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Architecture
Haveapp1 edited this page Aug 22, 2025
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System design and component overview of the Agentwise multi-agent orchestration platform.
Agentwise is built on a modular, scalable architecture that enables efficient coordination of multiple AI agents. The system emphasizes performance, reliability, and extensibility while maintaining a clean separation of concerns.
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ User Layer │ │ CLI/API │ │ Web Dashboard │
│ │ │ │ │ │
│ • Commands │◄──►│ • REST API │◄──►│ • Real-time UI │
│ • Requests │ │ • WebSocket │ │ • Monitoring │
│ • Feedback │ │ • Authentication│ │ • Analytics │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │ │
└───────────────────────┼───────────────────────┘
│
┌─────────────────────────────────────────────────┐
│ Orchestrator Core │
│ │
│ • Task Analysis • Load Balancing │
│ • Agent Assignment • Dependency Management │
│ • Context Sharing • Progress Monitoring │
│ • Token Optimization • Error Recovery │
└─────────────────────────────────────────────────┘
│
┌────────────────────────────┼────────────────────────────┐
│ │ │
┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐
│Frontend │ │Backend │ │Database │ │Testing │ │DevOps │
│Agent │ │Agent │ │Agent │ │Agent │ │Agent │
│ │ │ │ │ │ │ │ │ │
│• React │ │• Node.js│ │• Schema │ │• Jest │ │• Docker │
│• Vue │ │• Python │ │• Queries│ │• Cypress│ │• K8s │
│• Angular│ │• Go │ │• Models │ │• E2E │ │• CI/CD │
└─────────┘ └─────────┘ └─────────┘ └─────────┘ └─────────┘
│ │ │ │ │
└──────────────┼──────────────┼──────────────┼──────────────┘
│ │ │
┌─────────────────────────────────────────────────┐
│ MCP Integration Layer │
│ │
│ • Server Discovery • Protocol Handling │
│ • Tool Registration • Resource Management │
│ • Context Bridging • Extension Support │
└─────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────┐
│ Infrastructure │
│ │
│ • Configuration • Logging & Monitoring │
│ • State Management • Caching & Storage │
│ • Security Layer • Performance Metrics │
└─────────────────────────────────────────────────┘
The central orchestrator coordinates all system operations:
// Orchestrator responsibilities
{
"core_functions": [
"request_analysis",
"task_decomposition",
"agent_assignment",
"execution_monitoring",
"result_integration",
"error_handling"
],
"optimization": [
"token_budget_management",
"context_sharing",
"parallel_execution",
"load_balancing"
]
}// Main orchestrator structure
class OrchestrationEngine {
constructor() {
this.taskAnalyzer = new TaskAnalyzer();
this.agentManager = new AgentManager();
this.contextManager = new ContextManager();
this.tokenOptimizer = new TokenOptimizer();
this.progressMonitor = new ProgressMonitor();
}
async processRequest(request) {
const analysis = await this.taskAnalyzer.analyze(request);
const tasks = await this.taskAnalyzer.decompose(analysis);
const assignments = await this.agentManager.assignTasks(tasks);
return await this.executeParallel(assignments);
}
}Handles the lifecycle and coordination of specialized agents:
// Agent management architecture
{
"agent_lifecycle": {
"discovery": "Auto-detect available agents",
"registration": "Register capabilities and tools",
"allocation": "Assign to task queues",
"monitoring": "Track performance and health",
"scaling": "Dynamic pool management"
},
"agent_types": {
"built_in": ["frontend", "backend", "database", "testing", "devops"],
"custom": ["domain-specific agents", "user-created specialists"],
"dynamic": ["auto-generated based on project needs"]
}
}// Inter-agent communication
class AgentCommunication {
constructor() {
this.messageQueue = new MessageQueue();
this.contextBridge = new ContextBridge();
this.eventBus = new EventBus();
}
async broadcastContext(context, agents) {
const compressedContext = await this.contextBridge.compress(context);
for (const agent of agents) {
await this.messageQueue.send(agent.id, {
type: 'context_update',
data: compressedContext,
timestamp: Date.now()
});
}
}
}Intelligent task allocation and dependency management:
// Task distribution architecture
{
"components": {
"task_analyzer": "Breaks down complex requests",
"dependency_resolver": "Manages task interdependencies",
"load_balancer": "Distributes work optimally",
"queue_manager": "Handles task queues per agent",
"progress_tracker": "Monitors execution status"
},
"strategies": {
"round_robin": "Even distribution across agents",
"weighted": "Based on agent capabilities",
"priority": "Critical tasks first",
"dependency": "Respects task dependencies"
}
}Efficient context sharing and optimization:
// Context management system
class ContextManager {
constructor() {
this.sharedPool = new SharedContextPool();
this.compression = new ContextCompression();
this.versioning = new ContextVersioning();
}
async updateSharedContext(agentId, updates) {
const compressed = await this.compression.compress(updates);
const version = await this.versioning.increment();
await this.sharedPool.update({
version,
updates: compressed,
source: agentId,
timestamp: Date.now()
});
// Notify other agents of context change
this.broadcastContextUpdate(version, agentId);
}
}User Request → Analysis → Decomposition → Assignment → Execution → Integration → Response
│ │ │ │ │ │ │
│ │ │ │ │ │ │
