Runtime Architecture
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Understanding the Symbi runtime system architecture and core components.
Table of contents
- Overview
- System Architecture
- Core Components
- Multi-Tier Security
- Communication System
- Context & Knowledge Systems
- MCP Integration
- Policy Enforcement
- Audit and Compliance
- Performance Characteristics
- Configuration
- Monitoring and Observability
- Deployment
- Development and Testing
- Next Steps
Overview
The Symbi runtime system provides a secure, scalable, and policy-aware execution environment for autonomous agents. Built on Rust for performance and safety, it implements a multi-tier security model with comprehensive audit capabilities.
Core Principles
- Security by Default: Every operation is subject to policy enforcement
- Zero Trust: All components and communications are verified
- Complete Auditability: Every action is logged with cryptographic integrity
- Policy-Driven: Declarative policies control all system behavior
- High Performance: Rust-native implementation for production workloads
System Architecture
graph TB
subgraph "Runtime Core"
ARS[Agent Runtime Scheduler]
ALC[Agent Lifecycle Controller]
ARM[Agent Resource Manager]
ACB[Agent Communication Bus]
AEH[Agent Error Handler]
end
subgraph "Context & Knowledge"
ACM[Agent Context Manager]
VDB[Vector Database]
RAG[RAG Engine]
KB[Knowledge Base]
end
subgraph "Security & Policy"
PE[Policy Engine]
AT[Audit Trail]
SO[Sandbox Orchestrator]
CRYPTO[Crypto Operations]
end
subgraph "External Integration"
MCP[MCP Client]
TV[Tool Verification]
API[HTTP API]
end
subgraph "Sandbox Tiers"
T1[Tier 1: Docker]
T2[Tier 2: gVisor]
end
ARS --> ACM
ARS --> PE
ALC --> SO
ACB --> CRYPTO
ACM --> VDB
ACM --> RAG
SO --> T1
SO --> T2
MCP --> TV
PE --> AT
Core Components
Agent Runtime Scheduler
The central orchestrator responsible for managing agent execution with real task execution and graceful shutdown capabilities.
Key Responsibilities:
- Task Scheduling: Priority-based scheduling with resource awareness
- Real Task Execution: Actual process spawning and monitoring with comprehensive metrics
- Load Balancing: Distribution across available resources
- Resource Allocation: Memory, CPU, and I/O assignment
- Policy Coordination: Integration with policy enforcement
- Graceful Shutdown: Coordinated shutdown with resource cleanup
Execution Modes:
- Ephemeral: Run-once tasks that terminate after completion
- Persistent: Long-running agents with continuous monitoring
- Scheduled: Interval-based execution with automatic rescheduling
- Event-Driven: Triggered execution based on system events
Performance Characteristics:
- Support for 10,000+ concurrent agents
- Sub-millisecond scheduling decisions
- Priority-based preemption
- Resource-aware placement
- Real-time process monitoring and health checks
- Graceful shutdown with 30-second timeout before force termination
Real Task Execution Features:
- Process spawning with secure execution environments
- Resource monitoring (memory, CPU usage) every 5 seconds
- Task timeout enforcement with configurable limits
- Comprehensive execution metrics and statistics
- Process health monitoring and automatic failure detection
Graceful Shutdown Process:
- Stop New Tasks: Prevent new agent scheduling
- Graceful Termination: Attempt graceful shutdown of running agents (30s timeout)
- Force Termination: Force-kill remaining processes if needed
- Metrics Flush: Save performance and usage statistics
- Resource Cleanup: Release allocated resources and cleanup state
- Queue Cleanup: Clear pending agent queue
pub struct AgentScheduler {
priority_queues: Vec<PriorityQueue<AgentTask>>,
resource_pool: ResourcePool,
policy_engine: Arc<PolicyEngine>,
load_balancer: LoadBalancer,
task_manager: Arc<TaskManager>,
}
impl AgentScheduler {
pub async fn schedule_agent(&self, config: AgentConfig) -> Result<AgentId>;
pub async fn shutdown_agent(&self, agent_id: AgentId) -> Result<()>;
pub async fn get_system_status(&self) -> SystemStatus;
pub async fn shutdown(&self) -> Result<()>;
}
Agent Lifecycle Controller
Manages the complete lifecycle of agents from initialization to termination.
