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SILVIA Core 3.1 - System Architecture
Version: 3.1
Table of Contents
- The Behavior-Based Execution Model
- LIVE Pipeline: Listen-Infer-Validate-Execute
- Behavior Anatomy & Lifecycle
- Multi-Agent Coordination Patterns
- Trigger Mechanisms: Event-Driven, Scripted, and Inference-Based
- Audit Trail Architecture
- Extended API Ecosystem
The Behavior-Based Execution Model
SILVIA Core's architecture centers on behaviors as the fundamental unit of intelligence. Unlike traditional AI systems that produce raw outputs (text, classifications, predictions), SILVIA produces structured, validated, auditable actions through behavior execution.
What is a Behavior?
A behavior is a self-contained decision-action unit consisting of:
- Absorbers (Input Patterns) - What triggers this behavior
- Validators (Security & Preconditions) - What must be true for execution
- Exuders (Output Templates) - What this behavior produces
- Scripts (Procedural Logic) - What code executes during this behavior
- Metadata (Group, Name, Security Level, Priority) - How this behavior is classified and controlled
┌─────────────────────────────────────────────────────────────┐
│ BEHAVIOR: "TargetAcquisition" │
├─────────────────────────────────────────────────────────────┤
│ GROUP: weapons_control │
│ SECURITY: Level 5 (Operator clearance required) │
│ PRIORITY: High │
├─────────────────────────────────────────────────────────────┤
│ ABSORBER 1: "target at [coordinates]" │
│ ABSORBER 2: "engage target [designation]" │
├─────────────────────────────────────────────────────────────┤
│ VALIDATOR: User security level >= 5 │
│ Target within authorized engagement zone │
│ Weapon system status = armed │
├─────────────────────────────────────────────────────────────┤
│ EXUDER: "Target acquired: [designation] │
│ Range: [distance], Bearing: [angle]" │
├─────────────────────────────────────────────────────────────┤
│ SCRIPT: UpdateTargetingSystem([coordinates]); │
│ LogEngagementDecision([designation]); │
│ BroadcastToCoordinators("target_acquired"); │
└─────────────────────────────────────────────────────────────┘Why Behaviors, Not Direct Neural Outputs?
Traditional ML Pipeline:
Input → Neural Network → Raw Output → Hope It's CorrectProblem: No validation, no security, no auditability, no control
SILVIA Behavior Pipeline:
Input → Pattern Match → Behavior Selection → Security Validation →
Controlled Execution → Audited OutputGuarantee: Every action is authorized, logged, and traceable
This architectural decision enables:
- Predictable Execution: Same input + same brain state = same behavior selection
- Security Enforcement: Behaviors execute only if user has clearance
- Audit Compliance: Every behavior execution is logged with full context
- Maintainability: Update behaviors without recompiling system code
- Composability: Behaviors can trigger other behaviors, creating complex decision trees
LIVE Pipeline: Listen-Infer-Validate-Execute
Every interaction with SILVIA Core follows the LIVE pipeline, a four-stage process that guarantees secure, auditable, deterministic execution.
Stage 1: LISTEN (Input Processing & Conceptualization)
Objective: Transform raw input into a conceptual representation
Process:
- Input Reception: Receive text, sensor data, or API call
- Tokenization: Break input into atomic concepts (words, values, symbols)
- Conceptualization: Map tokens to internal concept IDs using atom dictionary
- Context Integration: Combine current input with conversation context
- Stopword Filtering: Remove low-information concepts (configurable)
Output: Concept array representing the semantic content of input
Example:
csharp
Input: "engage target at 35.2N 120.5W"
Tokenization:
["engage", "target", "at", "35.2N", "120.5W"]
Conceptualization (internal IDs):
[1247, 3982, 89, 5003, 5004]
Context Integration (with previous 3 turns):
Previous: [user said "radar contact"], [AI said "tracking"]
Current: [1247, 3982, 89, 5003, 5004]
Combined: [2193, 4472, 1247, 3982, 89, 5003, 5004]Stage 2: INFER (Behavior Matching & Selection)
Objective: Identify which behavior(s) best match the conceptual input
Process:
- Absorber Matching: Compare input concepts against all behavior absorbers
- Weight Calculation: Compute match quality (0.0-1.0) for each absorber