Parse Extract Create Match Execute Combine Format
Intent Requirements Tasks Capabilities Parallel Results Response
│ │ │ │ │ │ │
└─────────────┴───────────┴────────────┴────────────┴─────────────┴───────────┘
Context Sharing & Optimization
// Data architecture
{
"layers": {
"memory": {
"purpose": "Active task state, agent queues",
"technology": "Redis",
"retention": "Session-based"
},
"cache": {
"purpose": "Compiled templates, optimization data",
"technology": "Redis + File System",
"retention": "Configurable TTL"
},
"persistent": {
"purpose": "Configuration, metrics, history",
"technology": "SQLite/PostgreSQL",
"retention": "Permanent"
},
"artifacts": {
"purpose": "Generated code, project files",
"technology": "File System",
"retention": "User-controlled"
}
}
}// MCP integration structure
{
"discovery": {
"auto_scan": "Scan .claude/mcp_servers directory",
"config_parse": "Read claude_desktop_config.json",
"capability_detect": "Identify available tools and resources"
},
"server_management": {
"lifecycle": "Start/stop/restart MCP servers",
"health_check": "Monitor server availability",
"load_balancing": "Distribute requests across instances"
},
"tool_integration": {
"registration": "Register tools with appropriate agents",
"invocation": "Route tool calls to correct servers",
"result_handling": "Process and integrate tool outputs"
}
}// MCP server discovery
class MCPDiscovery {
async discoverServers() {
const configServers = await this.loadFromConfig();
const directoryServers = await this.scanDirectory();
const registeredServers = await this.loadRegistered();
return {
...configServers,
...directoryServers,
...registeredServers
};
}
async validateServer(server) {
try {
const client = new MCPClient(server);
const capabilities = await client.getCapabilities();
return { valid: true, capabilities };
} catch (error) {
return { valid: false, error: error.message };
}
}
}{
"authentication": {
"api_keys": "Bearer token authentication",
"jwt": "JSON Web Tokens for session management",
"oauth": "Third-party authentication support"
},
"authorization": {
"rbac": "Role-based access control",
"resource_scoping": "Fine-grained permission control",
"agent_isolation": "Sandboxed agent execution"
},
"data_protection": {
"encryption": "TLS for transport, AES for storage",
"sanitization": "Input validation and output filtering",
"audit_logging": "Comprehensive security event logging"
}
}// Agent isolation architecture
class AgentSandbox {
constructor(agentId) {
this.containerId = `agent-${agentId}-${Date.now()}`;
this.resourceLimits = this.getResourceLimits(agentId);
this.permissions = this.getPermissions(agentId);
}
async createSandbox() {
const container = await this.docker.createContainer({
image: 'agentwise/agent-runtime',
name: this.containerId,
resources: this.resourceLimits,
securityOptions: this.permissions,
networkMode: 'agentwise-network'
});
await container.start();
return container;
}
}{
"caching": {
"levels": ["memory", "redis", "disk"],
"strategies": ["LRU", "TTL", "size-based"],
"cache_keys": ["context", "templates", "results", "configurations"]
},
"parallelization": {
"agent_pool": "Multiple agents working simultaneously",
"task_pipeline": "Pipelined task execution",
"io_async": "Non-blocking I/O operations"
},
"resource_management": {
"connection_pooling": "Reuse database/API connections",
"memory_monitoring": "Track and limit memory usage",
"cpu_throttling": "Prevent system overload"
}
}// Load balancing implementation
class LoadBalancer {
constructor() {
this.strategies = {
'round-robin': new RoundRobinStrategy(),
'weighted': new WeightedStrategy(),
'least-connections': new LeastConnectionsStrategy(),
'resource-based': new ResourceBasedStrategy()
};
}
selectAgent(availableAgents, task, strategy = 'weighted') {
const balancer = this.strategies[strategy];
return balancer.select(availableAgents, task);
}
}{
"system_metrics": {
"cpu_usage": "Per-agent and system-wide CPU utilization",
"memory_usage": "Memory consumption and allocation",
"disk_io": "File system read/write operations",
"network_io": "API calls and data transfer"
},
"business_metrics": {
"task_completion_rate": "Percentage of successfully completed tasks",
"token_efficiency": "Tokens saved through optimization",
"agent_utilization": "How effectively agents are used",
"user_satisfaction": "Quality scores and feedback"
},
"custom_metrics": {
"project_types": "Performance by project category",
"agent_specialization": "Success rates by agent type",
"optimization_impact": "Before/after optimization metrics"
}
}// Health monitoring
class HealthMonitor {
constructor() {
this.checks = [
new AgentHealthCheck(),
new MCPServerCheck(),
new ResourceUtilizationCheck(),
new DatabaseConnectionCheck(),
new ExternalAPICheck()
];
}
async runHealthChecks() {
const results = await Promise.allSettled(
this.checks.map(check => check.run())
);
return {
status: this.calculateOverallStatus(results),
checks: results.map((result, i) => ({
name: this.checks[i].name,
status: result.status,
details: result.value || result.reason
}))
};
}
}# Multi-stage Dockerfile for Agentwise
FROM node:18-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
FROM node:18-alpine AS runtime
RUN addgroup -g 1001 -S agentwise && \
adduser -S agentwise -u 1001
WORKDIR /app
COPY --from=builder /app/node_modules ./node_modules
COPY --chown=agentwise:agentwise . .
