overview.md

Device Agent Architecture

Device Agents are the execution engines of UFO3's multi-device orchestration system. Each device agent operates as an autonomous, intelligent controller that translates high-level user intentions into low-level system commands. The architecture is designed for extensibility, safety, and scalability across heterogeneous computing environments.

Overview

UFO3 orchestrates tasks across multiple devices through a network of Device Agents. Originally designed as a Windows automation framework (UFO2), the architecture has evolved to support diverse platforms including Linux, macOS, and embedded systems. This document describes the abstract design principles and interfaces that enable this multi-platform capability.

Key Capabilities:

• Multi-Platform: Windows agents (HostAgent, AppAgent), Linux agent, extensible to macOS and embedded systems
• Safe Execution: Server-client separation isolates reasoning from system-level operations
• Scalable Architecture: Hierarchical agent coordination supports complex cross-device workflows
• LLM-Driven Reasoning: Dynamic decision-making using large language models
• Modular Design: Three-layer architecture (State, Strategy, Command) enables customization

---

Three-Layer Architecture

Device agents implement a three-layer framework that separates concerns, promotes modularity, and enables extensibility:

graph TB
    subgraph "Device Agent Architecture"
        subgraph "Level-1: State Layer (FSM)"
            S1[AgentState]
            S2[State Machine]
            S3[State Transitions]
            S1 --> S2 --> S3
        end
        
        subgraph "Level-2: Strategy Layer (Execution Logic)"
            P1[ProcessorTemplate<br/>Strategy Orchestrator]
            P2[DATA_COLLECTION<br/>Strategies]
            P3[LLM_INTERACTION<br/>Strategies]
            P4[ACTION_EXECUTION<br/>Strategies]
            P5[MEMORY_UPDATE<br/>Strategies]
            P1 -->|manages & executes| P2
            P2 --> P3 --> P4 --> P5
        end
        
        subgraph "Level-3: Command Layer (System Interface)"
            C1[CommandDispatcher]
            C2[MCP Tools]
            C3[Atomic Commands]
            C1 --> C2 --> C3
        end
        
        S3 -->|delegates to| P1
        P5 -->|executes via| C1
    end
    
    LLM[Large Language Model]
    P3 -.->|reasoning| LLM
    LLM -.->|decisions| P4

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Layer Responsibilities

Layer	Level	Responsibility	Key Components	Extensibility
State	Level-1	Finite State Machine governing agent lifecycle	AgentState, AgentStateManager, AgentStatus	Register new states via @AgentStateManager.register
Strategy	Level-2	Execution logic layer: processor manages sequence of modular strategies	ProcessorTemplate, ProcessingStrategy, ProcessingPhase, Middleware	Compose custom strategies via ComposedStrategy, add middleware
Command	Level-3	Atomic system operations mapped to MCP tools	BasicCommandDispatcher, Command, MCP integration	Add new tools via client-side MCP server registration

Design Rationale:

The three-layer separation ensures:

• State Layer (Level-1): Controls when and what to execute (state transitions, agent handoff)
• Strategy Layer (Level-2): Defines how to execute (processor orchestrates modular strategies)
• Command Layer (Level-3): Performs actual execution (deterministic system operations)

This separation allows replacing individual layers without affecting others.

---

Level-1: State Layer (FSM)

The State Layer implements a Finite State Machine (FSM) that governs the agent's execution lifecycle. Each state encapsulates:

• A processor (strategy execution logic)
• Transition rules (to next state)
• Agent handoff logic (for multi-agent workflows)

stateDiagram-v2
    [*] --> CONTINUE
    CONTINUE --> CONTINUE: Success
    CONTINUE --> PENDING: Wait for external event
    CONTINUE --> CONFIRM: User confirmation needed
    CONTINUE --> SCREENSHOT: Capture observation
    CONTINUE --> FINISH: Task complete
    CONTINUE --> FAIL: Error occurred
    CONTINUE --> ERROR: Critical failure
    
    PENDING --> CONTINUE: Event received
    CONFIRM --> CONTINUE: User confirmed
    CONFIRM --> FAIL: User rejected
    SCREENSHOT --> CONTINUE: Screenshot captured
    
    FINISH --> [*]
    FAIL --> [*]
    ERROR --> [*]