- Wildcard Handling: Process
*(any concept) and_(any single concept) patterns - Contextual Boosting: Adjust weights based on conversation history
- Ranking: Sort behaviors by match weight, breaking ties with priority
- Threshold Filtering: Discard behaviors below minimum match threshold
Matching Algorithm:
For each behavior B:
For each absorber A in B:
matched_concepts = 0
total_concepts = length(A.concepts)
For each concept C in A:
If C is wildcard:
matched_concepts += wildcard_weight
Else if C in input_concepts:
matched_concepts += 1
match_weight = matched_concepts / total_concepts
If match_weight >= threshold:
Add (B, match_weight) to candidates
Sort candidates by weight descending
Return top N candidatesOutput: Ranked list of candidate behaviors with match scores
Example:
csharp
Input concepts: [1247, 3982, 89, 5003, 5004]
("engage", "target", "at", "35.2N", "120.5W")
Candidate Behaviors:
1. TargetAcquisition - Weight: 0.95 (exact match on "engage target at [coords]")
2. RadarQuery - Weight: 0.73 (partial match on "target at")
3. GeneralAcknowledgment- Weight: 0.45 (wildcard match on any input)
Selected: TargetAcquisition (highest weight above threshold)Stage 3: VALIDATE (Security & Precondition Checks)
Objective: Ensure selected behavior is authorized and safe to execute
Process:
- User Clearance Check:
user.security_level >= behavior.security_level - Behavior Enabled Check: Behavior group is active (not disabled)
- Precondition Evaluation: Run any script-based validators
- Resource Availability: Verify required APIs/sensors are available
- Rate Limiting: Check if behavior executed too recently (anti-spam)
Security Model:
SECURITY LEVELS (0-255):
0-50: Public (any user can execute)
51-100: Operator (trained personnel only)
101-150: Supervisor (elevated privileges)
151-200: Administrator (system control)
201-255: Root (unrestricted access)
ENFORCEMENT POINT:
Before behavior execution, validate:
if (user.clearance < behavior.security) {
LogSecurityViolation(user, behavior);
RejectExecution();
return "Insufficient clearance";
}Critical Architectural Guarantee:
Jump methods (SetJump, SetJumpToGroup, SetBypassResponse) REQUEST behavior execution but do NOT bypass security validation. Even if a script calls SetJump("weapons_control", "FireMissile", 1.0f), the behavior will NOT execute unless the current user has clearance >= the FireMissile security level. Security validation happens at execution time, not suggestion time.
Output: PASS (proceed to execute) or FAIL (reject with audit log)
Example:
csharp
Behavior: TargetAcquisition
Security Level: 5 (Operator)
User: "john_doe"
Clearance: 3 (Observer)
Validation Result: FAIL
Reason: "User clearance (3) below required level (5)"
Audit Log Entry:
{
timestamp: "2025-11-18T14:32:17.442Z",
event: "SECURITY_VIOLATION",
user: "john_doe",
clearance: 3,
behavior: "TargetAcquisition",
required_clearance: 5,
input: "engage target at 35.2N 120.5W",
result: "REJECTED"
}Stage 4: EXECUTE (Controlled Action with Audit)
Objective: Perform the authorized action and log everything
Process:
- Pre-Execution Audit: Log behavior ID, input, user, timestamp (microsecond precision)
- Exuder Selection: Choose response template (random selection from variations if multiple)
- Variable Substitution: Replace placeholders with runtime values
- Script Execution: Run C# scripts (if present) with full API access
- Output Generation: Produce text, voice, socket data, behavior data
- Post-Execution Events: Trigger side effects (variable updates, sensor broadcasts)
- Post-Execution Audit: Log outputs, execution time, resource usage
Execution Context:
csharp
class BehaviorExecutionContext {
SilviaCore core; // Access to all APIs
SilviaBrain brain; // Access to knowledge base
string[] input_concepts; // What triggered this behavior
Dictionary<string,object> vars; // Current variable state
// Available to scripts:
string GetVariable(string name);
void SetVariable(string name, string value);
void BroadcastEvent(string eventName, string data);
void LogMessage(string message);
string CallExternalAPI(string url, string payload);
// ... and 100+ other API methods
}Output: Multi-modal outputs (text, voice, data) + audit trail entry
Example:
csharp
Behavior: TargetAcquisition
Input: "engage target at 35.2N 120.5W"
Execution Sequence:
1. Pre-Audit: Log behavior invocation
2. Exuder Selection: "Target acquired: [designation] Range: [distance]..."
3. Variable Substitution:
[designation] → "T-47" (from radar data)
[distance] → "2.3 km" (computed from coordinates)
4. Script Execution:
UpdateTargetingSystem(35.2, -120.5);
LogEngagementDecision("T-47");
BroadcastToCoordinators("target_acquired", {id:"T-47", coords:[35.2,-120.5]});
5. Output Generation:
Text: "Target acquired: T-47. Range: 2.3 km, Bearing: 045°"
Voice: Same text for TTS
Data: {target_id:"T-47", range_km:2.3, bearing_deg:45}
6. Post-Events:
$_last_target = "T-47"
$_engagement_status = "target_acquired"
7. Post-Audit: Log outputs, execution time (347 microseconds)
Audit Log Entry:
{
timestamp: "2025-11-18T14:32:17.000347Z",
event: "BEHAVIOR_EXECUTED",
behavior_id: 1247,
behavior_name: "TargetAcquisition",
group: "weapons_control",
user: "operator_alpha",
clearance: 5,
input_concepts: [1247, 3982, 89, 5003, 5004],
match_weight: 0.95,
execution_time_us: 347,
outputs: {
text: "Target acquired: T-47. Range: 2.3 km, Bearing: 045°",
data: "{\"target_id\":\"T-47\",\"range_km\":2.3,\"bearing_deg\":45}"
},
variables_changed: ["$_last_target", "$_engagement_status"],
coordinator_broadcasts: ["target_acquired"]
}Behavior Anatomy & Lifecycle
Behavior Authoring: Expert Text Format
Behaviors are authored in Expert Text, a human-readable format that compiles to bytecode. This enables domain experts (not just programmers) to create AI logic.
Example Expert Text:
[weapons_control TargetAcquisition]
@SECURITY 5
@PRIORITY high
# This behavior handles target acquisition requests from operators
# It validates coordinates and updates the targeting system
< engage target at * *
< target * bearing * range *
< lock target *
> Target acquired: [target_designation]
> Range: [range_km] km, Bearing: [bearing_deg]°
> Awaiting engagement authorization
@SCRIPT
// Get coordinates from input
string coords = GetConceptValue(3, 4); // positions 3,4 in input
double lat = ParseLatitude(coords);
double lon = ParseLongitude(coords);
// Validate engagement zone
if (!IsWithinAuthorizedZone(lat, lon)) {
SetVariable("$_error", "Target outside authorized engagement zone");
return "ABORT";
}
// Update targeting system
UpdateTargetingSystem(lat, lon);
string targetId = GenerateTargetID();
SetVariable("$_last_target", targetId);
// Notify other systems
BroadcastToCoordinators("target_acquired", $"{{\"id\":\"{targetId}\",\"lat\":{lat},\"lon\":{lon}}}");
LogEngagementDecision(targetId, lat, lon);
return "SUCCESS";
@ENDSCRIPTCompilation Process:
- Parse: Extract absorbers, exuders, metadata, scripts
- Conceptualize: Convert text patterns to concept ID arrays
- Script Compilation: Use Roslyn to compile C# scripts to native code
- Bytecode Generation: Serialize to compact binary format
- Brain File Creation: Package all behaviors into
.silbrain file
At Runtime:
- Brain file loaded into memory
- Behaviors indexed by group/name/security level
- Scripts remain compiled—no interpretation overhead
Behavior Groups & Hierarchical Organization
Behaviors are organized into groups for management and security control:
weapons_control/
├─ TargetAcquisition
├─ FireControl
├─ SafetyOverride
└─ WeaponStatus
navigation/
├─ SetWaypoint
├─ PlotCourse
├─ ObstacleAvoidance
└─ EmergencyReturn
communication/
├─ SendMessage
├─ BroadcastAlert
└─ RequestSupportGroup Operations:
- Enable/Disable: Turn entire groups on/off at runtime
- Security Inheritance: Groups can have base security levels
- Subgroups: Hierarchical organization (e.g.,
weapons_control/missiles,weapons_control/guns) - Bulk Management: Load/unload/merge groups independently
Use Case:
Disable weapons_control group during maintenance mode. All weapon-related behaviors become unavailable without restarting the system.
Behavior Lifecycle States
CREATED → Brain file loaded, behavior parsed
↓
ENABLED → Group active, behavior available for matching
↓
MATCHED → Input triggered this behavior (inference stage)
↓
VALIDATED → Security checks passed (validation stage)
↓
EXECUTING → Script running, outputs being generated
↓
COMPLETED → Execution finished, outputs delivered
↓
AUDITED → Log entry written, state synchronizedState Transitions:
CREATED → ENABLED: Happens at brain load or group enableENABLED → MATCHED: Triggered by inference engine on inputMATCHED → VALIDATED: Passes security and precondition checksVALIDATED → EXECUTING: Execution engine takes controlEXECUTING → COMPLETED: Script finishes, outputs generatedCOMPLETED → AUDITED: Audit trail entry written
Failure Points:
MATCHED → FAILED_VALIDATION: Security check failed, audit loggedEXECUTING → FAILED_EXECUTION: Script threw exception, error logged
Multi-Agent Coordination Patterns
SILVIA Core supports distributed AI architectures through multi-agent coordination, where multiple cores work together as specialized agents.
