DSL Guide
Master the Symbi DSL for building policy-aware, secure AI agents.
Table of contents
- Overview
- Language Syntax
- Metadata Blocks
- Agent Definitions
- Policy Definitions
- Type System
- Execution Context
- Built-in Functions
- Inter-Agent Communication
- Error Handling
- Advanced Features
- Best Practices
- Examples
- Next Steps
Overview
The Symbi DSL is a domain-specific language designed for creating autonomous, policy-aware agents. It combines traditional programming constructs with advanced security features, cryptographic operations, and declarative policy definitions.
Key Features
- Security-First Design: Built-in policy enforcement and audit capabilities
- Declarative Policies: Express security requirements as code
- Cryptographic Operations: Native support for encryption, signing, and proofs
- Inter-Agent Communication: Built-in messaging and collaboration patterns
- Type Safety: Strong typing with security-aware type annotations
Language Syntax
Basic Structure
Every Symbi program consists of optional metadata, imports, and agent definitions:
metadata {
version = "1.0.0"
author = "developer"
description = "Example agent"
}
import data_processing as dp;
import security_utils;
agent process_data(input: DataSet) -> Result {
// Agent implementation
}
Comments
// Single-line comment
/*
* Multi-line comment
* Supports markdown formatting
*/
Metadata Blocks
Metadata provides essential information about your agent:
metadata {
version = "1.2.0"
author = "ThirdKey Security Team"
description = "Healthcare data analysis agent with HIPAA compliance"
license = "Proprietary"
tags = ["healthcare", "hipaa", "analysis"]
min_runtime_version = "0.5.0"
dependencies = ["medical_nlp", "privacy_tools"]
}
Metadata Fields
Field | Type | Required | Description |
---|---|---|---|
version |
String | Yes | Semantic version of the agent |
author |
String | Yes | Agent author or organization |
description |
String | Yes | Brief description of agent functionality |
license |
String | No | License identifier |
tags |
Array[String] | No | Classification tags |
min_runtime_version |
String | No | Minimum required runtime version |
dependencies |
Array[String] | No | External dependencies |
Agent Definitions
Basic Agent Structure
agent agent_name(param1: Type1, param2: Type2) -> ReturnType {
capabilities = ["capability1", "capability2"]
policy policy_name {
// Policy rules
}
with configuration_options {
// Agent implementation
}
}
Agent Parameters
Support for various parameter types:
agent complex_agent(
// Basic types
name: String,
age: Integer,
active: Boolean,
// Optional parameters
email: Optional<String>,
// Complex types
data: Array<Record>,
config: Map<String, Value>,
// Security-aware types
sensitive_data: EncryptedData<PersonalInfo>,
credentials: SecureString
) -> ProcessingResult {
// Implementation
}
Capabilities Declaration
Declare what your agent can do:
agent data_processor(input: DataSet) -> Analysis {
capabilities = [
"data_analysis", // Core data processing
"statistical_modeling", // Advanced analytics
"report_generation", // Output formatting
"audit_logging" // Compliance tracking
]
// Implementation
}
Policy Definitions
Policies define security and compliance rules that are enforced at runtime.
Policy Structure
policy policy_name {
allow: action_list if condition
deny: action_list if condition
require: requirement_list
audit: audit_specification
conditions: {
field: value,
another_field: condition
}
}
Access Control Policies
policy medical_data_access {
allow: ["read", "analyze"] if user.role == "doctor"
allow: ["read"] if user.role == "nurse"
deny: ["export", "print"] if data.contains_pii == true
require: [
user.clearance >= "medical_professional",
session.mfa_verified == true,
audit_trail = true
]
}
Data Classification Policies
policy data_classification {
conditions: {
classification: "confidential",
retention_period: 7.years,
geographic_restriction: "EU",
encryption_required: true
}
allow: process(data) if data.anonymized == true
deny: store(data) if data.classification == "restricted"
audit: all_operations with digital_signature
}
Complex Policy Logic
policy dynamic_access_control {
allow: read(resource) if (
user.department == resource.owner_department ||
user.role == "administrator" ||
(user.role == "auditor" && current_time.business_hours)
)
deny: write(resource) if (
resource.locked == true ||
user.last_training < 30.days_ago ||
system.maintenance_mode == true
)
require: approval("supervisor") for operations on sensitive_data
}
Type System
Primitive Types
// Basic types
let name: String = "Alice";
let count: Integer = 42;
let rate: Float = 3.14;
let active: Boolean = true;
let data: Bytes = b"binary_data";