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AgentStatus Enum

class AgentStatus(Enum):
    """Agent status enumeration"""
    ERROR = "ERROR"            # Critical error occurred
    FINISH = "FINISH"          # Task completed successfully
    CONTINUE = "CONTINUE"      # Normal execution
    FAIL = "FAIL"              # Task failed
    PENDING = "PENDING"        # Waiting for external event
    CONFIRM = "CONFIRM"        # Awaiting user confirmation
    SCREENSHOT = "SCREENSHOT"  # Screenshot capture needed

State Registration:

New states can be registered dynamically using the @AgentStateManager.register decorator:

@AgentStateManager.register
class CustomState(AgentState):
    async def handle(self, agent, context):
        # Custom state logic
        pass
    
    def next_state(self, agent):
        return AgentStateManager.get_state("CONTINUE")

See State Layer Documentation for complete details.

---

Level-2: Strategy Layer (Execution Logic)

The Strategy Layer implements the execution logic within each state. Each state encapsulates a processor that manages a sequence of strategies to implement step-level workflow. This layer consists of two key components:

Processor: Strategy Orchestrator

The ProcessorTemplate orchestrates the execution of strategies:

• Registers Strategies: Configures which strategies execute in each phase
• Manages Middleware: Wraps strategy execution with logging, metrics, error handling
• Validates Dependencies: Ensures strategies have required data before execution
• Controls Execution: Sequences strategies through fixed workflow phases

graph TB
    State[AgentState] -->|encapsulates| Processor[ProcessorTemplate<br/>Strategy Orchestrator]
    
    Processor -->|1. Register| Strategies[ProcessingStrategies]
    Processor -->|2. Wrap| Middleware[Middleware Chain]
    Processor -->|3. Validate| Dependencies[Strategy Dependencies]
    Processor -->|4. Execute| Workflow[Workflow Phases]
    
    Workflow --> DC[DATA_COLLECTION]
    Workflow --> LLM[LLM_INTERACTION]
    Workflow --> AE[ACTION_EXECUTION]
    Workflow --> MU[MEMORY_UPDATE]
    
    DC & LLM & AE & MU -.->|implements| Strategies

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Processor and Strategy Relationship:

• Processor: Framework that manages the sequence of strategies
• Strategy: Modular, reusable execution units

Together they form Level-2: Strategy Layer, which handles:

• Data collection and environment inspection
• Prompt construction and LLM reasoning
• Action planning and tool invocation
• Memory updates and context synchronization

Strategy: Modular Execution Units

ProcessingStrategies are modular execution units with a unified execute() interface:

graph LR
    A[DATA_COLLECTION] --> B[LLM_INTERACTION]
    B --> C[ACTION_EXECUTION]
    C --> D[MEMORY_UPDATE]
    
    A1[Screenshots<br/>UI Info<br/>System Status] --> A
    B1[Prompt Construction<br/>LLM Call<br/>Response Parsing] --> B
    C1[Command Dispatch<br/>MCP Execution<br/>Result Handling] --> C
    D1[Memory Items<br/>Blackboard Update<br/>Context Sync] --> D

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Four Core Strategy Types

Strategy Type	ProcessingPhase	Purpose	Examples
DATA_COLLECTION	data_collection	Gather contextual information	Screenshot capture, UI tree extraction, system info
LLM_INTERACTION	llm_interaction	Construct prompts, interact with LLM, parse responses	Prompt building, LLM reasoning, JSON parsing
ACTION_EXECUTION	action_execution	Execute commands from LLM/toolkits	Click, type, scroll, API calls
MEMORY_UPDATE	memory_update	Update short-term/long-term memory	Add memory items, update blackboard, sync context

Strategy Layer Configuration Example:

Each state configures its processor with strategies and middleware:

class AppAgentProcessor(ProcessorTemplate):
    def _setup_strategies(self):
        # Register strategies for each phase
        self.strategies[ProcessingPhase.DATA_COLLECTION] = ComposedStrategy([
            AppScreenshotCaptureStrategy(),
            AppControlInfoStrategy()
        ])
        self.strategies[ProcessingPhase.LLM_INTERACTION] = AppLLMInteractionStrategy()
        self.strategies[ProcessingPhase.ACTION_EXECUTION] = AppActionExecutionStrategy()
        self.strategies[ProcessingPhase.MEMORY_UPDATE] = AppMemoryUpdateStrategy()
    
    def _setup_middleware(self):
        # Add middleware for logging, metrics, error handling
        self.middleware_chain = [
            LoggingMiddleware(),
            PerformanceMetricsMiddleware(),
            ErrorHandlingMiddleware()
        ]

See Processor Documentation and Strategy Documentation for complete details.