Architecture: Independent Cores with Event-Based Communication
┌───────────────┐ ┌───────────────┐ ┌───────────────┐
│ Vision Core │ │ Decision Core │ │ Control Core │
│ │ │ │ │ │
│ - Image proc │ │ - Planning │ │ - Actuators │
│ - Object det │ │ - Strategy │ │ - Servo ctrl │
│ - Tracking │ │ - Validation │ │ - Safety mon │
└──────┬────────┘ └──────┬────────┘ └──────┬────────┘
│ │ │
│ obstacle_detected │ execute_maneuver │
└──────────────────────┼──────────────────────┘
▲ │ ▲
│ │ │
Coordinator Sensors (Event Bus)
│ │ │
┌───────────┴──────────┴──────────┴───────────┐
│ Publish/Subscribe Events │
│ - Typed messages with serialized data │
│ - Async delivery with delivery guarantees │
│ - Audit-logged for full traceability │
└─────────────────────────────────────────────┘Coordinator Sensors: The Event Bus
Coordinator sensors are the inter-core communication mechanism. Each core can:
- Broadcast events: Publish typed messages with arbitrary data
- Subscribe to events: Register behaviors that trigger on specific event types
- Query state: Ask other cores about their current status
Coordinator Sensor API:
csharp
// Core A: Broadcast an event
core.ApiSensors().BroadcastToCoordinators("obstacle_detected",
"{\"type\":\"wall\",\"distance_m\":2.5,\"bearing_deg\":315}");
// Core B: Subscribe with a behavior
[sensor_fusion ObstacleHandler]
@COORDINATOR_EVENT obstacle_detected
< * # Triggered by coordinator event, not user input
> Obstacle detected: [obstacle_type] at [distance]m
> Adjusting course to avoid collision
@SCRIPT
// Parse event data
var eventData = GetCoordinatorEventData();
var obstacle = JsonDeserialize<Obstacle>(eventData);
// Update local state
SetVariable("$_obstacle_bearing", obstacle.bearing_deg.ToString());
SetVariable("$_obstacle_distance", obstacle.distance_m.ToString());
// Trigger avoidance maneuver
BroadcastToCoordinators("execute_maneuver",
$"{{\"type\":\"avoidance\",\"direction\":{(obstacle.bearing_deg + 90) % 360}}}");
LogObstacleEvent(obstacle);
@ENDSCRIPTCoordination Patterns
Pattern 1: Sensor Fusion
Multiple cores collect different sensor data, one core fuses it for decision-making.
Lidar Core → "lidar_scan" →
Radar Core → "radar_ping" → Fusion Core → Decision
Camera Core → "image_frame" →Fusion Core Behavior:
csharp
[sensor_fusion CombinedEnvironment]
@COORDINATOR_EVENT lidar_scan
@COORDINATOR_EVENT radar_ping
@COORDINATOR_EVENT image_frame
< *
@SCRIPT
// Collect all sensor data within 100ms window
var lidar = WaitForEvent("lidar_scan", timeout_ms: 100);
var radar = WaitForEvent("radar_ping", timeout_ms: 100);
var camera = WaitForEvent("image_frame", timeout_ms: 100);
// Fuse into unified environment model
var environment = FuseSensorData(lidar, radar, camera);
// Broadcast fused result
BroadcastToCoordinators("environment_update",
JsonSerialize(environment));
@ENDSCRIPTPattern 2: Hierarchical Decision Making
High-level strategic core directs multiple low-level tactical cores.
Strategic Core (plans mission)
↓
"execute_phase_2"
↓
┌────┴────┬──────────┐
↓ ↓ ↓
Tactical A Tactical B Tactical C
(formation) (scouting) (logistics)Strategic Core:
csharp
BroadcastToCoordinators("execute_phase_2",
"{\"formation\":\"wedge\",\"speed\":15,\"destination\":[35.2,-120.5]}");Tactical Cores: Each receives event, executes specialized role independently.
Pattern 3: Consensus-Based Validation
Multiple cores vote on a decision before execution.
Proposal Core → "propose_action" → Validator Cores (3)
↓
Vote: approve/reject
↓
Execution Core (if consensus)Validator Behavior:
csharp
[validation ActionValidator]
@COORDINATOR_EVENT propose_action
< *
@SCRIPT
var proposal = JsonDeserialize<ActionProposal>(GetCoordinatorEventData());
// Validate against local constraints
bool approved = ValidateAction(proposal);
// Cast vote
BroadcastToCoordinators("vote_cast",
$"{{\"proposal_id\":\"{proposal.id}\",\"vote\":\"{(approved ? "approve" : "reject")}\"}}");
@ENDSCRIPTPattern 4: Autonomous Swarm Coordination
Peer-to-peer coordination without central controller.