Lifecycle States:
- Initializing: Parsing DSL and validating configuration
- Ready: Waiting for task assignment
- Running: Actively executing tasks
- Suspended: Paused due to policy violation or resource constraints
- Terminated: Gracefully shutdown or forcibly stopped
stateDiagram-v2
[*] --> Initializing
Initializing --> Ready: Valid Config
Initializing --> Failed: Invalid Config
Ready --> Running: Task Assigned
Running --> Suspended: Policy Violation
Running --> Ready: Task Complete
Suspended --> Running: Policy Cleared
Suspended --> Terminated: Manual Override
Ready --> Terminated: Shutdown
Failed --> Terminated
Terminated --> [*]
Resource Management
Resource Types Managed:
- Memory: Heap allocation with limits and monitoring
- CPU: Core allocation and utilization tracking
- Disk I/O: Read/write bandwidth limits
- Network I/O: Bandwidth and connection limits
- Execution Time: Timeout enforcement
Resource Allocation Strategies:
- First Fit: Fastest allocation for low-latency scenarios
- Best Fit: Optimal utilization for resource efficiency
- Priority-Based: Guarantee resources for critical agents
pub struct ResourceLimits {
pub memory_mb: usize,
pub cpu_cores: f32,
pub disk_io_mbps: usize,
pub network_io_mbps: usize,
pub execution_timeout: Duration,
}
Multi-Tier Security
Sandbox Architecture
The runtime implements two security tiers based on operation risk:
Tier 1: Docker Isolation
Use Case: Low-risk operations, development tasks
- Container-based isolation
- Resource limits and capability dropping
- Network isolation and read-only filesystems
- Suitable for trusted code with minimal security requirements
Tier 2: gVisor Isolation
Use Case: Standard production tasks, data processing
- User-space kernel with system call interception
- Memory protection and I/O virtualization
- Enhanced security with minimal performance impact
- Default tier for most agent operations
Note: Additional isolation tiers are available in Enterprise editions for maximum security requirements.
Risk Assessment
The system automatically determines the appropriate security tier:
pub fn assess_security_tier(agent_config: &AgentConfig) -> SecurityTier {
let risk_factors = RiskAssessment {
data_sensitivity: assess_data_sensitivity(&agent_config.inputs),
code_trust_level: assess_code_trust(&agent_config.source),
network_access: agent_config.requires_network,
file_system_access: agent_config.requires_filesystem,
external_integrations: !agent_config.external_apis.is_empty(),
};
match calculate_risk_score(risk_factors) {
score if score < 0.5 => SecurityTier::Tier1,
_ => SecurityTier::Tier2,
}
}
Communication System
Message Types
The runtime supports multiple communication patterns:
Direct Messaging: Point-to-point communication with delivery guarantees
let response = agent_bus.send_message(
target_agent_id,
SecureMessage::new(payload)
).await?;
Publish-Subscribe: Topic-based event distribution
agent_bus.publish("data_processing.completed", event_data).await?;
agent_bus.subscribe("security.alerts", alert_handler).await?;
Request-Response: Synchronous communication with timeout
let result = agent_bus.request(
target_agent,
request_payload,
timeout_duration
).await?;
Security Features
Message Encryption: AES-256-GCM for payload protection Digital Signatures: Ed25519 signatures for authenticity Message Routing: Policy-based routing controls Rate Limiting: Per-agent message rate enforcement
pub struct SecureMessage {
pub id: MessageId,
pub sender: AgentId,
pub recipient: Option<AgentId>,
pub encrypted_payload: Vec<u8>,
pub signature: Ed25519Signature,
pub timestamp: SystemTime,
}
Context & Knowledge Systems
Agent Context Manager
Provides sophisticated persistent memory and knowledge management for agents with comprehensive search capabilities and access control.