Collection Types
// Arrays
let numbers: Array<Integer> = [1, 2, 3, 4, 5];
let names: Array<String> = ["Alice", "Bob", "Charlie"];
// Maps
let config: Map<String, String> = {
"host": "localhost",
"port": "8080",
"ssl": "true"
};
// Sets
let unique_ids: Set<String> = {"id1", "id2", "id3"};
Security-Aware Types
// Encrypted types
let secret: EncryptedString = encrypt("sensitive_data", key);
let secure_number: EncryptedInteger = encrypt(42, key);
// Private data with differential privacy
let private_data: PrivateData<Float> = PrivateData::new(value, epsilon=1.0);
// Verifiable results with zero-knowledge proofs
let verified_result: VerifiableResult<Analysis> = VerifiableResult {
value: analysis,
proof: generate_proof(analysis),
signature: sign(analysis)
};
Custom Types
// Struct definitions
struct PersonalInfo {
name: String,
email: EncryptedString,
phone: Optional<String>,
birth_date: Date
}
// Enum definitions
enum SecurityLevel {
Public,
Internal,
Confidential,
Restricted
}
// Type aliases
type UserId = String;
type EncryptedPersonalInfo = EncryptedData<PersonalInfo>;
Execution Context
Configure how your agent executes with the with
clause:
Memory Management
agent persistent_agent(data: DataSet) -> Result {
with memory = "persistent", storage = "encrypted" {
// Agent state persists across sessions
store_knowledge(data);
return process_with_history(data);
}
}
agent ephemeral_agent(query: String) -> Answer {
with memory = "ephemeral", cleanup = "immediate" {
// Agent state is discarded after execution
return quick_answer(query);
}
}
Privacy Settings
agent privacy_preserving_agent(sensitive_data: PersonalInfo) -> Statistics {
with privacy = "differential", epsilon = 1.0 {
// Add differential privacy noise
let noisy_stats = compute_statistics(sensitive_data);
return add_privacy_noise(noisy_stats, epsilon);
}
}
Security Configuration
agent high_security_agent(classified_data: ClassifiedInfo) -> Report {
with
security = "maximum",
sandbox = "firecracker",
encryption = "homomorphic",
requires = "top_secret_clearance"
{
// High-security processing
return process_classified(classified_data);
}
}
Built-in Functions
Data Processing
// Validation functions
if (validate_input(data)) {
// Process valid data
}
// Data transformation
let cleaned_data = sanitize(raw_data);
let normalized = normalize(cleaned_data);
Cryptographic Operations
// Encryption/Decryption
let encrypted = encrypt(plaintext, public_key);
let decrypted = decrypt(ciphertext, private_key);
// Digital signatures
let signature = sign(message, private_key);
let valid = verify(message, signature, public_key);
// Zero-knowledge proofs
let proof = prove(statement);
let verified = verify_proof(proof, public_statement);
Audit and Logging
// Audit logging
audit_log("operation_started", {
"operation": "data_processing",
"user": user.id,
"timestamp": now()
});
// Security events
security_event("policy_violation", {
"policy": "data_access",
"user": user.id,
"resource": resource.id
});
Inter-Agent Communication
Direct Messaging
agent coordinator(task: Task) -> Result {
with communication = "secure" {
// Send task to specialized agent
let result = agent security_analyzer.analyze(task);
if (result.safe) {
let processed = agent data_processor.process(task);
return processed;
} else {
return reject("Security check failed");
}
}
}
Publish-Subscribe Pattern
agent event_publisher(event: Event) -> Confirmation {
with communication = "broadcast" {
// Broadcast event to all subscribers
broadcast(EventNotification {
type: event.type,
data: event.data,
timestamp: now()
});
return Confirmation { sent: true };
}
}
agent event_subscriber() -> Void {
with communication = "subscribe" {
// Subscribe to specific events
let events = subscribe(EventNotification);
for event in events {
process_event(event);
}
}
}
Secure Communication
agent secure_collaborator(request: SecureRequest) -> SecureResponse {
with
communication = "encrypted",
authentication = "mutual_tls"
{
// Establish secure channel
let channel = establish_secure_channel(request.source);
// Send encrypted response
let response = process_request(request);
return encrypt_response(response, channel.key);
}
}
Error Handling
Try-Catch Blocks
agent robust_processor(data: DataSet) -> Result {
try {
let validated = validate_data(data);
let processed = process_data(validated);
return Ok(processed);
} catch (ValidationError e) {
audit_log("validation_failed", e.details);
return Error("Invalid input data");
} catch (ProcessingError e) {
audit_log("processing_failed", e.details);
return Error("Processing failed");
}
}
Error Recovery
agent fault_tolerant_agent(input: Input) -> Result {
let max_retries = 3;
let retry_count = 0;
