaticCodeAnalyst - AST analysis, syntax validation
- StylePatternAnalyst - Coding patterns, naming conventions
- ComplexityAnalyst - ✅ IMPLEMENTED - Cyclomatic complexity, code hotspots
- LicenseAnalyst - License compatibility, legal compliance
- LocalizationAnalyst - Internationalization patterns, text extraction
- SecurityVulnAnalyst - ✅ IMPLEMENTED - Vulnerability detection, security patterns
- PerformanceAnalyst - ✅ IMPLEMENTED - Performance bottlenecks, optimization opportunities
- DatabaseAnalyst - Database optimization, query performance
- CacheAnalyst - Caching strategies, cache invalidation patterns
- ArchitectureAnalyst - Design patterns, architectural decisions
- BusinessLogicAnalyst - Business rules, domain logic extraction
- DataFlowAnalyst - Data flow tracking, state management
- APIContractAnalyst - API schemas, contract validation
- EventSystemAnalyst - Event architectures, message patterns
- ComplianceAnalyst - Regulatory compliance, audit requirements
- TeamDynamicsAnalyst - Collaboration patterns, code ownership
- TechnicalDebtPrioritizer - Debt assessment, remediation prioritization
- IntegrationAnalyst - Service integrations, external dependencies
- MonitoringAnalyst - Observability patterns, logging strategies
- ErrorHandlingAnalyst - Error patterns, exception handling strategies
- KnowledgeGraphBuilder - ✅ IMPLEMENTED - Primary graph construction, node creation
- RelationshipMapper - Entity relationships, dependency mapping
- MetadataEnricher - Property enrichment, metadata addition
- QueryOptimizer - Index optimization, query performance
- PatternSynthesizer - ✅ IMPLEMENTED - Cross-cutting pattern recognition
- InsightGenerator - ✅ IMPLEMENTED - Actionable insights from analysis data
- RefactoringPlanner - ✅ IMPLEMENTED - Change strategies, impact analysis
- MigrationPlanner - ✅ IMPLEMENTED - Version migrations, compatibility planning
- TestStrategyPlanner - ✅ IMPLEMENTED - Test generation strategies, coverage planning
- SecurityRemediationPlanner - Security fix prioritization, remediation plans
- PerformanceOptimizationPlanner - Performance improvement strategies
- CodeRefactoringExecutor - ✅ IMPLEMENTED - Apply refactoring changes with safety checks
- TestGenerationExecutor - ✅ IMPLEMENTED - Generate and execute comprehensive test suites
- DocumentationGenerator - ✅ IMPLEMENTED - Generate/update documentation
- ConfigurationTransformer - Update configuration files
- SchemaTransformer - Database schema migrations
- ContainerTransformer - Container configuration updates
- TestRunner - Execute test suites, validate changes
- RegressionDetector - Identify breaking changes, compatibility issues
- ComplianceChecker - Verify regulatory compliance, security standards
- PerformanceValidator - Validate performance improvements, benchmarking
- DarwinGodelAudit - Meta-learning system, agent performance tracking
- PromptEvolutionAgent - Prompt optimization, template evolution
- LearningPatternAgent - Pattern discovery, knowledge extraction
- StrategyOptimizerAgent - Execution strategy optimization
- ComplexityAnalyst (
backend/app/agents/definitions/complexity_analyzer.py
)
- SecurityVulnAnalyst (
backend/app/agents/definitions/security_scanner.py
)
- PerformanceAnalyst (
backend/app/agents/definitions/performance_analyzer.py
)
- DocumentationGenerator (
backend/app/agents/definitions/documentation_generator.py
)
- KnowledgeGraphBuilder (
backend/app/agents/synthesis/knowledge_graph_builder.py
)
- PatternSynthesizer (
backend/app/agents/synthesis/pattern_synthesizer.py
)
- InsightGenerator (
backend/app/agents/synthesis/insight_generator.py
)
- RefactoringPlanner (
backend/app/agents/planning/refactoring_planner.py
)
- MigrationPlanner (
backend/app/agents/planning/migration_planner.py
)
- TestStrategyPlanner (
backend/app/agents/planning/testing_strategy_planner.py
)
- CodeRefactoringExecutor (
backend/app/agents/execution/code_refactoring_executor.py
)
- TestGenerationExecutor (
backend/app/agents/execution/test_generation_executor.py
)
- FileSystemScout - Foundation for all other agents
- GitHistoryScout - Essential for author analysis
- DependencyScout - Critical for impact analysis
- TestScout - Required for coverage mapping
- KnowledgeGraphBuilder - Central data synthesis
- StaticCodeAnalyst - Core AST integration
- StylePatternAnalyst - Author fingerprinting
- RelationshipMapper - Entity linking
- ArchitectureAnalyst - Design pattern detection
- BusinessLogicAnalyst - Business rule extraction
- TechnicalDebtPrioritizer - Debt assessment
- ConfigurationScout - Environment mapping
- AgentOrchestrator - Phase-based execution
- DarwinGodelAudit - Meta-learning system
- RefactoringPlanner - Change coordination
- TestGenerator - Test automation
from abc import ABC, abstractmethod