Context Types:
- Short-term Memory: Recent interactions and immediate context
- Long-term Memory: Persistent knowledge and learned patterns
- Working Memory: Active processing and temporary state
- Episodic Memory: Structured experiences with events and outcomes
- Semantic Memory: Concepts, relationships, and structured knowledge
- Shared Knowledge: Cross-agent knowledge sharing with access control
Advanced Search Modes:
- Keyword Search: Text-based search with relevance scoring
- Temporal Search: Time-range based queries with recency factors
- Similarity Search: Vector-based semantic similarity using embeddings
- Hybrid Search: Combined keyword and similarity search with weighted scoring
Access Control & Policy Integration:
- Policy Engine Integration: Connected to resource access policies
- Agent-Scoped Access: Isolated contexts per agent with secure boundaries
- Knowledge Sharing Controls: Granular permissions for cross-agent knowledge access
- Access Level Management: Public, Restricted, Confidential, and Secret classifications
Importance Calculation Algorithm:
- Multi-Factor Scoring: Base importance, access frequency, recency, and user feedback
- Memory Type Weighting: Different importance multipliers per memory type
- Age Decay: Exponential decay with configurable half-life per memory type
- Access Pattern Analysis: Logarithmic scaling for frequently accessed items
Context Archiving & Retention:
- Automatic Archiving: Policy-driven archiving of old memory items
- Retention Policies: Configurable retention periods per data type
- Compressed Storage: Gzip compression for archived data
- Incremental Cleanup: Background cleanup with retention statistics
Context Statistics & Monitoring:
- Memory Usage Tracking: Accurate byte-level memory calculations
- Retention Analytics: Items eligible for archiving and deletion
- Performance Metrics: Context retrieval latency and throughput
- Health Monitoring: Context manager health checks and status
Knowledge Search Fallback:
- Primary Vector Search: Semantic search via vector database when available
- Fallback Keyword Search: Text-based search when vector DB unavailable
- Relevance Scoring: Sophisticated scoring combining multiple factors
- Trust Score Calculation: Trust metrics for shared knowledge items
pub trait ContextManager {
async fn store_context(&self, agent_id: AgentId, context: AgentContext) -> Result<ContextId>;
async fn retrieve_context(&self, agent_id: AgentId, session_id: Option<SessionId>) -> Result<Option<AgentContext>>;
async fn query_context(&self, agent_id: AgentId, query: ContextQuery) -> Result<Vec<ContextItem>>;
async fn update_memory(&self, agent_id: AgentId, updates: Vec<MemoryUpdate>) -> Result<()>;
async fn search_knowledge(&self, agent_id: AgentId, query: &str, limit: usize) -> Result<Vec<KnowledgeItem>>;
async fn share_knowledge(&self, from_agent: AgentId, to_agent: AgentId, knowledge_id: KnowledgeId, access_level: AccessLevel) -> Result<()>;
async fn archive_context(&self, agent_id: AgentId, before: SystemTime) -> Result<u32>;
async fn get_context_stats(&self, agent_id: AgentId) -> Result<ContextStats>;
async fn shutdown(&self) -> Result<()>;
}
RAG Engine Integration
RAG Pipeline:
- Query Analysis: Understanding agent information needs
- Vector Search: Semantic similarity search in knowledge base
- Document Retrieval: Fetching relevant knowledge documents
- Context Ranking: Relevance scoring and filtering
- Response Generation: Context-augmented response synthesis
Performance Targets:
- Context retrieval: <50ms average
- Vector search: <100ms for 1M+ embeddings
- RAG pipeline: <500ms end-to-end
Vector Database
Supported Operations:
- Semantic Search: Similarity-based document retrieval
- Metadata Filtering: Constraint-based search refinement
- Batch Operations: Efficient bulk operations
- Real-time Updates: Dynamic knowledge base updates
Integration with Qdrant:
pub struct VectorConfig {
pub dimension: usize, // 1536 for OpenAI embeddings
pub distance_metric: DistanceMetric::Cosine,
pub index_type: IndexType::HNSW,
pub ef_construct: 200,
pub m: 16,
}
MCP Integration
Model Context Protocol Client
Enables agents to access external tools and resources securely.
