T4: Agentic Component Integration

Source: T4_ Agentic Component Integration.pdf Status: 🚧 Extraction in Progress Roadmap: Technical Architecture Roadmap

Overview

Agentic Component Integration enables AI agents to discover, select, and use components intelligently. This specification defines the patterns and mechanisms for integrating agents with the component ecosystem.

Agent-Component Bridge

Component Interface

Component Invocation Interface

pub trait ComponentInterface {
    async fn invoke(&self, input: ComponentInput, context: &InvocationContext) -> Result<ComponentOutput>;
    fn get_interface(&self) -> &ComponentInterfaceSpec;
    fn validate_input(&self, input: &ComponentInput) -> ValidationResult;
}

Component Input/Output

pub struct ComponentInput {
    pub parameters: HashMap<String, serde_json::Value>,
    pub context: Option<Context>,
    pub metadata: InvocationMetadata,
}

pub struct ComponentOutput {
    pub result: serde_json::Value,
    pub metadata: OutputMetadata,
    pub performance: PerformanceMetrics,
}

Agent Component Interface

Agent-to-Component Bridge

pub struct AgentComponentBridge {
    component_registry: ComponentRegistry,
    invocation_engine: InvocationEngine,
}

impl AgentComponentBridge {
    pub async fn invoke_component(
        &self,
        agent: &AgentId,
        component: &ComponentId,
        input: ComponentInput,
    ) -> Result<ComponentOutput> {
        // 1. Validate agent permissions
        // 2. Load component
        // 3. Validate input
        // 4. Execute component
        // 5. Return result
    }
}

Component Invocation from Agents

Invocation Patterns

• Synchronous invocation
• Asynchronous invocation
• Streaming invocation
• Batch invocation

Invocation API

pub trait ComponentInvoker {
    async fn invoke(&self, request: InvocationRequest) -> Result<InvocationResponse>;
    async fn invoke_stream(&self, request: InvocationRequest) -> Result<Stream<OutputChunk>>;
    async fn invoke_batch(&self, requests: Vec<InvocationRequest>) -> Result<Vec<InvocationResponse>>;
}

Result Handling

Result Processing

pub struct ResultHandler {
    transformers: Vec<ResultTransformer>,
    validators: Vec<ResultValidator>,
}

impl ResultHandler {
    pub fn process(&self, output: ComponentOutput) -> Result<ProcessedResult> {
        // Transform and validate result
    }
}

Error Management

pub enum ComponentError {
    InvocationFailed(InvocationError),
    ValidationFailed(ValidationError),
    Timeout(Duration),
    ResourceExhausted,
    PermissionDenied,
}

Async Component Execution

Async Execution Model

pub struct AsyncExecutor {
    executor: tokio::runtime::Handle,
    task_registry: TaskRegistry,
}

impl AsyncExecutor {
    pub async fn execute_async(
        &self,
        component: &ComponentId,
        input: ComponentInput,
    ) -> Result<TaskId> {
        // Submit async task
    }

    pub async fn get_result(&self, task_id: &TaskId) -> Result<ComponentOutput> {
        // Retrieve async result
    }
}

Intelligent Composition

Agent-Driven Component Selection

Selection Strategy

pub trait ComponentSelector {
    fn select_components(
        &self,
        agent: &AgentId,
        goal: &Goal,
        context: &SelectionContext,
    ) -> Vec<SelectedComponent>;
}

Selection Criteria

• Component capabilities
• Performance characteristics
• Cost considerations
• Quality metrics
• Compatibility

Selection Algorithm

pub struct IntelligentSelector {
    scoring_engine: ScoringEngine,
    learning_model: LearningModel,
}

impl ComponentSelector for IntelligentSelector {
    fn select_components(&self, agent: &AgentId, goal: &Goal, context: &SelectionContext) -> Vec<SelectedComponent> {
        // 1. Find candidate components
        // 2. Score candidates
        // 3. Apply learning insights
        // 4. Select optimal set
    }
}

Context-Aware Composition

Context Gathering

pub struct CompositionContext {
    pub agent_context: AgentContext,
    pub workspace_context: WorkspaceContext,
    pub execution_context: ExecutionContext,
    pub historical_context: HistoricalContext,
}

Context-Aware Selection

pub trait ContextAwareSelector {
    fn select_with_context(
        &self,
        goal: &Goal,
        context: &CompositionContext,
    ) -> Result<Composition>;
}

Dynamic Adaptation

Adaptation Strategies

• Component replacement
• Parameter adjustment
• Composition restructuring
• Fallback selection