from typing import Dict, Any, List, Optional
from dataclasses import dataclass
from enum import Enum
import asyncio
import logging
import time
class AgentCapability(Enum):
DISCOVERY = "discovery"
ANALYSIS = "analysis"
SYNTHESIS = "synthesis"
PLANNING = "planning"
EXECUTION = "execution"
VALIDATION = "validation"
META_LEARNING = "meta_learning"
@dataclass
class AgentConfig:
max_concurrent_tasks: int = 5
timeout_seconds: int = 300
retry_attempts: int = 3
resource_limits: Dict[str, Any] = None
capabilities: List[AgentCapability] = None
class AgentRole(ABC):
def __init__(self, agent_id: str, config: AgentConfig):
self.agent_id = agent_id
self.config = config
self.role = self.__class__.__name__
self.logger = logging.getLogger(f"agent.{self.role}")
self.metrics = AgentMetrics()
@abstractmethod
async def execute_task(self, task: AgentTask) -> AgentResult:
pass
@abstractmethod
def get_capabilities(self) -> List[AgentCapability]:
pass
- Minimum 80% test coverage for all agents
- Test structure:
tests/agents/test_{agent_name}.py
- Mock external dependencies (LLM calls, file system, databases)
- Performance benchmarks for critical paths
- Maximum 750 lines per agent file - Large agents split into utility modules
- ruff linting with zero errors tolerance
- bandit security scanning with zero vulnerabilities
- Type hints with full typing coverage
- Comprehensive docstrings for all public methods
- Modular Architecture: Large agents split into focused utility classes
- Security First: All subprocess calls and random generators marked as safe
- Error Handling: Comprehensive exception handling and logging
- Performance: Memory usage tracking and execution time monitoring
- Testing: Full test suites with fixtures and mock data
- Comprehensive error handling with retry logic
- Structured logging with correlation IDs
- Performance metrics for Darwin Gödel evolution
- Resource usage tracking
- Health check endpoints
class SwarmOrchestrator:
async def execute_phase(self, phase: ExecutionPhase, agents: List[AgentRole]):
if phase.parallel_mode == ParallelMode.FULLY_PARALLEL:
return await self._execute_parallel(agents, phase.max_concurrent)
elif phase.parallel_mode == ParallelMode.SEQUENTIAL:
return await self._execute_sequential(agents)
else: # DEPENDENCY_AWARE
return await self._execute_dependency_aware(agents)
- CPU/Memory allocation per agent type
- Container resource limits
- Queue management for resource-intensive tasks
- Dynamic scaling based on workload
- Redis pub/sub for inter-agent communication
- Shared context via Redis for dependency coordination
- Event-driven architecture for phase transitions
- Implement core discovery agents (FileSystemScout, GitHistoryScout, DependencyScout, TestScout)
- Set up agent testing framework
- Create base orchestration system
- Implement KnowledgeGraphBuilder and RelationshipMapper
- Add StaticCodeAnalyst and StylePatternAnalyst
- Integrate with Neo4j knowledge graph
- Implement architecture and business logic analysts
- Add technical debt prioritization
- Create configuration and environment discovery
- Complete agent orchestrator with phase management
- Implement Darwin Gödel meta-learning system
- Add planning and execution agents
- Implement validation agents
- Performance optimization
- Comprehensive testing and security scanning
- 80%+ test coverage for all agent implementations
- 0 security vulnerabilities in bandit scans ✅ ACHIEVED for all implemented agents
- 0 linting errors in ruff/flake8 checks ✅ ACHIEVED for all implemented agents
- Maximum 750 lines per agent file ✅ ACHIEVED - Large files refactored into modules
- Discovery phase: <5 minutes for 100k LOC
- Analysis phase: <15 minutes for 100k LOC
- Synthesis phase: <10 minutes
- Total execution: <45 minutes for 1M LOC
- 95%+ agent task completion rate
- <5% false positive rate for suggestions
- 90%+ pattern recognition accuracy
- 80%+ developer approval for generated changes
- Parser Service (Rust): AST analysis, code metrics
- Container Manager (Go): Execution environments
- Knowledge Graph (Neo4j): Data storage and relationships
- Message Queue (Redis): Task distribution and coordination
- gRPC: Internal service communication
- REST/GraphQL: External API access
- WebSocket: Real-time updates
- Protocol Buffers: Type-safe message passing
- Containerized execution environments
- Resource limit enforcement
- Network access controls
- Audit logging for all agent actions
- No sensitive data in agent logs
- Encrypted communication channels
- Access control for knowledge graph
- Secure credential management
This document serves as the definitive guide for implementing the complete Tesseract agent swarm with comprehensive test coverage, security validation, and performance optimization.