---

Level-3: Command Layer (System Interface)

The Command Layer provides atomic, deterministic system operations. Each command maps to an MCP tool that executes on the device client.

sequenceDiagram
    participant Agent as Device Agent (Server)
    participant Dispatcher as CommandDispatcher
    participant Protocol as AIP Protocol
    participant Client as Device Client
    participant MCP as MCP Tool

    Agent->>Dispatcher: execute_commands([command1, command2])
    Dispatcher->>Protocol: Send ServerMessage (COMMAND)
    Protocol->>Client: WebSocket (AIP)
    Client->>MCP: Route to MCP server
    MCP->>MCP: Execute tool function
    MCP->>Client: Return result
    Client->>Protocol: Send ClientMessage (RESULT)
    Protocol->>Dispatcher: Receive results
    Dispatcher->>Agent: Return List[Result]

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Command Structure

@dataclass
class Command:
    """Atomic command to be executed on device client"""
    tool_name: str                   # MCP tool name (e.g., "click_element")
    parameters: Dict[str, Any]       # Tool arguments
    tool_type: str                   # "data_collection" or "action"
    call_id: str                     # Unique identifier

!!! warning "Deterministic Execution" Commands are designed to be:

- **Atomic**: Single, indivisible operation
- **Deterministic**: Same inputs → same outputs
- **Auditable**: Full command history logged
- **Reversible**: Where possible, support undo operations

Extensibility:

New commands can be added by:

1. Registering MCP tool on device client
2. LLM dynamically selects tool from available MCP registry
3. No server-side code changes required

See Command Layer Documentation for complete details.

---

Server-Client Architecture

Device agents use a server-client separation to balance safety, scalability, and functionality:

graph TB
    subgraph "Server Side (UFO3 Orchestrator)"
        Server[Device Agent Server]
        State[State Machine]
        Processor[Strategy Processor]
        LLM[LLM Service]
        Memory[Memory & Context]
        
        Server --> State
        Server --> Processor
        Server --> Memory
        Processor -.-> LLM
    end
    
    subgraph "Communication Layer"
        AIP[AIP Protocol<br/>WebSocket]
    end
    
    subgraph "Client Side (Device)"
        Client[Device Client]
        Dispatcher[Command Dispatcher]
        MCP[MCP Server Manager]
        Tools[MCP Tools]
        OS[Operating System]
        
        Client --> Dispatcher
        Dispatcher --> MCP
        MCP --> Tools
        Tools --> OS
    end
    
    Server <-->|Commands/Results| AIP
    AIP <-->|Commands/Results| Client

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Separation of Concerns

Component	Location	Responsibilities	Security Boundary
Agent Server	Orchestrator	State management, reasoning, planning, memory	Untrusted (LLM-driven decisions)
Device Client	Device	Command execution, MCP tool calls, resource access	Trusted (validated operations)
AIP Protocol	Communication	Message serialization, WebSocket transport, error handling	Secure channel (authentication, encryption)

Why Server-Client Separation?

Safety: Isolates potentially unsafe LLM-generated decisions from direct system access. Clients validate all commands before execution.

Scalability: Single orchestrator server manages multiple device clients. Reduces per-device resource requirements.

Flexibility: Device clients can run on resource-constrained devices (embedded systems, mobile) while heavy reasoning occurs on server.

See Server-Client Architecture for complete details.

---

Supported Device Platforms

UFO3 currently supports Windows and Linux device agents, with architecture designed for extensibility to other platforms.