Adaptation Engine

pub trait AdaptationEngine {
    fn adapt(&self, composition: &Composition, feedback: &Feedback) -> Result<AdaptedComposition>;
    fn monitor(&self, composition: &Composition) -> AdaptationSignals;
}

Learning from Usage Patterns

Usage Pattern Analysis

pub struct UsageAnalyzer {
    pattern_detector: PatternDetector,
    learning_engine: LearningEngine,
}

impl UsageAnalyzer {
    pub fn analyze_patterns(&self, usage_history: &[UsageRecord]) -> UsagePatterns {
        // Detect patterns in component usage
    }

    pub fn learn_from_usage(&self, patterns: &UsagePatterns) -> LearnedInsights {
        // Extract insights for future selection
    }
}

Multi-Agent Coordination

Agent Collaboration Patterns

Collaboration Types

• Sequential collaboration
• Parallel collaboration
• Hierarchical collaboration
• Peer-to-peer collaboration

Collaboration Framework

pub struct CollaborationFramework {
    coordinator: AgentCoordinator,
    communication: CommunicationChannel,
}

pub trait AgentCoordinator {
    fn coordinate(&self, agents: &[AgentId], task: &Task) -> Result<CoordinationPlan>;
    fn execute_collaboration(&self, plan: &CoordinationPlan) -> Result<CollaborationResult>;
}

Shared Component State

State Management

pub struct SharedState {
    state_store: StateStore,
    synchronization: SynchronizationProtocol,
}

impl SharedState {
    pub fn get(&self, key: &str) -> Result<Option<StateValue>>;
    pub fn set(&self, key: &str, value: StateValue) -> Result<()>;
    pub fn sync(&self) -> Result<SyncResult>;
}

State Synchronization

• Event-driven synchronization
• Periodic synchronization
• On-demand synchronization
• Conflict resolution

Conflict Resolution

Conflict Types

• Resource conflicts
• State conflicts
• Component conflicts
• Priority conflicts

Resolution Strategies

pub enum ConflictResolution {
    FirstWins,
    LastWins,
    PriorityBased,
    Negotiation,
    Manual,
}

Workflow Orchestration

Workflow Definition

pub struct Workflow {
    pub steps: Vec<WorkflowStep>,
    pub dependencies: Vec<Dependency>,
    pub error_handling: ErrorHandlingStrategy,
}

pub struct WorkflowStep {
    pub agent: AgentId,
    pub component: ComponentId,
    pub input: ComponentInput,
    pub condition: Option<Condition>,
}

Workflow Execution

pub trait WorkflowOrchestrator {
    fn execute(&self, workflow: &Workflow) -> Result<WorkflowResult>;
    fn execute_parallel(&self, steps: &[WorkflowStep]) -> Result<Vec<StepResult>>;
}

Integration Patterns

Pattern 1: Direct Invocation

Pattern Description Agent directly invokes component with explicit parameters.

Use Cases

• Simple, well-defined tasks
• Performance-critical operations
• Deterministic requirements

Implementation

let output = agent_component_bridge
    .invoke_component(agent_id, component_id, input)
    .await?;

Pattern 2: Goal-Based Selection

Pattern Description Agent specifies goal, system selects and invokes appropriate components.

Use Cases

• Complex, multi-step tasks
• Dynamic requirements
• Exploration scenarios

Implementation

let composition = intelligent_selector
    .select_components(agent_id, &goal, &context)?;

let result = composition_engine
    .execute(&composition)
    .await?;

Pattern 3: Learning-Based Selection

Pattern Description System learns from past usage to improve component selection.

Use Cases

• Repeated tasks
• Optimization scenarios
• Personalization

Implementation

let insights = usage_analyzer
    .learn_from_usage(&usage_history)?;

let composition = learning_selector
    .select_with_insights(&goal, &insights)?;

Performance Considerations

Optimization Strategies

• Component caching
• Result caching
• Parallel execution
• Lazy loading
• Connection pooling

Monitoring and Metrics

Metrics

• Invocation latency
• Success rate
• Resource usage
• Component utilization
• Error rates

Implementation Status

📋 Planned

• Agent component interface
• Component invocation from agents
• Result handling and error management
• Async component execution
• Agent-driven component selection
• Context-aware composition
• Dynamic adaptation
• Learning from usage patterns
• Agent collaboration patterns
• Shared component state
• Conflict resolution
• Workflow orchestration

Related Documentation

• Technical Architecture Roadmap
• Core Architecture Specification
• Global Component Graph
• Agent System Architecture

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Note: This specification is extracted from the OpenKor T4 document. Detailed integration patterns may need manual review from the source PDF.