Agent 1 ←→ Agent 2 ←→ Agent 3 ←→ Agent 4
↕ ↕ ↕ ↕
All agents broadcast position, receive neighbors, adjust behaviorSwarm Agent Behavior:
csharp
[swarm PositionUpdate]
@TIMED_FUNCTION interval_ms: 500 // Every 500ms
< *
@SCRIPT
// Broadcast my position
var myPos = GetCurrentPosition();
BroadcastToCoordinators("agent_position",
$"{{\"agent_id\":\"{GetAgentID()}\",\"x\":{myPos.x},\"y\":{myPos.y}}}");
// Receive neighbor positions (last 1 second)
var neighbors = GetCoordinatorEvents("agent_position", since_ms: 1000);
// Compute formation adjustment
var adjustment = ComputeFormationAdjustment(myPos, neighbors);
// Execute movement
ExecuteMovement(adjustment);
@ENDSCRIPTCoordinator Sensor Implementation Details
Event Message Structure:
csharp
class CoordinatorEvent {
string event_type; // "obstacle_detected"
string source_core_id; // "vision_core_01"
long timestamp_us; // Microsecond precision
string payload; // JSON serialized data
string signature; // HMAC for tamper detection
}Delivery Guarantees:
- At-least-once: Events delivered to all subscribers, retries on failure
- Ordering: Events from single source maintain order
- Audit logging: All events logged for forensic reconstruction
Network Resilience:
- Cores maintain local event queues
- Network failures don't lose events (queue persists)
- Reconnection automatically syncs missed events
Trigger Mechanisms: Event-Driven, Scripted, and Inference-Based
SILVIA behaviors can be triggered through three distinct mechanisms, each optimized for different use cases.
Mechanism 1: Inference-Based (Traditional Input Matching)
How it works: User input → concept matching → behavior selection
Use cases:
- Conversational AI
- Command processing
- Natural language interfaces
Example:
csharp
User: "set waypoint to 35N 120W"
↓
Conceptualization: [set, waypoint, to, 35N, 120W]
↓
Behavior Match: SetWaypoint (weight: 0.92)
↓
Execution: Script updates navigation systemConfiguration:
[navigation SetWaypoint]
< set waypoint to * *
< waypoint * *
< navigate to * *
@SCRIPT
// Extract coordinates from input
var coords = ParseCoordinates(GetInputConcepts());
UpdateWaypoint(coords.lat, coords.lon);
@ENDSCRIPTMechanism 2: Event-Driven (Coordinator Sensors)
How it works: External event → matching behavior triggered instantly
Use cases:
- Sensor fusion
- Multi-agent coordination
- Real-time responsiveness
Example:
csharp
Vision Core: BroadcastToCoordinators("obstacle_detected", {...})
↓
Control Core: Receives event, triggers ObstacleHandler behavior
↓
Execution: Script adjusts course immediatelyConfiguration:
csharp
[sensor_fusion ObstacleHandler]
@COORDINATOR_EVENT obstacle_detected # <- Event trigger
< * # No input matching needed
@SCRIPT
var data = GetCoordinatorEventData();
ExecuteAvoidanceManeuver(data);
@ENDSCRIPTAdvantages:
- Zero latency: No inference delay, event directly triggers behavior
- Decoupled systems: Cores don't need to know about each other
- Scalable: Add more cores without reconfiguring existing ones
Mechanism 3: Scripted (Timed Functions & Programmatic Triggers)
How it works: Timer expires or API call directly invokes behavior
Use cases:
- Periodic tasks (sensor polling, health checks)
- Scheduled operations (maintenance windows)
- Programmatic control (jump to specific behavior)
Example A: Timed Function
csharp
[monitoring SystemHealthCheck]
@TIMED_FUNCTION interval_ms: 5000 # Every 5 seconds
< * # No input needed
@SCRIPT
var cpuUsage = GetCPUUsage();
var memUsage = GetMemoryUsage();
var diskUsage = GetDiskUsage();
if (cpuUsage > 80 || memUsage > 90 || diskUsage > 95) {
BroadcastToCoordinators("health_warning",
$"{{\"cpu\":{cpuUsage},\"mem\":{memUsage},\"disk\":{diskUsage}}}");
LogHealthWarning();
}
SetVariable("$_cpu_usage", cpuUsage.ToString());
SetVariable("$_mem_usage", memUsage.ToString());
@ENDSCRIPTExample B: Programmatic Jump
csharp
// From another behavior's script:
if (emergencyDetected) {
// Request jump to emergency behavior
core.ApiBrain().SetJump("safety", "EmergencyShutdown", 1.0f);
// NOTE: Still subject to security validation!
