🔁 Execution Logic

Deterministic Agent Coordination

AppDuck's execution model ensures reliable, predictable outputs through structured agent coordination and robust error handling.

Key Principles

Isolation with Shared Memory

• Each agent runs in its own execution context

• Shared memory system maintains state across agents

• Clean interfaces between agent boundaries

• No direct agent-to-agent communication

Task Ordering & Dependencies

• Sequential execution with clear handoff points

• Dependency validation before agent activation

• Parallel processing where tasks are independent

• Smart queuing for optimal resource utilization

Persistent Context

• Tasks are not stateless between executions

• Prompt history is scoped and ordered per agent

• Chains can persist across sessions

• Single-step execution supported for quick iterations

Failure Handling

• Built-in retry logic with exponential backoff

• Constraint adjustment for failed outputs

• Structured error reporting with recovery suggestions

• Graceful degradation when agents cannot complete tasks

Execution Flow

Input: Startup Idea
    ↓
[Clara] → Validation & Reality Check
    ↓
[Vince] → Name Generation & Brand Strategy  
    ↓
[Mira] → Business Model & Revenue Design
    ↓
[Dex] → Product Definition & Feature Scope
    ↓
[Lana] → Interface Design & User Experience
    ↓
[Toru] → Package Creation & Export
    ↓
Output: Complete Startup Package

Error Recovery Examples

Agent Timeout:

Agent: Clara
Status: Timeout after 30 seconds
Action: Retry with reduced scope constraints
Fallback: Skip validation, proceed with warning flag

Invalid Output Format:

Agent: Mira
Error: Business model missing revenue projections
Action: Re-prompt with specific requirements
Constraint: Must include 3-year financial forecast

System Resource Limits:

Issue: Design generation exceeding memory limits
Action: Switch to lightweight mockup generation
Quality: Reduce resolution, maintain functionality