Windows Agents

graph TB
    subgraph "Windows Device (Two-Tier Hierarchy)"
        Host[HostAgent<br/>Application Selection]
        App1[AppAgent<br/>Word]
        App2[AppAgent<br/>Excel]
        App3[AppAgent<br/>Browser]
        
        Host -->|delegates| App1
        Host -->|delegates| App2
        Host -->|delegates| App3
    end
    
    User[User Request] --> Host

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HostAgent (Application-Level Coordinator):

• Selects appropriate application(s) for user request
• Decomposes tasks into application-specific subtasks
• Coordinates multiple AppAgents
• Manages application switching and data transfer

AppAgent (Application-Level Executor):

• Controls specific Windows application (Word, Excel, browser, etc.)
• Uses UI Automation for control element discovery
• Executes application-specific actions (type, click, scroll)
• Maintains application context and memory

!!! example "Windows Agent Example" User Request: "Create a chart from sales.xlsx and insert into report.docx"

1. **HostAgent** decomposes: 
    - Open Excel → Create chart → Copy chart
    - Open Word → Paste chart
2. **AppAgent (Excel)**: Opens `sales.xlsx`, creates chart, copies to clipboard
3. **AppAgent (Word)**: Opens `report.docx`, pastes chart at cursor

Linux Agent

graph TB
    subgraph "Linux Device (Single-Tier Architecture)"
        Linux[LinuxAgent<br/>Direct System Control]
        Shell[Shell Commands]
        Files[File Operations]
        Apps[Application Launch]
        
        Linux --> Shell
        Linux --> Files
        Linux --> Apps
    end
    
    User[User Request] --> Linux

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LinuxAgent (System-Level Executor):

• Direct shell command execution
• File system operations
• Application launch and management
• Single-tier architecture (no application-level hierarchy)

!!! info "Architecture Difference" Windows uses two-tier hierarchy (HostAgent → AppAgent) due to:

- UI Automation framework's application-centric model
- Distinct application contexts requiring specialized agents

**Linux** uses single-tier architecture because:

- Shell provides unified interface to all system operations
- Application control occurs through same command-line interface

Platform Comparison

Feature	Windows (UFO2)	Linux	macOS (Future)	Embedded (Future)
Agent Hierarchy	Two-tier (Host → App)	Single-tier	TBD	Single-tier
UI Control	UI Automation	X11/Wayland	Accessibility API	Platform-specific
Command Interface	MCP tools (Win32 API)	MCP tools (Shell)	MCP tools (AppleScript)	MCP tools (Custom)
Observation	Screenshot + UI tree	Screenshot + Shell output	Screenshot + UI tree	Sensor data
State Management	Shared FSM	Shared FSM	Shared FSM	Shared FSM
Strategy Layer	Processor framework	Processor framework	Processor framework	Processor framework
Current Status	✅ Production	✅ Production	🔜 Planned	🔜 Planned

Extensibility Path:

Adding a new platform requires:

1. Implement Agent Class: Extend BasicAgent (inherit State layer, Processor framework)
2. Create Processor: Subclass ProcessorTemplate, implement platform-specific strategies
3. Define MCP Tools: Register platform-specific MCP tools on device client
4. Register Agent: Use @AgentRegistry.register decorator

No changes to core State layer, Processor framework, or AIP protocol required.

See Agent Types Documentation for complete implementation details.

---

Agent Lifecycle

A typical device agent execution follows this lifecycle:

sequenceDiagram
    participant User
    participant Orchestrator
    participant Agent
    participant State
    participant Processor
    participant LLM
    participant Dispatcher
    participant Client

    User->>Orchestrator: Submit task
    Orchestrator->>Agent: Initialize agent (CONTINUE state)
    
    loop Until FINISH/FAIL/ERROR
        Agent->>State: handle(agent, context)
        State->>Processor: execute strategies
        
        Processor->>Processor: DATA_COLLECTION
        Note over Processor: Screenshot, UI info
        
        Processor->>LLM: LLM_INTERACTION
        LLM-->>Processor: Action decision
        
        Processor->>Dispatcher: ACTION_EXECUTION
        Dispatcher->>Client: Execute commands
        Client-->>Dispatcher: Results
        Dispatcher-->>Processor: Results
        
        Processor->>Processor: MEMORY_UPDATE
        Note over Processor: Update memory, blackboard
        
        State->>State: next_state(agent)
        State->>Agent: Update agent status
    end
    
    Agent->>Orchestrator: Task complete/failed
    Orchestrator->>User: Return result

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Execution Phases

1. Initialization: Agent created with default state (CONTINUE), processor, memory
2. State Handling: Current state's handle() method invoked with agent and context
3. Strategy Execution: Processor runs strategies in sequence (DATA_COLLECTION → LLM_INTERACTION → ACTION_EXECUTION → MEMORY_UPDATE)
4. State Transition: State's next_state() determines next FSM state
5. Repeat/Terminate: Loop continues until terminal state (FINISH, FAIL, ERROR)