}Example C: Fixed Update Loop (Unity/Physics Integration)
csharp
// Register physics callback
core.AddFixedUpdateCallback((deltaTime) => {
// Called every physics frame (e.g., 50Hz)
core.ApiBrain().SetJump("physics", "UpdateRigidbodies", 1.0f);
});Combining Trigger Mechanisms
Behaviors can respond to multiple trigger types simultaneously:
csharp
[weapons_control TargetTracking]
@COORDINATOR_EVENT radar_contact # Event trigger
@TIMED_FUNCTION interval_ms: 100 # Timed trigger (10Hz)
< track target * # Also inference-based
@SCRIPT
// This behavior runs:
// 1. When radar_contact event received
// 2. Every 100ms (timed function)
// 3. When user says "track target X"
var target = GetCurrentTarget();
if (target != null) {
UpdateTrackingDisplay(target);
BroadcastToCoordinators("tracking_update", SerializeTarget(target));
}
@ENDSCRIPTAudit Trail Architecture
Every action, decision, and state change in SILVIA Core is logged with cryptographic signing for tamper-evident audit trails.
Audit Log Structure
Entry Format:
csharp
class AuditLogEntry {
// Temporal
long timestamp_us; // Microsecond precision (epoch time)
long sequence_number; // Monotonically increasing ID
// Event Classification
string event_type; // BEHAVIOR_EXECUTED, SECURITY_VIOLATION, etc.
string category; // execution, security, system, network
// Actor Information
string user_id; // Who triggered this
int user_clearance; // User's security level
string core_id; // Which SILVIA core
// Behavior Details
int behavior_id; // Behavior that executed
string behavior_name; // Human-readable name
string behavior_group; // Group classification
int behavior_security; // Required security level
// Input Context
int[] input_concepts; // Conceptualized input
string input_text; // Original text (if applicable)
double match_weight; // How well behavior matched
// Execution Results
string result; // SUCCESS, FAILED, REJECTED
long execution_time_us; // Microseconds to execute
string[] outputs; // What was produced
string error_message; // If failed, why
// State Changes
string[] variables_changed; // Which variables modified
string[] events_broadcast; // Which coordinator events sent
// Cryptographic Integrity
string signature; // HMAC-SHA256 signature
string previous_entry_hash; // Hash of previous log entry (blockchain-style)
}Cryptographic Signing Process
Per-Instance Key Generation:
csharp
On core initialization:
1. Generate random 256-bit key (per-instance, per-session)
2. Store in protected memory (not persisted to disk)
3. Use for HMAC signing all audit entries
Key rotation:
- New key generated on core restart
- Old key included in final log entry for verificationEntry Signing:
csharp
For each audit entry:
1. Serialize entry fields to canonical form
2. Compute HMAC-SHA256(canonical_form, instance_key)
3. Store signature in entry
4. Compute SHA256(entire_entry) as "previous_entry_hash" for next entry
Result: Blockchain-style chain where tampering with any entry breaks the chainVerification:
csharp
To verify audit trail integrity:
1. Start with first entry
2. For each entry:
a. Verify HMAC signature matches computed signature
b. Verify previous_entry_hash matches hash of previous entry
c. Verify timestamp is monotonically increasing
3. If any check fails → tamper detected
Chain breaks indicate:
- Unauthorized modification of log entries
- Deletion of log entries
- Insertion of fake log entriesEvent Types & Categories
Execution Events:
BEHAVIOR_EXECUTED- Normal behavior invocation and completionBEHAVIOR_FAILED- Behavior script threw exceptionBEHAVIOR_TIMEOUT- Execution exceeded time limit
Security Events:
SECURITY_VIOLATION- User attempted unauthorized behaviorCLEARANCE_INSUFFICIENT- Failed security level checkGROUP_DISABLED- Attempted to execute disabled group behavior
System Events:
CORE_STARTED- SILVIA core initializedCORE_SHUTDOWN- SILVIA core terminatedBRAIN_LOADED- Brain file loaded/reloadedVARIABLE_SET- System variable modified
Network Events:
COORDINATOR_BROADCAST- Event sent to other coresCOORDINATOR_RECEIVED- Event received from other coreAPI_CALL- External API invoked from script
Audit Trail Storage & Retrieval
Storage Options:
1. In-Memory Buffer (Real-Time)
csharp
core.SetAuditBufferSize(10000); // Keep last 10K entries in RAM
var recentLogs = core.GetAuditBuffer();2. File-Based (Long-Term)
csharp
core.SetAuditLogPath("logs/audit_{timestamp}.log");
// Automatically rotates files at size limit or time interval3. Database Integration (Enterprise)