USER agentwise
EXPOSE 3001
HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
CMD node healthcheck.js
CMD ["npm", "start"]# Kubernetes deployment configuration
apiVersion: apps/v1
kind: Deployment
metadata:
name: agentwise-orchestrator
spec:
replicas: 3
selector:
matchLabels:
app: agentwise-orchestrator
template:
metadata:
labels:
app: agentwise-orchestrator
spec:
containers:
- name: orchestrator
image: agentwise/orchestrator:latest
ports:
- containerPort: 3001
resources:
requests:
memory: "512Mi"
cpu: "250m"
limits:
memory: "1Gi"
cpu: "500m"
env:
- name: NODE_ENV
value: "production"
- name: REDIS_URL
valueFrom:
secretKeyRef:
name: agentwise-secrets
key: redis-url{
"scaling_dimensions": {
"orchestrator": "Multiple orchestrator instances with load balancing",
"agents": "Agent pool can scale independently",
"mcp_servers": "MCP servers can be distributed across nodes",
"storage": "Distributed storage for artifacts and cache"
},
"auto_scaling": {
"triggers": [
"queue_depth > threshold",
"response_time > target",
"cpu_usage > 80%",
"memory_usage > 85%"
],
"actions": [
"spawn_additional_agents",
"scale_orchestrator_replicas",
"increase_resource_limits"
]
}
}// Database architecture for scale
{
"sharding_strategy": {
"by_tenant": "Separate database per user/organization",
"by_project": "Shard based on project ID",
"by_time": "Time-based partitioning for metrics"
},
"read_replicas": {
"purpose": "Distribute read load across multiple replicas",
"consistency": "Eventually consistent for non-critical reads"
}
}// Circuit breaker implementation
class CircuitBreaker {
constructor(threshold = 5, timeout = 60000) {
this.failureThreshold = threshold;
this.timeout = timeout;
this.failureCount = 0;
this.state = 'CLOSED'; // CLOSED, OPEN, HALF_OPEN
this.nextAttempt = Date.now();
}
async execute(operation) {
if (this.state === 'OPEN') {
if (Date.now() < this.nextAttempt) {
throw new Error('Circuit breaker is OPEN');
}
this.state = 'HALF_OPEN';
}
try {
const result = await operation();
this.onSuccess();
return result;
} catch (error) {
this.onFailure();
throw error;
}
}
}// Exponential backoff retry
class RetryManager {
async executeWithRetry(operation, maxRetries = 3) {
let attempt = 0;
while (attempt < maxRetries) {
try {
return await operation();
} catch (error) {
attempt++;
if (attempt >= maxRetries) {
throw error;
}
const delay = Math.min(1000 * Math.pow(2, attempt), 30000);
await this.sleep(delay);
}
}
}
}// Configuration architecture
{
"configuration_sources": [
"environment_variables",
"configuration_files",
"command_line_arguments",
"runtime_overrides"
],
"validation": {
"schema_validation": "JSON schema validation",
"constraint_checking": "Business rule validation",
"dependency_verification": "Ensure all dependencies are met"
},
"hot_reloading": {
"supported_configs": ["logging", "rate_limits", "feature_flags"],
"mechanism": "File system watching + event emission"
}
}For more information, see Configuration, Agent System, or Performance Tuning.
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