!!! tip "Multi-Agent Handoff" For multi-agent scenarios (e.g., Windows HostAgent → AppAgent), states implement next_agent():

```python
def next_agent(self, agent: BasicAgent) -> BasicAgent:
    # HostAgent delegates to AppAgent
    if agent.status == "DELEGATE_TO_APP":
        return agent.create_app_agent(...)
    return agent
```

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Memory and Context Management

Device agents maintain two types of memory:

Short-Term Memory (Agent Memory)

Purpose: Track agent's execution history within a session

Implementation: Memory class with MemoryItem entries

class Memory:
    """Agent's short-term memory"""
    _content: List[MemoryItem]
    
    def add_memory_item(self, memory_item: MemoryItem):
        """Add new memory entry"""
        self._content.append(memory_item)

Content: Actions taken, observations made, results received

Lifetime: Single session (cleared between tasks)

Long-Term Memory (Blackboard)

Purpose: Share information across agents and sessions

Implementation: Blackboard class with multiple memory types

class Blackboard:
    """Multi-agent shared memory"""
    _questions: Memory      # Q&A history
    _requests: Memory       # Request history
    _trajectories: Memory   # Action trajectories
    _screenshots: Memory    # Visual observations

Content: Common knowledge, successful action patterns, user preferences

Lifetime: Persistent across sessions (can be saved/loaded)

Blackboard Usage Example:

Scenario: HostAgent delegates to AppAgent (Excel)

1. HostAgent adds to blackboard:
   • Request: "Create sales chart"
   • Context: Previous analysis results
2. AppAgent reads from blackboard:
   • Retrieves request and context
   • Adds action trajectories as executed
   • Adds screenshot after chart creation
3. HostAgent reads updated blackboard:
   • Verifies chart creation
   • Continues to next step (insert to Word)

See Memory System Documentation for complete details.

---

Integration with UFO3 Components

Device agents integrate with other UFO3 components:

graph TB
    subgraph "UFO3 Architecture"
        Session[Session/Round Manager]
        Context[Global Context]
        Agent[Device Agent]
        Dispatcher[Command Dispatcher]
        AIP[AIP Protocol]
        Client[Device Client]
        MCP[MCP Servers]
    end
    
    Session -->|manages lifecycle| Agent
    Session -->|provides| Context
    Agent -->|reads/writes| Context
    Agent -->|sends commands| Dispatcher
    Dispatcher -->|uses| AIP
    AIP <-->|WebSocket| Client
    Client -->|calls| MCP

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Integration Points

Component	Relationship	Description
Session Manager	Parent	Creates agents, manages agent lifecycle, coordinates multi-agent workflows
Round Manager	Sibling	Manages round-based execution, tracks round state, synchronizes with agent steps
Global Context	Shared State	Agent reads request/config, writes results/status, shares data across components
Command Dispatcher	Execution Interface	Agent sends commands, dispatcher routes to client, returns results
AIP Protocol	Communication	Serializes commands/results, manages WebSocket, handles errors/timeouts
Device Client	Executor	Receives commands, invokes MCP tools, returns results
MCP Servers	Tool Registry	Provides available tools, executes tool functions, returns structured results

See Session Documentation, Context Documentation, and AIP Protocol for integration details.

---

Design Patterns

Device agent architecture leverages several design patterns:

1. State Pattern (FSM Layer)

Purpose: Encapsulate state-specific behavior, enable dynamic state transitions

Implementation: AgentState abstract class, concrete state classes

class AgentState(ABC):
    @abstractmethod
    async def handle(self, agent, context):
        """Execute state-specific logic"""
        pass
    
    @abstractmethod
    def next_state(self, agent):
        """Determine next state"""
        pass

2. Strategy Pattern (Strategy Layer)

Purpose: Define family of algorithms (strategies), make them interchangeable

Implementation: ProcessingStrategy protocol, concrete strategy classes

class ProcessingStrategy(Protocol):
    async def execute(self, agent, context) -> ProcessingResult:
        """Execute strategy logic"""
        pass