csharp
core.SetAuditDatabase("Server=sql.local;Database=silvia_audit;...");
// Entries streamed to SQL Server, PostgreSQL, or MongoDBRetrieval API:
csharp
// Query by time range
var logs = core.GetAuditLogs(
startTime: DateTime.Parse("2025-11-18 14:00:00"),
endTime: DateTime.Parse("2025-11-18 15:00:00")
);
// Query by event type
var violations = core.GetAuditLogs(
eventType: "SECURITY_VIOLATION"
);
// Query by user
var userActions = core.GetAuditLogs(
userId: "operator_alpha"
);
// Verify integrity
bool intact = core.VerifyAuditChain(logs);
if (!intact) {
LogCriticalAlert("Audit trail tampered!");
}Forensic Reconstruction
Audit logs enable complete system state reconstruction:
Scenario: Autonomous vehicle incident at 14:32:17
Reconstruction Process:
- Extract all audit logs within ±10 seconds of incident
- Verify cryptographic chain integrity
- Rebuild execution timeline:
- What sensors reported (coordinator events)
- What behaviors executed (behavior logs)
- What decisions were made (output logs)
- What security checks passed (validation logs)
- Identify causal chain from input to action
- Generate forensic report with frame-by-frame analysis
Output:
14:32:15.234 - Lidar sensor detected obstacle at 2.5m, bearing 315°
14:32:15.238 - ObstacleHandler behavior triggered (event-driven)
14:32:15.241 - Security validation: PASSED (user: system, clearance: 255)
14:32:15.243 - Script execution: ComputeAvoidanceManeuver()
14:32:15.247 - Coordinator broadcast: execute_maneuver (avoidance, direction: 45°)
14:32:15.250 - ControlCore received execute_maneuver event
14:32:15.253 - SteeringControl behavior triggered
14:32:15.256 - Security validation: PASSED (user: system, clearance: 255)
14:32:15.258 - Actuator command: SetSteeringAngle(15°)
14:32:15.261 - Vehicle initiated turn
TIMELINE: 27ms from obstacle detection to steering actuation
CAUSAL CHAIN: Sensor → Behavior → Validation → Script → Event → Control
SECURITY: All actions properly authorized
CONCLUSION: System performed as designed, no anomalies detectedExtended API Ecosystem
The behavior system is the orchestration layer that coordinates specialized functionality provided by SILVIA's extended API ecosystem. While behaviors define the what (decision logic) and when (triggers), the extended APIs provide the how (implementation).
Core API Integration Points
Behaviors interact with the extended APIs through script execution, variable access, and coordinator events. The API ecosystem includes (high-level overview):
Foundational APIs (covered in detail in previous documentation):
- ApiCore - Core lifecycle, variable management, timed functions
- ApiBrain - Inference engine, behavior execution, security control
- ApiApp - Output management, command processing, TTS integration
- ApiData - Knowledge base construction, concepts, bindings
- ApiMem - Brain file persistence, group management
- ApiUser - Multi-user session management, data isolation
- ApiFeedback - Spontaneous thought generation, conversational memory
Extended APIs (detailed in separate documentation):
Machine Learning Integration:
- ApiML - Neural network inference, model loading, tensor operations
- Integration: Behaviors invoke ML models for classification/prediction
- Use case: "run image classifier, then select behavior based on result"
Domain-Specific Subsystems:
- ApiBallistics - Trajectory calculation, projectile physics, wind compensation
- ApiLogistics - Supply chain optimization, route planning, resource allocation
- ApiFinance - Financial modeling, risk analysis, portfolio optimization
- ApiNavigation - Pathfinding, obstacle avoidance, map management
- ApiComponents - Hardware abstraction, sensor drivers, actuator control
- ApiMeshNet - Peer-to-peer networking, mesh topology, distributed state
- ApiNavajo - Specialized encryption/communications (domain-specific)
- ApiModding - Plugin system, dynamic behavior loading, extension framework
Sensor Integration:
- ApiSensors - Sensor registration, coordinator events, data fusion, database support
- Integration: Behaviors subscribe to sensor events, process readings
- Use case: "when temperature sensor exceeds threshold, trigger cooling behavior"
- SQL query execution, connection pooling, transaction management
- Integration: Behaviors query databases for real-time data lookups
- Use case: "get customer history from database before responding"
How Behaviors Orchestrate APIs