Core Capabilities:
- Server Discovery: Automatic discovery of available MCP servers
- Tool Management: Dynamic tool discovery and invocation
- Resource Access: Secure access to external data sources
- Protocol Handling: Full MCP specification compliance
Tool Discovery Process
sequenceDiagram
participant Agent
participant MCP as MCP Client
participant Server as MCP Server
participant Verifier as Tool Verifier
Agent->>MCP: Request Tools
MCP->>Server: Connect & List Tools
Server-->>MCP: Tool Definitions
MCP->>Verifier: Verify Tool Schemas
Verifier-->>MCP: Verification Results
MCP-->>Agent: Verified Tools
Agent->>MCP: Invoke Tool
MCP->>Server: Tool Invocation
Server-->>MCP: Tool Response
MCP-->>Agent: Verified Response
Tool Verification with SchemaPin
Verification Process:
- Schema Discovery: Retrieve tool schema from MCP server
- Signature Verification: Verify cryptographic signature
- Trust-On-First-Use: Pin trusted keys for future verification
- Policy Enforcement: Apply tool usage policies
- Audit Logging: Log all tool interactions
pub struct ToolVerifier {
key_store: SchemaPinKeyStore,
policy_engine: Arc<PolicyEngine>,
audit_logger: AuditLogger,
}
impl ToolVerifier {
pub async fn verify_tool(&self, tool: &MCPTool) -> VerificationResult;
pub async fn enforce_policies(&self, agent_id: AgentId, tool: &MCPTool) -> PolicyResult;
}
Policy Enforcement
Policy Engine Architecture
Policy Types:
- Access Control: Who can access what resources
- Data Flow: How data moves through the system
- Resource Usage: Limits on computational resources
- Audit Requirements: What must be logged and how
Policy Evaluation:
pub enum PolicyDecision {
Allow,
Deny { reason: String },
AllowWithConditions { conditions: Vec<PolicyCondition> },
}
pub trait PolicyEngine {
async fn evaluate_policy(&self, context: PolicyContext, action: Action) -> PolicyDecision;
async fn register_policy(&self, policy: Policy) -> Result<PolicyId>;
}
Real-time Enforcement
Enforcement Points:
- Agent creation and configuration
- Message sending and routing
- Resource allocation requests
- External tool invocation
- Data access operations
Performance:
- Policy evaluation: <1ms per decision
- Batch evaluation: 10,000+ decisions per second
- Real-time updates: Policy changes propagated instantly
Audit and Compliance
Cryptographic Audit Trail
Audit Event Structure:
pub struct AuditEvent {
pub event_id: Uuid,
pub timestamp: SystemTime,
pub agent_id: AgentId,
pub event_type: AuditEventType,
pub details: AuditDetails,
pub signature: Ed25519Signature,
pub chain_hash: Hash,
}
Integrity Guarantees:
- Digital Signatures: Ed25519 signatures on all events
- Hash Chaining: Events linked in immutable chain
- Timestamp Verification: Cryptographic timestamps
- Batch Verification: Efficient bulk verification
Compliance Features
Regulatory Support:
- HIPAA: Healthcare data protection compliance
- GDPR: European data protection requirements
- SOX: Financial audit trail requirements
- Custom: Configurable compliance frameworks
Audit Capabilities:
- Real-time event streaming
- Historical event querying
- Compliance report generation
- Integrity verification
Performance Characteristics
Scalability Metrics
Agent Management:
- Concurrent Agents: 10,000+ simultaneous agents
- Agent Startup: <1s for standard agents
- Memory Usage: 1-5MB per agent (varies by configuration)
- CPU Overhead: <5% system overhead for runtime
Communication Performance:
- Message Throughput: 100,000+ messages/second
- Message Latency: <10ms for local routing
- Encryption Overhead: <1ms per message
- Memory Pooling: Zero-allocation message passing
Context & Knowledge:
- Context Retrieval: <50ms average
- Vector Search: <100ms for 1M+ embeddings
- Knowledge Updates: Real-time with <10ms latency
- Storage Efficiency: Compressed embeddings with 80% size reduction
Resource Management
Memory Management:
- Allocation Strategy: Pool-based allocation for performance
