goal-seeking-agent-pattern

# Goal-Seeking Agent Pattern Skill

## 1. What Are Goal-Seeking Agents?

Goal-seeking agents are autonomous AI agents that execute multi-phase objectives by:

1. **Understanding High-Level Goals**: Accept natural language objectives without explicit step-by-step instructions
2. **Planning Execution**: Break goals into phases with dependencies and success criteria
3. **Autonomous Execution**: Make decisions and adapt behavior based on intermediate results
4. **Self-Assessment**: Evaluate progress against success criteria and adjust approach
5. **Resilient Operation**: Handle failures gracefully and explore alternative solutions

### Core Characteristics

**Autonomy**: Agents decide HOW to achieve goals, not just follow prescriptive steps

**Adaptability**: Adjust strategy based on runtime conditions and intermediate results

**Goal-Oriented**: Focus on outcomes (what to achieve) rather than procedures (how to achieve)

**Multi-Phase**: Complex objectives decomposed into manageable phases with dependencies

**Self-Monitoring**: Track progress, detect failures, and course-correct autonomously

### Distinction from Traditional Agents

| Traditional Agent             | Goal-Seeking Agent            |
| ----------------------------- | ----------------------------- |
| Follows fixed workflow        | Adapts workflow to context    |
| Prescriptive steps            | Outcome-oriented objectives   |
| Human intervention on failure | Autonomous recovery attempts  |
| Single-phase execution        | Multi-phase with dependencies |
| Rigid decision tree           | Dynamic strategy adjustment   |

### When Goal-Seeking Makes Sense

Goal-seeking agents excel when:

- **Problem space is large**: Many possible paths to success
- **Context varies**: Runtime conditions affect optimal approach
- **Failures are expected**: Need autonomous recovery without human intervention
- **Objectives are clear**: Success criteria well-defined but path is flexible
- **Multi-step complexity**: Requi