Pattern 1: Direct API Invocation
csharp
[weapons_control BallisticCalculation]
< calculate trajectory for * at * meters
@SCRIPT
// Behavior calls ApiBallistics for physics computation
double angle = ParseAngle(GetConcept(3));
double distance = ParseDouble(GetConcept(5));
var trajectory = core.ApiBallistics().ComputeTrajectory(
angle: angle,
distance: distance,
wind_speed: GetWindSpeed(),
projectile_mass: 5.0
);
SetVariable("$_trajectory_data", JsonSerialize(trajectory));
BroadcastToCoordinators("trajectory_computed", SerializeTrajectory(trajectory));
@ENDSCRIPTPattern 2: ML Model Integration
csharp
[vision ObjectClassification]
@COORDINATOR_EVENT image_captured
@SCRIPT
// Get image data from event
var imageData = GetCoordinatorEventData();
// Invoke ML model through ApiML
var classification = core.ApiML().RunInference(
model: "yolov8_detection",
input: imageData
);
// Select behavior based on classification
if (classification.label == "person") {
core.ApiBrain().SetJump("safety", "PersonDetected", 1.0f);
} else if (classification.label == "vehicle") {
core.ApiBrain().SetJump("tracking", "VehicleTracking", 1.0f);
}
@ENDSCRIPTPattern 3: Database-Driven Decision Making
csharp
[customer_service PersonalizedResponse]
< help with order *
@SCRIPT
// Query customer history from database
var db = core.ApiSensors().GetSensor("masterDB");
string customerId = GetVariable("$_user_id");
var query = $"SELECT * FROM orders WHERE customer_id = '{customerId}' ORDER BY date DESC LIMIT 5";
var orders = db.ExecuteQuery(query);
// Customize response based on data
string response;
if (orders.Count > 0) {
var latestOrder = orders[0];
response = $"I see your recent order #{latestOrder.id} for {latestOrder.product}. ";
if (latestOrder.status == "shipped") {
response += "It's currently in transit, expected delivery in 2-3 days.";
} else if (latestOrder.status == "processing") {
response += "It's being prepared for shipment.";
}
} else {
response = "I don't see any recent orders. Would you like to browse our catalog?";
}
core.ApiApp().SetTextOutput(response);
@ENDSCRIPTPattern 4: Multi-API Coordination
csharp
[autonomous_navigation WaypointNavigation]
@TIMED_FUNCTION interval_ms: 100 # 10Hz update rate
@SCRIPT
// 1. Get current position from sensors
var gpsData = core.ApiSensors().GetSensorReading("gps");
var currentPos = ParsePosition(gpsData);
// 2. Get target waypoint from database
var waypoint = core.ApiSensors().ExecuteDatabaseQuery("SELECT * FROM waypoints WHERE status = 'active' LIMIT 1")[0];
var targetPos = new Position(waypoint.lat, waypoint.lon);
// 3. Compute path using ApiNavigation
var path = core.ApiNavigation().ComputePath(currentPos, targetPos, obstacles: GetObstacleMap());
// 4. Execute first path segment through ApiComponents
if (path.segments.Count > 0) {
var nextSegment = path.segments[0];
core.ApiComponents().SetMotorSpeed("left", nextSegment.leftSpeed);
core.ApiComponents().SetMotorSpeed("right", nextSegment.rightSpeed);
}
// 5. Broadcast progress to coordinator network
BroadcastToCoordinators("nav_update", JsonSerialize(new {
position = currentPos,
target = targetPos,
distance_remaining = nav.CalculateDistance(currentPos, targetPos)
}));
@ENDSCRIPTArchitectural Benefits
By separating orchestration (behaviors) from implementation (APIs), SILVIA achieves:
- Modularity: Swap API implementations without changing behaviors
- Testability: Mock APIs for behavior testing in isolation
- Reusability: Same APIs used across different behavior contexts
- Performance: APIs optimized in native code, behaviors remain high-level
- Security: APIs enforce their own security boundaries independent of behaviors
- Scalability: Add new APIs without modifying core behavior engine
The behavior system is the conductor. The extended APIs are the orchestra.
Summary: Architecture for Production AI
SILVIA Core's architecture delivers on five critical requirements for production-deployed AI:
1. Determinism - LIVE pipeline guarantees same input + same state = same output
2. Security - Multi-layer validation with cryptographic audit trails
3. Scalability - Multi-agent coordination enables distributed workloads
4. Flexibility - Three trigger mechanisms cover all operational patterns
5. Maintainability - Behavior-based orchestration with extended API integration
This architecture transforms AI from advisory suggestions to validated, auditable, production-critical execution.
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