- Garbage Collection: Incremental cleanup with bounded pause times
- Memory Protection: Guard pages and overflow detection
- Leak Prevention: Automatic cleanup and monitoring
CPU Utilization:
- Scheduler Overhead: <2% CPU for 10,000 agents
- Context Switching: Hardware-assisted virtual threads
- Load Balancing: Dynamic load distribution
- Priority Scheduling: Real-time and batch processing tiers
Configuration
Runtime Configuration
[runtime]
max_concurrent_agents = 10000
scheduler_threads = 8
message_buffer_size = 1048576
gc_interval_ms = 100
[security]
default_sandbox_tier = "gvisor"
enforce_policies = true
audit_enabled = true
crypto_provider = "ring"
[context]
vector_db_url = "http://localhost:6333"
embedding_dimension = 1536
context_cache_size = "1GB"
knowledge_retention_days = 365
[mcp]
discovery_enabled = true
tool_verification = "strict"
connection_timeout_s = 30
max_concurrent_connections = 100
Environment Variables
# Core runtime
export SYMBI_LOG_LEVEL=info
export SYMBI_RUNTIME_MODE=production
export SYMBI_CONFIG_PATH=/etc/symbi/config.toml
# Security
export SYMBI_CRYPTO_PROVIDER=ring
export SYMBI_AUDIT_STORAGE=/var/log/symbi/audit
# External dependencies
export QDRANT_URL=http://localhost:6333
export OPENAI_API_KEY=your_api_key_here
export MCP_SERVER_DISCOVERY=enabled
Monitoring and Observability
Metrics Collection
System Metrics:
- Agent count and resource usage
- Message throughput and latency
- Policy evaluation performance
- Security event rates
Business Metrics:
- Task completion rates
- Error frequencies by type
- Resource utilization efficiency
- Compliance audit results
Integration:
- Prometheus: Metrics collection and alerting
- Grafana: Visualization and dashboards
- Jaeger: Distributed tracing
- ELK Stack: Log aggregation and analysis
Health Monitoring
pub struct HealthStatus {
pub overall_status: SystemStatus,
pub component_health: HashMap<String, ComponentHealth>,
pub resource_utilization: ResourceUtilization,
pub recent_errors: Vec<ErrorSummary>,
}
pub async fn health_check() -> HealthStatus {
// Comprehensive system health assessment
}
Deployment
Container Deployment
FROM rust:1.88-slim as builder
WORKDIR /app
COPY . .
RUN cargo build --release --features production
FROM debian:bookworm-slim
RUN apt-get update && apt-get install -y ca-certificates
COPY --from=builder /app/target/release/symbi /usr/local/bin/
EXPOSE 8080
CMD ["symbi", "mcp", "--config", "/etc/symbi/config.toml"]
Kubernetes Deployment
apiVersion: apps/v1
kind: Deployment
metadata:
name: symbi-runtime
spec:
replicas: 3
selector:
matchLabels:
app: symbi-runtime
template:
metadata:
labels:
app: symbi-runtime
spec:
containers:
- name: runtime
image: ghcr.io/thirdkeyai/symbi:latest
ports:
- containerPort: 8080
env:
- name: SYMBI_RUNTIME_MODE
value: "production"
resources:
requests:
memory: "1Gi"
cpu: "500m"
limits:
memory: "4Gi"
cpu: "2"
Development and Testing
Local Development
# Start dependencies
docker-compose up -d qdrant redis postgres
# Run in development mode
RUST_LOG=debug cargo run --example full_system
# Run tests
cargo test --all --features test-utils
Integration Testing
The runtime includes comprehensive test suites:
- Unit Tests: Component-level testing
- Integration Tests: Cross-component testing
- Performance Tests: Load and stress testing
- Security Tests: Penetration and compliance testing
# Run all test suites
cargo test --workspace
# Run performance benchmarks
cargo bench
# Run security tests
cargo test --features security-tests
Next Steps
- Security Model - Deep dive into security implementation
- Contributing - Development and contribution guidelines
- API Reference - Complete API documentation
- Examples - Runtime examples and tutorials
The runtime architecture provides a robust foundation for building secure, scalable AI agents. Its modular design and comprehensive security model make it suitable for both development and production environments.