3. Template Method Pattern (Processor Framework)

Purpose: Define skeleton of algorithm, let subclasses override specific steps

Implementation: ProcessorTemplate abstract class

class ProcessorTemplate(ABC):
    @abstractmethod
    def _setup_strategies(self):
        """Subclass defines which strategies to use"""
        pass
    
    async def process(self, agent, context):
        """Template method - runs strategies in sequence"""
        for phase, strategy in self.strategies.items():
            result = await strategy.execute(agent, context)
            # Handle result, update context

4. Singleton Pattern (State Manager)

Purpose: Ensure single instance of state registry

Implementation: AgentStateManager with metaclass

class AgentStateManager(ABC, metaclass=SingletonABCMeta):
    _state_mapping: Dict[str, Type[AgentState]] = {}
    
    def get_state(self, status: str) -> AgentState:
        """Lazy load and return state instance"""
        pass

5. Registry Pattern (Agent Registration)

Purpose: Register agent types, enable dynamic agent creation

Implementation: AgentRegistry decorator

@AgentRegistry.register(agent_name="appagent", processor_cls=AppAgentProcessor)
class AppAgent(BasicAgent):
    pass

6. Blackboard Pattern (Multi-Agent Coordination)

Purpose: Share data across multiple agents

Implementation: Blackboard class

class Blackboard:
    _questions: Memory
    _requests: Memory
    _trajectories: Memory
    _screenshots: Memory

---

Best Practices

State Design

• Keep states focused: Each state should have single, clear responsibility
• Use rule-based transitions for deterministic flows, LLM-driven transitions for adaptive behavior
• Implement error states for graceful degradation
• Document state invariants and transition conditions

Strategy Design

• Keep strategies atomic: Each strategy should perform one cohesive task
• Declare dependencies explicitly using get_dependencies()
• Use ComposedStrategy to combine multiple strategies within a phase
• Implement fail-fast for critical errors, continue-on-error for optional operations

Command Design

• Keep commands atomic: Single, indivisible operation
• Design commands to be idempotent where possible
• Validate arguments on client side before execution
• Return structured results with success/failure status

Memory Management

• Use short-term memory for agent-specific execution history
• Use blackboard for multi-agent coordination and persistent knowledge
• Clear memory between sessions to avoid context pollution
• Implement memory pruning for long-running sessions

!!! warning "Security Considerations" - Validate all commands on client side before execution - Sanitize LLM outputs before converting to commands - Limit command scope via MCP tool permissions - Audit all actions for compliance and debugging - Isolate agents to prevent unauthorized cross-agent access

---

Related Documentation

Deep Dive Into Layers:

• State Layer Documentation: FSM, AgentState, transitions, state registration
• Processor and Strategy Documentation: ProcessorTemplate, strategies, dependency management
• Command Layer Documentation: CommandDispatcher, MCP integration, atomic commands

Supporting Systems:

• Memory System Documentation: Memory, MemoryItem, Blackboard patterns
• Agent Types Documentation: Windows agents, Linux agent, platform-specific implementations

Integration Points:

• Server-Client Architecture: Server and client separation, communication patterns
• Server Architecture: Agent server, WebSocket manager, orchestration
• Client Architecture: Device client, MCP servers, command execution
• AIP Protocol: Agent Interaction Protocol for server-client communication
• MCP Integration: Model Context Protocol for tool execution

---

Summary

Key Takeaways:

✅ Three-Layer Architecture: State (FSM) → Strategy (Execution Logic) → Command (System Interface)

✅ Server-Client Separation: Safe isolation of reasoning (server) from execution (client)

✅ Multi-Platform Support: Windows (two-tier), Linux (single-tier), extensible to macOS and embedded

✅ LLM-Driven Reasoning: Dynamic decision-making with structured command output

✅ Modular & Extensible: Register new states, compose strategies, add MCP tools without core changes

✅ Memory Systems: Short-term (agent memory) and long-term (blackboard) for coordination

✅ Design Patterns: State, Strategy, Template Method, Singleton, Registry, Blackboard

The Device Agent architecture provides a robust, extensible foundation for multi-device automation. By separating concerns across three layers and isolating reasoning from execution, UFO3 achieves both safety and flexibility for orchestrating complex cross-device workflows.

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Reference

Below is the reference for the BasicAgent class. All device agents inherit from BasicAgent and implement platform-specific processors and states:

::: agents.agent.basic.BasicAgent