while (retry_count < max_retries) {
try {
return process_with_fallback(input);
} catch (TransientError e) {
retry_count += 1;
sleep(exponential_backoff(retry_count));
} catch (PermanentError e) {
return Error(e.message);
}
}
return Error("Max retries exceeded");
}
Advanced Features
Conditional Compilation
agent development_agent(data: DataSet) -> Result {
capabilities = ["development", "testing"]
#if debug {
debug_log("Processing data: " + data.summary);
}
#if feature.enhanced_security {
policy strict_security {
require: multi_factor_authentication
audit: all_operations with timestamps
}
}
// Implementation
}
Macros and Code Generation
// Define reusable policy template
macro secure_data_policy($classification: String) {
policy secure_access {
allow: read(data) if user.clearance >= $classification
deny: export(data) if data.contains_pii
audit: all_operations with signature
}
}
agent classified_processor(data: ClassifiedData) -> Report {
// Use the macro
secure_data_policy!("secret");
// Implementation
}
Integration with External Systems
agent api_integrator(request: APIRequest) -> APIResponse {
capabilities = ["api_access", "data_transformation"]
policy api_access {
allow: call(external_api) if api.rate_limit_ok
require: valid_api_key
audit: all_api_calls with response_codes
}
with
timeout = 30.seconds,
retry_policy = "exponential_backoff"
{
let response = call_external_api(request);
return transform_response(response);
}
}
Best Practices
Security Guidelines
- Always define policies for data access and operations
- Use encrypted types for sensitive data
- Implement audit logging for compliance
- Validate all inputs before processing
- Use least privilege principle in policy definitions
Performance Optimization
- Use ephemeral memory for short-lived agents
- Batch operations when possible
- Implement proper error handling with retries
- Monitor resource usage in execution context
- Use appropriate data types for your use case
Code Organization
- Group related policies in the same block
- Use descriptive capability names
- Document complex policy logic with comments
- Separate concerns into different agents
- Reuse common patterns with macros
Examples
Healthcare Data Processor
metadata {
version = "2.1.0"
author = "Medical AI Team"
description = "HIPAA-compliant patient data analyzer"
tags = ["healthcare", "hipaa", "privacy"]
}
agent medical_analyzer(patient_data: EncryptedPatientRecord) -> MedicalInsights {
capabilities = [
"medical_analysis",
"privacy_preservation",
"audit_logging",
"report_generation"
]
policy hipaa_compliance {
allow: analyze(data) if user.medical_license.valid
deny: export(data) if data.contains_identifiers
require: [
user.hipaa_training.completed,
session.secure_connection,
audit_trail = true
]
conditions: {
data_classification: "medical",
retention_period: 7.years,
access_logging: "detailed"
}
}
with
memory = "encrypted",
privacy = "differential",
security = "high",
requires = "medical_clearance"
{
try {
let decrypted = decrypt(patient_data, medical_key);
let anonymized = anonymize_data(decrypted);
let insights = analyze_medical_data(anonymized);
audit_log("analysis_completed", {
"patient_id_hash": hash(decrypted.id),
"insights_generated": insights.count,
"timestamp": now()
});
return insights;
} catch (DecryptionError e) {
security_event("decryption_failed", e.details);
return Error("Unable to process patient data");
}
}
}
Financial Transaction Monitor
agent fraud_detector(transaction: Transaction) -> FraudAssessment {
capabilities = ["fraud_detection", "risk_analysis", "real_time_processing"]
policy financial_compliance {
allow: analyze(transaction) if user.role == "fraud_analyst"
deny: store(transaction.details) if transaction.amount > 10000
require: [
user.financial_license.valid,
system.compliance_mode.active,
real_time_monitoring = true
]
audit: all_decisions with reasoning
}
with
memory = "ephemeral",
timeout = 500.milliseconds,
priority = "high"
{
let risk_score = calculate_risk(transaction);
let historical_pattern = analyze_pattern(transaction.account_id);
if (risk_score > 0.8 || historical_pattern.suspicious) {
alert_fraud_team(transaction, risk_score);
return FraudAssessment {
risk_level: "high",
recommended_action: "block_transaction",
confidence: risk_score
};
}
return FraudAssessment {
risk_level: "low",
recommended_action: "approve",
confidence: 1.0 - risk_score
};
}
}
Next Steps
- Runtime Architecture - Understand how agents execute
- Security Model - Learn about security implementation
- API Reference - Complete function and type reference
- Examples - More complete examples
Ready to build your first agent? Check out our getting started guide or explore the runtime examples.